pystencils/boundaries/inkernel.py → src/pystencils/boundaries/inkernel.py

 import sympy as sp
-from pystencils import Field, TypedSymbol
-from pystencils.integer_functions import bitwise_and
+
 from pystencils.boundaries.boundaryhandling import DEFAULT_FLAG_TYPE
-from pystencils.data_types import create_type
+from pystencils.typing import TypedSymbol, create_type
+from pystencils.field import Field
+from pystencils.integer_functions import bitwise_and
 
 
 def add_neumann_boundary(eqs, fields, flag_field, boundary_flag="neumann_flag", inverse_flag=False):
     """
     Replaces all neighbor accesses by flag field guarded accesses.
     If flag in neighboring cell is set, the center value is used instead
-    :param eqs: list of equations containing field accesses to direct neighbors
-    :param fields: fields for which the Neumann boundary should be applied
-    :param flag_field: integer field marking boundary cells
-    :param boundary_flag: if flag field has value 'boundary_flag' (no bit operations yet)
-                          the cell is assumed to be boundary
-    :param inverse_flag: if true, boundary cells are where flag field has not the value of boundary_flag
-    :return: list of equations with guarded field accesses
+
+    Args:
+        eqs: list of equations containing field accesses to direct neighbors
+        fields: fields for which the Neumann boundary should be applied
+        flag_field: integer field marking boundary cells
+        boundary_flag: if flag field has value 'boundary_flag' (no bit operations yet)
+                       the cell is assumed to be boundary
+        inverse_flag: if true, boundary cells are where flag field has not the value of boundary_flag
+
+    Returns:
+        list of equations with guarded field accesses
     """
     if not hasattr(fields, "__len__"):
         fields = [fields]

src/pystencils/cache.py

+import os
+from collections.abc import Hashable
+from functools import partial, wraps
+from itertools import chain
+
+from functools import lru_cache as memorycache
+
+from joblib import Memory
+from appdirs import user_cache_dir
+
+if 'PYSTENCILS_CACHE_DIR' in os.environ:
+    cache_dir = os.environ['PYSTENCILS_CACHE_DIR']
+else:
+    cache_dir = user_cache_dir('pystencils')
+disk_cache = Memory(cache_dir, verbose=False).cache
+disk_cache_no_fallback = disk_cache
+
+
+def _wrapper(wrapped_func, cached_func, *args, **kwargs):
+    if all(isinstance(a, Hashable) for a in chain(args, kwargs.values())):
+        return cached_func(*args, **kwargs)
+    else:
+        return wrapped_func(*args, **kwargs)
+
+
+def memorycache_if_hashable(maxsize=128, typed=False):
+    def wrapper(func):
+        return partial(_wrapper, func, memorycache(maxsize, typed)(func))
+
+    return wrapper
+
+
+def sharedmethodcache(cache_id: str):
+    """Decorator for memoization of instance methods, allowing multiple methods to use the same cache.
+
+    This decorator caches results of instance methods per instantiated object of the surrounding class.
+    It allows multiple methods to use the same cache, by passing them the same `cache_id` string.
+    Cached values are stored in a dictionary, which is added as a member `self.<cache_id>` to the 
+    `self` object instance. Make sure that this doesn't cause any naming conflicts with other members!
+    Of course, for this to be useful, said methods must have the same signature (up to additional kwargs)
+    and must return the same result when called with the same arguments."""
+    def _decorator(user_method):
+        @wraps(user_method)
+        def _decorated_func(self, *args, **kwargs):
+            objdict = self.__dict__
+            cache = objdict.setdefault(cache_id, dict())
+
+            key = args
+            for item in kwargs.items():
+                key += item
+
+            if key not in cache:
+                result = user_method(self, *args, **kwargs)
+                cache[key] = result
+                return result
+            else:
+                return cache[key]
+        return _decorated_func
+    return _decorator
+
+
+def clear_cache():
+    """
+    Clears the pystencils cache created by joblib.
+    """
+    memory = Memory(cache_dir, verbose=0)
+    memory.clear(warn=False)
+
+
+# Disable memory cache:
+# disk_cache = lambda o: o
+# disk_cache_no_fallback = lambda o: o

src/pystencils/config.py

+from copy import copy
+from collections import defaultdict
+from dataclasses import dataclass, field
+from types import MappingProxyType
+from typing import Union, Tuple, List, Dict, Callable, Any, DefaultDict, Iterable
+
+from pystencils import Target, Backend, Field
+from pystencils.typing.typed_sympy import BasicType
+from pystencils.typing.utilities import collate_types
+
+import numpy as np
+
+# TODO: There exists DTypeLike in NumPy which would be better than type for type hinting, to new at the moment
+# from numpy.typing import DTypeLike
+
+
+# TODO: CreateKernelConfig is bloated think of more classes better usage, factory whatever ...
+# Proposition: CreateKernelConfigs Classes for different targets?
+@dataclass
+class CreateKernelConfig:
+    """
+    **Below all parameters for the CreateKernelConfig are explained**
+    """
+    target: Target = Target.CPU
+    """
+    All targets are defined in :class:`pystencils.enums.Target`
+    """
+    backend: Backend = None
+    """
+    All backends are defined in :class:`pystencils.enums.Backend`
+    """
+    function_name: str = 'kernel'
+    """
+    Name of the generated function - only important if generated code is written out
+    """
+    data_type: Union[type, str, DefaultDict[str, BasicType], Dict[str, BasicType]] = np.float64
+    """
+    Data type used for all untyped symbols (i.e. non-fields), can also be a dict from symbol name to type.
+    If specified as a dict ideally a defaultdict is used to define a default value for symbols not listed in the
+    dict. If a plain dict is provided it will be transformed into a defaultdict internally. The default value 
+    will then be specified via type collation then.
+    """
+    default_number_float: Union[type, str, BasicType] = None
+    """
+    Data type used for all untyped floating point numbers (i.e. 0.5). By default the value of data_type is used.
+    If data_type is given as a defaultdict its default_factory is used.
+    """
+    default_number_int: Union[type, str, BasicType] = np.int64
+    """
+    Data type used for all untyped integer numbers (i.e. 1)
+    """
+    iteration_slice: Tuple = None
+    """
+    Rectangular subset to iterate over, if not specified the complete non-ghost layer part of the field is iterated over
+    """
+    ghost_layers: Union[bool, int, List[Tuple[int]]] = None
+    """
+    A single integer specifies the ghost layer count at all borders, can also be a sequence of
+    pairs ``[(x_lower_gl, x_upper_gl), .... ]``. These layers are excluded from the iteration.
+    If left to default, the number of ghost layers is determined automatically from the assignments.
+    """
+    cpu_openmp: Union[bool, int] = False
+    """
+    `True` or number of threads for OpenMP parallelization, `False` for no OpenMP. If set to `True`, the maximum number
+    of available threads will be chosen.
+    """
+    cpu_vectorize_info: Dict = None
+    """
+    A dictionary with keys, 'vector_instruction_set', 'assume_aligned' and 'nontemporal'
+    for documentation of these parameters see vectorize function. Example:
+    '{'instruction_set': 'avx512', 'assume_aligned': True, 'nontemporal':True}'
+    """
+    cpu_blocking: Tuple[int] = None
+    """
+    A tuple of block sizes or `None` if no blocking should be applied
+    """
+    omp_single_loop: bool = True
+    """
+    If OpenMP is active: whether multiple outer loops are permitted
+    """
+    base_pointer_specification: Union[List[Iterable[str]], List[Iterable[int]]] = None
+    """
+    Specification of how many and which intermediate pointers are created for a field access.
+    For example [ (0), (2,3,)]  creates on base pointer for coordinates 2 and 3 and writes the offset for coordinate
+    zero directly in the field access. These specifications are defined dependent on the loop ordering.
+    This function translates more readable version into the specification above.
+    
+    For more information see: `pystencils.transformations.create_intermediate_base_pointer`
+    """
+    gpu_indexing: str = 'block'
+    """
+    Either 'block' or 'line' , or custom indexing class, see `pystencils.gpu.AbstractIndexing`
+    """
+    gpu_indexing_params: MappingProxyType = field(default_factory=lambda: MappingProxyType({}))
+    """
+    Dict with indexing parameters (constructor parameters of indexing class)
+    e.g. for 'block' one can specify '{'block_size': (20, 20, 10) }'.
+    """
+    # TODO Markus rework this docstring
+    default_assignment_simplifications: bool = False
+    """
+    If `True` default simplifications are first performed on the Assignments. If problems occur during the
+    simplification a warning will be thrown.
+    Furthermore, it is essential to know that this is a two-stage process. The first stage of the process acts
+    on the level of the `pystencils.AssignmentCollection`.  In this part,
+    `pystencil.simp.create_simplification_strategy` from pystencils.simplificationfactory will be used to
+    apply optimisations like insertion of constants to
+    remove pressure from the registers. Thus the first part of the optimisations can only be executed if
+    an `AssignmentCollection` is passed. The second part of the optimisation acts on the level of each Assignment
+    individually. In this stage, all optimisations from `sympy.codegen.rewriting.optims_c99` are applied
+    to each Assignment. Thus this stage can also be applied if a list of Assignments is passed.
+    """
+    cpu_prepend_optimizations: List[Callable] = field(default_factory=list)
+    """
+    List of extra optimizations to perform first on the AST.
+    """
+    use_auto_for_assignments: bool = False
+    """
+    If set to `True`, auto can be used in the generated code for data types. This makes the type system more robust.
+    """
+    index_fields: List[Field] = None
+    """
+    List of index fields, i.e. 1D fields with struct data type. If not `None`, `create_index_kernel`
+    instead of `create_domain_kernel` is used.
+    """
+    coordinate_names: Tuple[str, Any] = ('x', 'y', 'z')
+    """
+    Name of the coordinate fields in the struct data type.
+    """
+    allow_double_writes: bool = False
+    """
+    If True, don't check if every field is only written at a single location. This is required
+    for example for kernels that are compiled with loop step sizes > 1, that handle multiple
+    cells at once. Use with care!
+    """
+    skip_independence_check: bool = False
+    """
+    By default the assignment list is checked for read/write independence. This means fields are only written at
+    locations where they are read. Doing so guarantees thread safety. In some cases e.g. for
+    periodicity kernel, this can not be assured and does the check needs to be deactivated. Use with care!
+    """
+
+    class DataTypeFactory:
+        """Because of pickle, we need to have a nested class, instead of a lambda in __post_init__"""
+        def __init__(self, dt):
+            self.dt = dt
+
+        def __call__(self):
+            return BasicType(self.dt)
+
+    def _check_type(self, dtype_to_check):
+        if isinstance(dtype_to_check, str) and (dtype_to_check == 'float' or dtype_to_check == 'int'):
+            self._typing_error()
+
+        if isinstance(dtype_to_check, type) and not hasattr(dtype_to_check, "dtype"):
+            # NumPy-types are also of type 'type'. However, they have more properties
+            self._typing_error()
+
+    @staticmethod
+    def _typing_error():
+        raise ValueError("It is not possible to use python types (float, int) for datatypes because these "
+                         "types are ambiguous. For example float will map to double. "
+                         "Also the string version like 'float' is not allowed, e.g. use 'float64' instead")
+
+    def __post_init__(self):
+        # ----  Legacy parameters
+        if not isinstance(self.target, Target):
+            raise ValueError("target must be provided by the 'Target' enum")
+
+        # ---- Auto Backend
+        if not self.backend:
+            if self.target == Target.CPU:
+                self.backend = Backend.C
+            elif self.target == Target.GPU:
+                self.backend = Backend.CUDA
+            else:
+                raise NotImplementedError(f'Target {self.target} has no default backend')
+
+        if not isinstance(self.backend, Backend):
+            raise ValueError("backend must be provided by the 'Backend' enum")
+
+        # Normalise data types
+        for dtype in [self.data_type, self.default_number_float, self.default_number_int]:
+            self._check_type(dtype)
+
+        if not isinstance(self.data_type, dict):
+            dt = copy(self.data_type)  # The copy is necessary because BasicType has sympy shinanigans
+            self.data_type = defaultdict(self.DataTypeFactory(dt))
+
+        if isinstance(self.data_type, dict) and not isinstance(self.data_type, defaultdict):
+            for dtype in self.data_type.values():
+                self._check_type(dtype)
+
+            dt = collate_types([BasicType(dtype) for dtype in self.data_type.values()])
+            dtype_dict = self.data_type
+            self.data_type = defaultdict(self.DataTypeFactory(dt), dtype_dict)
+
+        assert isinstance(self.data_type, defaultdict), "At this point data_type must be a defaultdict!"
+        for dtype in self.data_type.values():
+            self._check_type(dtype)
+        self._check_type(self.data_type.default_factory())
+
+        if self.default_number_float is None:
+            self.default_number_float = self.data_type.default_factory()
+
+        if not isinstance(self.default_number_float, BasicType):
+            self.default_number_float = BasicType(self.default_number_float)
+        if not isinstance(self.default_number_int, BasicType):
+            self.default_number_int = BasicType(self.default_number_int)

pystencils/cpu/__init__.py → src/pystencils/cpu/__init__.py

-from pystencils.cpu.kernelcreation import create_kernel, create_indexed_kernel, add_openmp
 from pystencils.cpu.cpujit import make_python_function
+from pystencils.cpu.kernelcreation import add_openmp, create_indexed_kernel, create_kernel, add_pragmas
 
-__all__ = ['create_kernel', 'create_indexed_kernel', 'add_openmp', 'make_python_function']
+__all__ = ['create_kernel', 'create_indexed_kernel', 'add_openmp', 'add_pragmas', 'make_python_function']

pystencils/cpu/cpujit.py → src/pystencils/cpu/cpujit.py

@@ -13,7 +13,7 @@ in a configuration file.
 3. or in your home directory at ``~/.config/pystencils/config.json`` (Linux) or
    ``%HOMEPATH%\.pystencils\config.json`` (Windows)
 
-If no configuration file is found, a default configuration is created at the above mentioned location in your home.
+If no configuration file is found, a default configuration is created at the above-mentioned location in your home.
 So run *pystencils* once, then edit the created configuration file.
 
 
@@ -23,7 +23,7 @@ Compiler Config (Linux)
 - **'os'**: should be detected automatically as 'linux'
 - **'command'**: path to C++ compiler (defaults to 'g++')
 - **'flags'**: space separated list of compiler flags. Make sure to activate OpenMP in your compiler
-- **'restrict_qualifier'**: the restrict qualifier is not standardized accross compilers.
+- **'restrict_qualifier'**: the 'restrict' qualifier is not standardized across compilers.
   For most Linux compilers the qualifier is ``__restrict__``
 
 
@@ -39,32 +39,40 @@ Then 'cl.exe' is used to compile.
   where Visual Studio is installed. This path has to contain a file called 'vcvarsall.bat'
 - **'arch'**: 'x86' or 'x64'
 - **'flags'**: flags passed to 'cl.exe', make sure OpenMP is activated
-- **'restrict_qualifier'**: the restrict qualifier is not standardized across compilers.
+- **'restrict_qualifier'**: the 'restrict' qualifier is not standardized across compilers.
   For Windows compilers the qualifier should be ``__restrict``
 
 """
-import os
+from appdirs import user_cache_dir, user_config_dir
+from collections import OrderedDict
 import hashlib
+import importlib.util
 import json
+import os
 import platform
 import shutil
+import subprocess
+import sysconfig
+import tempfile
 import textwrap
-from tempfile import TemporaryDirectory
+import time
+import warnings
+import pathlib
 
 import numpy as np
-import subprocess
-from appdirs import user_config_dir, user_cache_dir
-from collections import OrderedDict
 
-from pystencils.utils import recursive_dict_update
-from sysconfig import get_paths
 from pystencils import FieldType
+from pystencils.astnodes import LoopOverCoordinate
 from pystencils.backends.cbackend import generate_c, get_headers
-from pystencils.utils import file_handle_for_atomic_write, atomic_file_write
+from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets
+from pystencils.cpu.msvc_detection import get_environment
 from pystencils.include import get_pystencils_include_path
+from pystencils.kernel_wrapper import KernelWrapper
+from pystencils.typing import BasicType, CastFunc, VectorType, VectorMemoryAccess
+from pystencils.utils import atomic_file_write, recursive_dict_update
 
 
-def make_python_function(kernel_function_node):
+def make_python_function(kernel_function_node, custom_backend=None):
     """
     Creates C code from the abstract syntax tree, compiles it and makes it accessible as Python function
 
@@ -73,9 +81,10 @@ def make_python_function(kernel_function_node):
         - all symbols which are not defined in the kernel itself are expected as parameters
 
     :param kernel_function_node: the abstract syntax tree
+    :param custom_backend: use own custom printer for code generation
     :return: kernel functor
     """
-    result = compile_and_load(kernel_function_node)
+    result = compile_and_load(kernel_function_node, custom_backend)
     return result
 
 
@@ -115,15 +124,15 @@ def get_configuration_file_path():
 
     # 1) Read path from environment variable if found
     if 'PYSTENCILS_CONFIG' in os.environ:
-        return os.environ['PYSTENCILS_CONFIG'], True
+        return os.environ['PYSTENCILS_CONFIG']
     # 2) Look in current directory for pystencils.json
     elif os.path.exists("pystencils.json"):
-        return "pystencils.json", True
+        return "pystencils.json"
     # 3) Try ~/.pystencils.json
     elif os.path.exists(config_path_in_home):
-        return config_path_in_home, True
+        return config_path_in_home
     else:
-        return config_path_in_home, False
+        return config_path_in_home
 
 
 def create_folder(path, is_file):
@@ -143,21 +152,42 @@ def read_config():
             ('flags', '-Ofast -DNDEBUG -fPIC -march=native -fopenmp -std=c++11'),
             ('restrict_qualifier', '__restrict__')
         ])
+        if platform.machine().startswith('ppc64') or platform.machine() == 'arm64':
+            default_compiler_config['flags'] = default_compiler_config['flags'].replace('-march=native',
+                                                                                        '-mcpu=native')
     elif platform.system().lower() == 'windows':
         default_compiler_config = OrderedDict([
             ('os', 'windows'),
             ('msvc_version', 'latest'),
             ('arch', 'x64'),
-            ('flags', '/Ox /fp:fast /openmp /arch:avx'),
+            ('flags', '/Ox /fp:fast /OpenMP /arch:avx'),
             ('restrict_qualifier', '__restrict')
         ])
+        if platform.machine() == 'ARM64':
+            default_compiler_config['arch'] = 'ARM64'
+            default_compiler_config['flags'] = default_compiler_config['flags'].replace(' /arch:avx', '')
     elif platform.system().lower() == 'darwin':
         default_compiler_config = OrderedDict([
             ('os', 'darwin'),
             ('command', 'clang++'),
-            ('flags', '-Ofast -DNDEBUG -fPIC -march=native -fopenmp -std=c++11'),
+            ('flags', '-Ofast -DNDEBUG -fPIC -march=native -Xclang -fopenmp -std=c++11'),
             ('restrict_qualifier', '__restrict__')
         ])
+        if platform.machine() == 'arm64':
+            if 'sme' in get_supported_instruction_sets():
+                flag = '-march=armv8.7-a+sme '
+            else:
+                flag = ''
+            default_compiler_config['flags'] = default_compiler_config['flags'].replace('-march=native ', flag)
+        for libomp in ['/opt/local/lib/libomp/libomp.dylib', '/usr/local/lib/libomp.dylib',
+                       '/opt/homebrew/lib/libomp.dylib']:
+            if os.path.exists(libomp):
+                default_compiler_config['flags'] += ' ' + libomp
+                break
+    else:
+        raise NotImplementedError('Generation of default compiler flags for %s is not implemented' %
+                                  (platform.system(),))
+
     default_cache_config = OrderedDict([
         ('object_cache', os.path.join(user_cache_dir('pystencils'), 'objectcache')),
         ('clear_cache_on_start', False),
@@ -166,26 +196,47 @@ def read_config():
     default_config = OrderedDict([('compiler', default_compiler_config),
                                   ('cache', default_cache_config)])
 
-    config_path, config_exists = get_configuration_file_path()
+    from fasteners import InterProcessLock
+
+    config_path = pathlib.Path(get_configuration_file_path())
+    config_path.parent.mkdir(parents=True, exist_ok=True)
+    
     config = default_config.copy()
-    if config_exists:
-        with open(config_path, 'r') as json_config_file:
-            loaded_config = json.load(json_config_file)
-        config = recursive_dict_update(config, loaded_config)
-    else:
-        create_folder(config_path, True)
-        json.dump(config, open(config_path, 'w'), indent=4)
+
+    lockfile = config_path.with_suffix(config_path.suffix + ".lock")
+    with InterProcessLock(lockfile):
+        if config_path.exists():
+            with open(config_path, 'r') as json_config_file:
+                loaded_config = json.load(json_config_file)
+            config = recursive_dict_update(config, loaded_config)
+        else:
+            with open(config_path, 'w') as f:
+                json.dump(config, f, indent=4)
 
     if config['cache']['object_cache'] is not False:
         config['cache']['object_cache'] = os.path.expanduser(config['cache']['object_cache']).format(pid=os.getpid())
 
-        if config['cache']['clear_cache_on_start']:
-            clear_cache()
+        clear_cache_on_start = False
+        cache_status_file = os.path.join(config['cache']['object_cache'], 'last_config.json')
+        if os.path.exists(cache_status_file):
+            # check if compiler config has changed
+            last_config = json.load(open(cache_status_file, 'r'))
+            if set(last_config.items()) != set(config['compiler'].items()):
+                clear_cache_on_start = True
+            else:
+                for key in last_config.keys():
+                    if last_config[key] != config['compiler'][key]:
+                        clear_cache_on_start = True
+
+        if config['cache']['clear_cache_on_start'] or clear_cache_on_start:
+            shutil.rmtree(config['cache']['object_cache'], ignore_errors=True)
 
         create_folder(config['cache']['object_cache'], False)
+        with tempfile.NamedTemporaryFile('w', dir=os.path.dirname(cache_status_file), delete=False) as f:
+            json.dump(config['compiler'], f, indent=4)
+        os.replace(f.name, cache_status_file)
 
     if config['compiler']['os'] == 'windows':
-        from pystencils.cpu.msvc_detection import get_environment
         msvc_env = get_environment(config['compiler']['msvc_version'], config['compiler']['arch'])
         if 'env' not in config['compiler']:
             config['compiler']['env'] = {}
@@ -232,6 +283,7 @@ def clear_cache():
         create_folder(cache_config['object_cache'], False)
 
 
+# TODO don't hardcode C type. [1] of tuple output
 type_mapping = {
     np.float32: ('PyFloat_AsDouble', 'float'),
     np.float64: ('PyFloat_AsDouble', 'double'),
@@ -243,7 +295,6 @@ type_mapping = {
     np.uint64: ('PyLong_AsUnsignedLong', 'uint64_t'),
 }
 
-
 template_extract_scalar = """
 PyObject * obj_{name} = PyDict_GetItemString(kwargs, "{name}");
 if( obj_{name} == NULL) {{  PyErr_SetString(PyExc_TypeError, "Keyword argument '{name}' missing"); return NULL; }};
@@ -318,15 +369,14 @@ def equal_size_check(fields):
         return ""
 
     ref_field = fields[0]
-    cond = ["(buffer_{field.name}.shape[{i}] == buffer_{ref_field.name}.shape[{i}])".format(ref_field=ref_field,
-                                                                                            field=field_to_test, i=i)
+    cond = [f"(buffer_{field_to_test.name}.shape[{i}] == buffer_{ref_field.name}.shape[{i}])"
             for field_to_test in fields[1:]
             for i in range(fields[0].spatial_dimensions)]
     cond = " && ".join(cond)
     return template_size_check.format(cond=cond)
 
 
-def create_function_boilerplate_code(parameter_info, name, insert_checks=True):
+def create_function_boilerplate_code(parameter_info, name, ast_node, insert_checks=True):
     pre_call_code = ""
     parameters = []
     post_call_code = ""
@@ -338,24 +388,55 @@ def create_function_boilerplate_code(parameter_info, name, insert_checks=True):
             field = param.fields[0]
             pre_call_code += template_extract_array.format(name=field.name)
             post_call_code += template_release_buffer.format(name=field.name)
-            parameters.append("({dtype} *)buffer_{name}.buf".format(dtype=str(field.dtype), name=field.name))
+            parameters.append(f"({str(field.dtype)} *)buffer_{field.name}.buf")
 
             if insert_checks:
                 np_dtype = field.dtype.numpy_dtype
                 item_size = np_dtype.itemsize
 
-                if np_dtype.isbuiltin and FieldType.is_generic(field):
-                    dtype_cond = "buffer_{name}.format[0] == '{format}'".format(name=field.name,
-                                                                                format=field.dtype.numpy_dtype.char)
+                aligned = False
+                if ast_node.assignments:
+                    aligned = any([a.lhs.args[2] for a in ast_node.assignments
+                                   if hasattr(a, 'lhs') and isinstance(a.lhs, CastFunc)
+                                   and hasattr(a.lhs, 'dtype') and isinstance(a.lhs.dtype, VectorType)])
+
+                if ast_node.instruction_set and aligned:
+                    byte_width = ast_node.instruction_set['width'] * item_size
+                    if 'cachelineZero' in ast_node.instruction_set:
+                        has_openmp, has_nontemporal = False, False
+                        for loop in ast_node.atoms(LoopOverCoordinate):
+                            has_openmp = has_openmp or any(['#pragma omp' in p for p in loop.prefix_lines])
+                            has_nontemporal = has_nontemporal or any([a.args[0].field == field and a.args[3] for a in
+                                                                      loop.atoms(VectorMemoryAccess)])
+                        if has_openmp and has_nontemporal:
+                            cl_size = ast_node.instruction_set['cachelineSize']
+                            byte_width = f"({cl_size}) < SIZE_MAX ? ({cl_size}) : ({byte_width})"
+                    offset = max(max(ast_node.ghost_layers)) * item_size
+                    offset_cond = f"(((uintptr_t) buffer_{field.name}.buf) + {offset}) % ({byte_width}) == 0"
+
+                    message = str(offset) + ". This is probably due to a different number of ghost_layers chosen for " \
+                                            "the arrays and the kernel creation. If the number of ghost layers for " \
+                                            "the kernel creation is not specified it will choose a suitable value " \
+                                            "automatically. This value might not " \
+                                            "be compatible with the allocated arrays."
+                    if type(byte_width) is not int:
+                        message += " Note that when both OpenMP and non-temporal stores are enabled, alignment to the "\
+                                   "cacheline size is required."
+                    pre_call_code += template_check_array.format(cond=offset_cond, what="offset", name=field.name,
+                                                                 expected=message)
+
+                if (np_dtype.isbuiltin and FieldType.is_generic(field)
+                        and not np.issubdtype(field.dtype.numpy_dtype, np.complexfloating)):
+                    dtype_cond = f"buffer_{field.name}.format[0] == '{field.dtype.numpy_dtype.char}'"
                     pre_call_code += template_check_array.format(cond=dtype_cond, what="data type", name=field.name,
                                                                  expected=str(field.dtype.numpy_dtype))
 
-                item_size_cond = "buffer_{name}.itemsize == {size}".format(name=field.name, size=item_size)
+                item_size_cond = f"buffer_{field.name}.itemsize == {item_size}"
                 pre_call_code += template_check_array.format(cond=item_size_cond, what="itemsize", name=field.name,
                                                              expected=item_size)
 
                 if field.has_fixed_shape:
-                    shape_cond = ["buffer_{name}.shape[{i}] == {s}".format(s=s, name=field.name, i=i)
+                    shape_cond = [f"buffer_{field.name}.shape[{i}] == {s}"
                                   for i, s in enumerate(field.spatial_shape)]
                     shape_cond = " && ".join(shape_cond)
                     pre_call_code += template_check_array.format(cond=shape_cond, what="shape", name=field.name,
@@ -375,14 +456,15 @@ def create_function_boilerplate_code(parameter_info, name, insert_checks=True):
         elif param.is_field_stride:
             field = param.fields[0]
             item_size = field.dtype.numpy_dtype.itemsize
-            parameters.append("buffer_{name}.strides[{i}] / {bytes}".format(bytes=item_size, i=param.symbol.coordinate,
-                                                                            name=field.name))
+            parameters.append(f"buffer_{field.name}.strides[{param.symbol.coordinate}] / {item_size}")
         elif param.is_field_shape:
-            parameters.append("buffer_{name}.shape[{i}]".format(i=param.symbol.coordinate, name=param.field_name))
+            parameters.append(f"buffer_{param.field_name}.shape[{param.symbol.coordinate}]")
         else:
             extract_function, target_type = type_mapping[param.symbol.dtype.numpy_dtype.type]
-            pre_call_code += template_extract_scalar.format(extract_function=extract_function, target_type=target_type,
+            pre_call_code += template_extract_scalar.format(extract_function=extract_function,
+                                                            target_type=target_type,
                                                             name=param.symbol.name)
+
             parameters.append(param.symbol.name)
 
     pre_call_code += equal_size_check(variable_sized_normal_fields)
@@ -401,10 +483,17 @@ def create_module_boilerplate_code(module_name, names):
 
 
 def load_kernel_from_file(module_name, function_name, path):
-    from importlib.util import spec_from_file_location, module_from_spec
-    spec = spec_from_file_location(name=module_name, location=path)
-    mod = module_from_spec(spec)
-    spec.loader.exec_module(mod)
+    try:
+        spec = importlib.util.spec_from_file_location(name=module_name, location=path)
+        mod = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(mod)
+    except ImportError:
+        warnings.warn(f"Could not load {path}, trying on more time in 5 seconds ...")
+        time.sleep(5)
+        spec = importlib.util.spec_from_file_location(name=module_name, location=path)
+        mod = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(mod)
+
     return getattr(mod, function_name)
 
 
@@ -413,7 +502,6 @@ def run_compile_step(command):
     config_env = compiler_config['env'] if 'env' in compiler_config else {}
     compile_environment = os.environ.copy()
     compile_environment.update(config_env)
-
     try:
         shell = True if compiler_config['os'].lower() == 'windows' else False
         subprocess.check_output(command, env=compile_environment, stderr=subprocess.STDOUT, shell=shell)
@@ -424,61 +512,74 @@ def run_compile_step(command):
 
 
 class ExtensionModuleCode:
-    def __init__(self, module_name='generated'):
+    def __init__(self, module_name='generated', custom_backend=None):
         self.module_name = module_name
 
         self._ast_nodes = []
         self._function_names = []
+        self._custom_backend = custom_backend
+        self._code_string = str()
+        self._code_hash = None
 
     def add_function(self, ast, name=None):
         self._ast_nodes.append(ast)
         self._function_names.append(name if name is not None else ast.function_name)
 
-    def write_to_file(self, restrict_qualifier, function_prefix, file):
+    def create_code_string(self, restrict_qualifier, function_prefix):
+        self._code_string = str()
+
         headers = {'<math.h>', '<stdint.h>'}
         for ast in self._ast_nodes:
+            for field in ast.fields_accessed:
+                if isinstance(field.dtype, BasicType) and field.dtype.is_half():
+                    # Add the half precision header only if half precision numbers occur in the AST
+                    headers.add('"half_precision.h"')
             headers.update(get_headers(ast))
-        header_list = list(headers)
-        header_list.sort()
+
+        header_list = sorted(headers)
         header_list.insert(0, '"Python.h"')
+        ps_headers = [os.path.join(os.path.dirname(__file__), '..', 'include', h[1:-1]) for h in header_list
+                      if os.path.exists(os.path.join(os.path.dirname(__file__), '..', 'include', h[1:-1]))]
+        header_hash = b''.join([hashlib.sha256(open(h, 'rb').read()).digest() for h in ps_headers])
 
-        includes = "\n".join(["#include %s" % (include_file,) for include_file in header_list])
-        print(includes, file=file)
-        print("\n", file=file)
-        print("#define RESTRICT %s" % (restrict_qualifier,), file=file)
-        print("#define FUNC_PREFIX %s" % (function_prefix,), file=file)
-        print("\n", file=file)
+        includes = "\n".join([f"#include {include_file}" for include_file in header_list])
+        self._code_string += includes
+        self._code_string += "\n"
+        self._code_string += f"#define RESTRICT {restrict_qualifier} \n"
+        self._code_string += f"#define FUNC_PREFIX {function_prefix}"
+        self._code_string += "\n"
 
         for ast, name in zip(self._ast_nodes, self._function_names):
             old_name = ast.function_name
-            ast.function_name = "kernel_" + name
-            print(generate_c(ast), file=file)
-            print(create_function_boilerplate_code(ast.get_parameters(), name), file=file)
+            ast.function_name = f"kernel_{name}"
+            self._code_string += generate_c(ast, custom_backend=self._custom_backend)
+            self._code_string += create_function_boilerplate_code(ast.get_parameters(), name, ast)
             ast.function_name = old_name
-        print(create_module_boilerplate_code(self.module_name, self._function_names), file=file)
 
+        self._code_hash = "mod_" + hashlib.sha256(self._code_string.encode() + header_hash).hexdigest()
+        self._code_string += create_module_boilerplate_code(self._code_hash, self._function_names)
 
-class KernelWrapper:
-    def __init__(self, kernel, parameters, ast_node):
-        self.kernel = kernel
-        self.parameters = parameters
-        self.ast = ast_node
+    def get_hash_of_code(self):
+        assert self._code_string, "The code must be generated first"
+        return self._code_hash
 
-    def __call__(self, **kwargs):
-        return self.kernel(**kwargs)
+    def write_to_file(self, file):
+        assert self._code_string, "The code must be generated first"
+        print(self._code_string, file=file)
 
 
-def compile_module(code, code_hash, base_dir):
+def compile_module(code, code_hash, base_dir, compile_flags=None):
+    if compile_flags is None:
+        compile_flags = []
+
     compiler_config = get_compiler_config()
-    extra_flags = ['-I' + get_paths()['include'], '-I' + get_pystencils_include_path()]
+    extra_flags = ['-I' + sysconfig.get_paths()['include'], '-I' + get_pystencils_include_path()] + compile_flags
 
     if compiler_config['os'].lower() == 'windows':
-        function_prefix = '__declspec(dllexport)'
         lib_suffix = '.pyd'
         object_suffix = '.obj'
         windows = True
     else:
-        function_prefix = ''
         lib_suffix = '.so'
         object_suffix = '.o'
         windows = False
@@ -488,8 +589,11 @@ def compile_module(code, code_hash, base_dir):
     object_file = os.path.join(base_dir, code_hash + object_suffix)
 
     if not os.path.exists(object_file):
-        with file_handle_for_atomic_write(src_file) as f:
-            code.write_to_file(compiler_config['restrict_qualifier'], function_prefix, f)
+        try:
+            with open(src_file, 'x') as f:
+                code.write_to_file(f)
+        except FileExistsError:
+            pass
 
         if windows:
             compile_cmd = ['cl.exe', '/c', '/EHsc'] + compiler_config['flags'].split()
@@ -503,11 +607,15 @@ def compile_module(code, code_hash, base_dir):
 
         # Linking
         if windows:
-            import sysconfig
             config_vars = sysconfig.get_config_vars()
             py_lib = os.path.join(config_vars["installed_base"], "libs",
-                                  "python{}.lib".format(config_vars["py_version_nodot"]))
+                                  f"python{config_vars['py_version_nodot']}.lib")
             run_compile_step(['link.exe', py_lib, '/DLL', '/out:' + lib_file, object_file])
+        elif platform.system().lower() == 'darwin':
+            with atomic_file_write(lib_file) as file_name:
+                run_compile_step([compiler_config['command'], '-shared', object_file, '-o', file_name, '-undefined',
+                                  'dynamic_lookup']
+                                 + compiler_config['flags'].split())
         else:
             with atomic_file_write(lib_file) as file_name:
                 run_compile_step([compiler_config['command'], '-shared', object_file, '-o', file_name]
@@ -515,18 +623,34 @@ def compile_module(code, code_hash, base_dir):
     return lib_file
 
 
-def compile_and_load(ast):
+def compile_and_load(ast, custom_backend=None):
     cache_config = get_cache_config()
-    code_hash_str = "mod_" + hashlib.sha256(generate_c(ast, dialect='c').encode()).hexdigest()
-    code = ExtensionModuleCode(module_name=code_hash_str)
+
+    compiler_config = get_compiler_config()
+    if compiler_config['os'].lower() == 'windows':
+        function_prefix = '__declspec(dllexport)'
+    elif ast.instruction_set and 'function_prefix' in ast.instruction_set:
+        function_prefix = ast.instruction_set['function_prefix']
+    else:
+        function_prefix = ''
+
+    code = ExtensionModuleCode(custom_backend=custom_backend)
     code.add_function(ast, ast.function_name)
 
+    code.create_code_string(compiler_config['restrict_qualifier'], function_prefix)
+    code_hash_str = code.get_hash_of_code()
+
+    compile_flags = []
+    if ast.instruction_set and 'compile_flags' in ast.instruction_set:
+        compile_flags = ast.instruction_set['compile_flags']
+
     if cache_config['object_cache'] is False:
-        with TemporaryDirectory() as base_dir:
-            lib_file = compile_module(code, code_hash_str, base_dir)
+        with tempfile.TemporaryDirectory() as base_dir:
+            lib_file = compile_module(code, code_hash_str, base_dir, compile_flags=compile_flags)
             result = load_kernel_from_file(code_hash_str, ast.function_name, lib_file)
     else:
-        lib_file = compile_module(code, code_hash_str, base_dir=cache_config['object_cache'])
+        lib_file = compile_module(code, code_hash_str, base_dir=cache_config['object_cache'],
+                                  compile_flags=compile_flags)
         result = load_kernel_from_file(code_hash_str, ast.function_name, lib_file)
 
     return KernelWrapper(result, ast.get_parameters(), ast)

pystencils/cpu/kernelcreation.py → src/pystencils/cpu/kernelcreation.py

 import sympy as sp
-from functools import partial
-from pystencils.astnodes import SympyAssignment, Block, LoopOverCoordinate, KernelFunction
-from pystencils.transformations import resolve_buffer_accesses, resolve_field_accesses, make_loop_over_domain, \
-    add_types, get_optimal_loop_ordering, parse_base_pointer_info, move_constants_before_loop, \
-    split_inner_loop, get_base_buffer_index, filtered_tree_iteration
-from pystencils.data_types import TypedSymbol, BasicType, StructType, create_type
-from pystencils.field import Field, FieldType
+
 import pystencils.astnodes as ast
+from pystencils.config import CreateKernelConfig
+from pystencils.enums import Target, Backend
+from pystencils.astnodes import Block, KernelFunction, LoopOverCoordinate, SympyAssignment
 from pystencils.cpu.cpujit import make_python_function
-from pystencils.assignment import Assignment
-from typing import List, Union
-
-AssignmentOrAstNodeList = List[Union[Assignment, ast.Node]]
+from pystencils.typing import StructType, TypedSymbol, create_type
+from pystencils.typing.transformations import add_types
+from pystencils.field import Field, FieldType
+from pystencils.node_collection import NodeCollection
+from pystencils.transformations import (
+    filtered_tree_iteration, iterate_loops_by_depth, get_base_buffer_index, get_optimal_loop_ordering,
+    make_loop_over_domain, add_outer_loop_over_indexed_elements,
+    move_constants_before_loop, parse_base_pointer_info, resolve_buffer_accesses,
+    resolve_field_accesses, split_inner_loop)
 
 
-def create_kernel(assignments: AssignmentOrAstNodeList, function_name: str = "kernel", type_info='double',
-                  split_groups=(), iteration_slice=None, ghost_layers=None,
-                  skip_independence_check=False) -> KernelFunction:
+def create_kernel(assignments: NodeCollection,
+                  config: CreateKernelConfig) -> KernelFunction:
     """Creates an abstract syntax tree for a kernel function, by taking a list of update rules.
 
     Loops are created according to the field accesses in the equations.
@@ -24,35 +25,25 @@ def create_kernel(assignments: AssignmentOrAstNodeList, function_name: str = "ke
     Args:
         assignments: list of sympy equations, containing accesses to :class:`pystencils.field.Field`.
         Defining the update rules of the kernel
-        function_name: name of the generated function - only important if generated code is written out
-        type_info: a map from symbol name to a C type specifier. If not specified all symbols are assumed to
-                   be of type 'double' except symbols which occur on the left hand side of equations where the
-                   right hand side is a sympy Boolean which are assumed to be 'bool' .
-        split_groups: Specification on how to split up inner loop into multiple loops. For details see
-                      transformation :func:`pystencils.transformation.split_inner_loop`
-        iteration_slice: if not None, iteration is done only over this slice of the field
-        ghost_layers: a sequence of pairs for each coordinate with lower and upper nr of ghost layers
-                     if None, the number of ghost layers is determined automatically and assumed to be equal for a
-                     all dimensions
-        skip_independence_check: don't check that loop iterations are independent. This is needed e.g. for
-                                 periodicity kernel, that access the field outside the iteration bounds. Use with care!
+        config: create kernel config
 
     Returns:
         AST node representing a function, that can be printed as C or CUDA code
     """
+    function_name = config.function_name
+    iteration_slice = config.iteration_slice
+    ghost_layers = config.ghost_layers
+    fields_written = assignments.bound_fields
+    fields_read = assignments.rhs_fields
 
-    def type_symbol(term):
-        if isinstance(term, Field.Access) or isinstance(term, TypedSymbol):
-            return term
-        elif isinstance(term, sp.Symbol):
-            if not hasattr(type_info, '__getitem__'):
-                return TypedSymbol(term.name, create_type(type_info))
-            else:
-                return TypedSymbol(term.name, type_info[term.name])
-        else:
-            raise ValueError("Term has to be field access or symbol")
+    split_groups = ()
+    if 'split_groups' in assignments.simplification_hints:
+        split_groups = assignments.simplification_hints['split_groups']
+    assignments = assignments.all_assignments
+
+    # TODO Cleanup: move add_types to create_domain_kernel or create_kernel
+    assignments = add_types(assignments, config)
 
-    fields_read, fields_written, assignments = add_types(assignments, type_info, not skip_independence_check)
     all_fields = fields_read.union(fields_written)
     read_only_fields = set([f.name for f in fields_read - fields_written])
 
@@ -61,15 +52,33 @@ def create_kernel(assignments: AssignmentOrAstNodeList, function_name: str = "ke
 
     body = ast.Block(assignments)
     loop_order = get_optimal_loop_ordering(fields_without_buffers)
-    ast_node = make_loop_over_domain(body, function_name, iteration_slice=iteration_slice,
-                                     ghost_layers=ghost_layers, loop_order=loop_order)
-    ast_node.target = 'cpu'
+    loop_node, ghost_layer_info = make_loop_over_domain(body, iteration_slice=iteration_slice,
+                                                        ghost_layers=ghost_layers, loop_order=loop_order)
+    loop_node = add_outer_loop_over_indexed_elements(loop_node)
+
+    ast_node = KernelFunction(loop_node, Target.CPU, Backend.C, compile_function=make_python_function,
+                              ghost_layers=ghost_layer_info, function_name=function_name, assignments=assignments)
 
     if split_groups:
+        type_info = config.data_type
+
+        def type_symbol(term):
+            if isinstance(term, Field.Access) or isinstance(term, TypedSymbol):
+                return term
+            elif isinstance(term, sp.Symbol):
+                if isinstance(type_info, str) or not hasattr(type_info, '__getitem__'):
+                    return TypedSymbol(term.name, create_type(type_info))
+                else:
+                    return TypedSymbol(term.name, type_info[term.name])
+            else:
+                raise ValueError("Term has to be field access or symbol")
+
         typed_split_groups = [[type_symbol(s) for s in split_group] for split_group in split_groups]
         split_inner_loop(ast_node, typed_split_groups)
 
-    base_pointer_spec = [['spatialInner0'], ['spatialInner1']] if len(loop_order) >= 2 else [['spatialInner0']]
+    base_pointer_spec = config.base_pointer_specification
+    if base_pointer_spec is None:
+        base_pointer_spec = []
     base_pointer_info = {field.name: parse_base_pointer_info(base_pointer_spec, loop_order,
                                                              field.spatial_dimensions, field.index_dimensions)
                          for field in fields_without_buffers}
@@ -81,14 +90,14 @@ def create_kernel(assignments: AssignmentOrAstNodeList, function_name: str = "ke
 
     if any(FieldType.is_buffer(f) for f in all_fields):
         resolve_buffer_accesses(ast_node, get_base_buffer_index(ast_node), read_only_fields)
+    # TODO think about typing
     resolve_field_accesses(ast_node, read_only_fields, field_to_base_pointer_info=base_pointer_info)
     move_constants_before_loop(ast_node)
-    ast_node.compile = partial(make_python_function, ast_node)
     return ast_node
 
 
-def create_indexed_kernel(assignments: AssignmentOrAstNodeList, index_fields, function_name="kernel",
-                          type_info=None, coordinate_names=('x', 'y', 'z')) -> KernelFunction:
+def create_indexed_kernel(assignments: NodeCollection,
+                          config: CreateKernelConfig) -> KernelFunction:
     """
     Similar to :func:`create_kernel`, but here not all cells of a field are updated but only cells with
     coordinates which are stored in an index field. This traversal method can e.g. be used for boundary handling.
@@ -100,33 +109,41 @@ def create_indexed_kernel(assignments: AssignmentOrAstNodeList, index_fields, fu
 
     Args:
         assignments: list of assignments
-        index_fields: list of index fields, i.e. 1D fields with struct data type
-        type_info: see documentation of :func:`create_kernel`
-        function_name: see documentation of :func:`create_kernel`
-        coordinate_names: name of the coordinate fields in the struct data type
+        config: Kernel configuration
     """
-    fields_read, fields_written, assignments = add_types(assignments, type_info, check_independence_condition=False)
+    function_name = config.function_name
+    index_fields = config.index_fields
+    coordinate_names = config.coordinate_names
+    fields_written = assignments.bound_fields
+    fields_read = assignments.rhs_fields
+
     all_fields = fields_read.union(fields_written)
+    # extract the index fields based on the name. The original index field might have been modified
+    index_fields = [idx_field for idx_field in index_fields if idx_field.name in [f.name for f in all_fields]]
+    non_index_fields = [f for f in all_fields if f not in index_fields]
+    spatial_coordinates = {f.spatial_dimensions for f in non_index_fields}
+    assert len(spatial_coordinates) == 1, f"Non-index fields do not have the same number of spatial coordinates " \
+                                          f"Non index fields are {non_index_fields}, spatial coordinates are " \
+                                          f"{spatial_coordinates}"
+    spatial_coordinates = list(spatial_coordinates)[0]
+
+    assignments = assignments.all_assignments
+    assignments = add_types(assignments, config)
 
     for index_field in index_fields:
         index_field.field_type = FieldType.INDEXED
         assert FieldType.is_indexed(index_field)
         assert index_field.spatial_dimensions == 1, "Index fields have to be 1D"
 
-    non_index_fields = [f for f in all_fields if f not in index_fields]
-    spatial_coordinates = {f.spatial_dimensions for f in non_index_fields}
-    assert len(spatial_coordinates) == 1, "Non-index fields do not have the same number of spatial coordinates"
-    spatial_coordinates = list(spatial_coordinates)[0]
-
     def get_coordinate_symbol_assignment(name):
         for idx_field in index_fields:
             assert isinstance(idx_field.dtype, StructType), "Index fields have to have a struct data type"
             data_type = idx_field.dtype
             if data_type.has_element(name):
                 rhs = idx_field[0](name)
-                lhs = TypedSymbol(name, BasicType(data_type.get_element_type(name)))
+                lhs = TypedSymbol(name, data_type.get_element_type(name))
                 return SympyAssignment(lhs, rhs)
-        raise ValueError("Index %s not found in any of the passed index fields" % (name,))
+        raise ValueError(f"Index {name} not found in any of the passed index fields")
 
     coordinate_symbol_assignments = [get_coordinate_symbol_assignment(n)
                                      for n in coordinate_names[:spatial_coordinates]]
@@ -141,14 +158,14 @@ def create_indexed_kernel(assignments: AssignmentOrAstNodeList, index_fields, fu
         loop_body.append(assignment)
 
     function_body = Block([loop_node])
-    ast_node = KernelFunction(function_body, backend="cpu", function_name=function_name)
+    ast_node = KernelFunction(function_body, Target.CPU, Backend.C, make_python_function,
+                              ghost_layers=None, function_name=function_name, assignments=assignments)
 
     fixed_coordinate_mapping = {f.name: coordinate_typed_symbols for f in non_index_fields}
 
     read_only_fields = set([f.name for f in fields_read - fields_written])
     resolve_field_accesses(ast_node, read_only_fields, field_to_fixed_coordinates=fixed_coordinate_mapping)
     move_constants_before_loop(ast_node)
-    ast_node.compile = partial(make_python_function, ast_node)
     return ast_node
 
 
@@ -168,7 +185,7 @@ def add_openmp(ast_node, schedule="static", num_threads=True, collapse=None, ass
 
     assert type(ast_node) is ast.KernelFunction
     body = ast_node.body
-    threads_clause = "" if num_threads and isinstance(num_threads, bool) else " num_threads(%s)" % (num_threads,)
+    threads_clause = "" if num_threads and isinstance(num_threads, bool) else f" num_threads({num_threads})"
     wrapper_block = ast.PragmaBlock('#pragma omp parallel' + threads_clause, body.take_child_nodes())
     body.append(wrapper_block)
 
@@ -184,10 +201,6 @@ def add_openmp(ast_node, schedule="static", num_threads=True, collapse=None, ass
         except TypeError:
             loop_range = None
 
-        if num_threads is None:
-            import multiprocessing
-            num_threads = multiprocessing.cpu_count()
-
         if loop_range is not None and loop_range < num_threads and not collapse:
             contained_loops = [l for l in loop_to_parallelize.body.args if isinstance(l, LoopOverCoordinate)]
             if len(contained_loops) == 1:
@@ -199,7 +212,22 @@ def add_openmp(ast_node, schedule="static", num_threads=True, collapse=None, ass
                 except TypeError:
                     pass
 
-        prefix = "#pragma omp for schedule(%s)" % (schedule,)
+        prefix = f"#pragma omp for schedule({schedule})"
         if collapse:
-            prefix += " collapse(%d)" % (collapse, )
+            prefix += f" collapse({collapse})"
         loop_to_parallelize.prefix_lines.append(prefix)
+
+
+def add_pragmas(ast_node, pragma_lines, nesting_depth=-1):
+    """Prepends given pragma lines to all loops of specified nesting depth.
+    
+    Args:
+        ast_node: pystencils abstract syntax tree
+        pragma_lines: Iterable of strings containing the pragma lines
+        nesting_depth: Nesting depth of the loops the pragmas should be applied to.
+                       Outermost loop has depth 0.
+                       A depth of -1 indicates the innermost loops.
+    """
+    loop_nodes = iterate_loops_by_depth(ast_node, nesting_depth)
+    for n in loop_nodes:
+        n.prefix_lines += list(pragma_lines)

pystencils/cpu/msvc_detection.py → src/pystencils/cpu/msvc_detection.py

-import subprocess
 import os
+import subprocess
 
 
 def get_environment(version_specifier, arch='x64'):
@@ -71,7 +71,7 @@ def normalize_msvc_version(version):
 
 def get_environment_from_vc_vars_file(vc_vars_file, arch):
     out = subprocess.check_output(
-        'cmd /u /c "{}" {} && set'.format(vc_vars_file, arch),
+        f'cmd /u /c "{vc_vars_file}" {arch} && set',
         stderr=subprocess.STDOUT,
     ).decode('utf-16le', errors='replace')
 

pystencils/cpu/vectorization.py → src/pystencils/cpu/vectorization.py

-import sympy as sp
 import warnings
-from typing import Union, Container
-from pystencils.backends.simd_instruction_sets import get_vector_instruction_set
-from pystencils.fast_approximation import fast_division, fast_sqrt, fast_inv_sqrt
-from pystencils.integer_functions import modulo_floor, modulo_ceil
-from pystencils.sympyextensions import fast_subs
-from pystencils.data_types import TypedSymbol, VectorType, get_type_of_expression, vector_memory_access, cast_func, \
-    collate_types, PointerType
+from typing import Container, Union
+
+import numpy as np
+import sympy as sp
+from sympy.logic.boolalg import BooleanFunction, BooleanAtom
+
 import pystencils.astnodes as ast
-from pystencils.transformations import cut_loop, filtered_tree_iteration, replace_inner_stride_with_one
+from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets, get_vector_instruction_set
+from pystencils.typing import (BasicType, PointerType, TypedSymbol, VectorType, CastFunc, collate_types,
+                               get_type_of_expression, VectorMemoryAccess)
+from pystencils.functions import DivFunc
 from pystencils.field import Field
+from pystencils.integer_functions import modulo_ceil, modulo_floor
+from pystencils.sympyextensions import fast_subs
+from pystencils.transformations import cut_loop, filtered_tree_iteration, replace_inner_stride_with_one
 
 
 # noinspection PyPep8Naming
 class vec_any(sp.Function):
-    nargs = (1, )
+    nargs = (1,)
 
 
 # noinspection PyPep8Naming
 class vec_all(sp.Function):
-    nargs = (1, )
+    nargs = (1,)
+
+
+class NontemporalFence(ast.Node):
+    def __init__(self):
+        super(NontemporalFence, self).__init__(parent=None)
+
+    @property
+    def symbols_defined(self):
+        return set()
+
+    @property
+    def undefined_symbols(self):
+        return set()
+
+    @property
+    def args(self):
+        return []
 
+    def __eq__(self, other):
+        return isinstance(other, NontemporalFence)
 
-def vectorize(kernel_ast: ast.KernelFunction, instruction_set: str = 'avx',
+
+class CachelineSize(ast.Node):
+    symbol = sp.Symbol("_clsize")
+    mask_symbol = sp.Symbol("_clsize_mask")
+    last_symbol = sp.Symbol("_cl_lastvec")
+    
+    def __init__(self):
+        super(CachelineSize, self).__init__(parent=None)
+
+    @property
+    def symbols_defined(self):
+        return {self.symbol, self.mask_symbol, self.last_symbol}
+
+    @property
+    def undefined_symbols(self):
+        return set()
+
+    @property
+    def args(self):
+        return []
+
+    def __eq__(self, other):
+        return isinstance(other, CachelineSize)
+
+    def __hash__(self):
+        return hash(self.symbol)
+
+
+def vectorize(kernel_ast: ast.KernelFunction, instruction_set: str = 'best',
               assume_aligned: bool = False, nontemporal: Union[bool, Container[Union[str, Field]]] = False,
               assume_inner_stride_one: bool = False, assume_sufficient_line_padding: bool = True):
+    # TODO Vectorization Revamp we first introduce the remainder loop and then check if we can even vectorise.
+    #  Maybe first copy the ast and return the copied version on failure
     """Explicit vectorization using SIMD vectorization via intrinsics.
 
     Args:
@@ -47,9 +100,14 @@ def vectorize(kernel_ast: ast.KernelFunction, instruction_set: str = 'avx',
                                         depending on the access pattern there might be additional padding
                                         required at the end of the array
     """
+    if instruction_set == 'best':
+        if get_supported_instruction_sets():
+            instruction_set = get_supported_instruction_sets()[-1]
+        else:
+            instruction_set = 'avx'
     if instruction_set is None:
         return
-    
+
     all_fields = kernel_ast.fields_accessed
     if nontemporal is None or nontemporal is False:
         nontemporal = {}
@@ -65,39 +123,53 @@ def vectorize(kernel_ast: ast.KernelFunction, instruction_set: str = 'avx',
                                   "to differently typed floating point fields")
     float_size = field_float_dtypes.pop().numpy_dtype.itemsize
     assert float_size in (8, 4)
-    vector_is = get_vector_instruction_set('double' if float_size == 8 else 'float',
-                                           instruction_set=instruction_set)
-    vector_width = vector_is['width']
+    default_float_type = 'float64' if float_size == 8 else 'float32'
+    vector_is = get_vector_instruction_set(default_float_type, instruction_set=instruction_set)
     kernel_ast.instruction_set = vector_is
 
-    vectorize_inner_loops_and_adapt_load_stores(kernel_ast, vector_width, assume_aligned,
-                                                nontemporal, assume_sufficient_line_padding)
-    insert_vector_casts(kernel_ast)
+    if nontemporal and 'cachelineZero' in vector_is:
+        kernel_ast.use_all_written_field_sizes = True
+    strided = 'storeS' in vector_is and 'loadS' in vector_is
+    keep_loop_stop = '{loop_stop}' in vector_is['storeA' if assume_aligned and 'storeA' in vector_is else 'storeU']
+    vectorize_inner_loops_and_adapt_load_stores(kernel_ast, assume_aligned, nontemporal,
+                                                strided, keep_loop_stop, assume_sufficient_line_padding,
+                                                default_float_type)
 
 
-def vectorize_inner_loops_and_adapt_load_stores(ast_node, vector_width, assume_aligned, nontemporal_fields,
-                                                assume_sufficient_line_padding):
+def vectorize_inner_loops_and_adapt_load_stores(ast_node, assume_aligned, nontemporal_fields,
+                                                strided, keep_loop_stop, assume_sufficient_line_padding,
+                                                default_float_type):
     """Goes over all innermost loops, changes increment to vector width and replaces field accesses by vector type."""
-    all_loops = filtered_tree_iteration(ast_node, ast.LoopOverCoordinate, stop_type=ast.SympyAssignment)
-    inner_loops = [n for n in all_loops if n.is_innermost_loop]
-    zero_loop_counters = {l.loop_counter_symbol: 0 for l in all_loops}
+    all_loops = list(filtered_tree_iteration(ast_node, ast.LoopOverCoordinate, stop_type=ast.SympyAssignment))
+    inner_loops = [loop for loop in all_loops if loop.is_innermost_loop]
+    zero_loop_counters = {loop.loop_counter_symbol: 0 for loop in all_loops}
+
+    vector_is = ast_node.instruction_set
+    assert vector_is, "The ast needs to hold information about the instruction_set for the vectorisation"
+    vector_width = vector_is['width']
+    vector_int_width = vector_is['intwidth']
 
     for loop_node in inner_loops:
         loop_range = loop_node.stop - loop_node.start
 
         # cut off loop tail, that is not a multiple of four
-        if assume_aligned and assume_sufficient_line_padding:
+        if keep_loop_stop:
+            pass
+        elif assume_aligned and assume_sufficient_line_padding:
             loop_range = loop_node.stop - loop_node.start
             new_stop = loop_node.start + modulo_ceil(loop_range, vector_width)
             loop_node.stop = new_stop
         else:
             cutting_point = modulo_floor(loop_range, vector_width) + loop_node.start
-            loop_nodes = [l for l in cut_loop(loop_node, [cutting_point]).args if isinstance(l, ast.LoopOverCoordinate)]
+            # TODO cut_loop calls deepcopy on the loop_node. This is bad as documented in cut_loop
+            loop_nodes = [loop for loop in cut_loop(loop_node, [cutting_point]).args
+                          if isinstance(loop, ast.LoopOverCoordinate)]
             assert len(loop_nodes) in (0, 1, 2)  # 2 for main and tail loop, 1 if loop range divisible by vector width
             if len(loop_nodes) == 0:
                 continue
             loop_node = loop_nodes[0]
-        
+            # loop_node is the vectorized one
+
         # Find all array accesses (indexed) that depend on the loop counter as offset
         loop_counter_symbol = ast.LoopOverCoordinate.get_loop_counter_symbol(loop_node.coordinate_to_loop_over)
         substitutions = {}
@@ -105,54 +177,184 @@ def vectorize_inner_loops_and_adapt_load_stores(ast_node, vector_width, assume_a
         for indexed in loop_node.atoms(sp.Indexed):
             base, index = indexed.args
             if loop_counter_symbol in index.atoms(sp.Symbol):
+                if 'loadA' not in vector_is and 'storeA' not in vector_is and 'maskStoreA' not in vector_is:
+                    # don't need to generate the alignment check when there are no aligned load/store instructions
+                    aligned_access = False
+                else:
+                    if not isinstance(vector_width, int):
+                        raise NotImplementedError('Access alignment cannot be statically determined for sizeless '
+                                                  'vector ISAs')
+                    aligned_access = (index - loop_counter_symbol).subs(zero_loop_counters) % vector_width == 0
                 loop_counter_is_offset = loop_counter_symbol not in (index - loop_counter_symbol).atoms()
-                aligned_access = (index - loop_counter_symbol).subs(zero_loop_counters) == 0
-                if not loop_counter_is_offset:
+                stride = sp.simplify(index.subs({loop_counter_symbol: loop_counter_symbol + 1}) - index)
+                if not loop_counter_is_offset and (not strided or loop_counter_symbol in stride.atoms()):
                     successful = False
                     break
                 typed_symbol = base.label
-                assert type(typed_symbol.dtype) is PointerType, \
-                    "Type of access is {}, {}".format(typed_symbol.dtype, indexed)
+                assert type(typed_symbol.dtype) is PointerType, f"Type of access is {typed_symbol.dtype}, {indexed}"
 
                 vec_type = VectorType(typed_symbol.dtype.base_type, vector_width)
                 use_aligned_access = aligned_access and assume_aligned
                 nontemporal = False
                 if hasattr(indexed, 'field'):
                     nontemporal = (indexed.field in nontemporal_fields) or (indexed.field.name in nontemporal_fields)
-                substitutions[indexed] = vector_memory_access(indexed, vec_type, use_aligned_access, nontemporal)
+                substitutions[indexed] = VectorMemoryAccess(indexed, vec_type, use_aligned_access, nontemporal, True,
+                                                            stride if strided else 1)
+                if nontemporal:
+                    # insert NontemporalFence after the outermost loop
+                    parent = loop_node.parent
+                    while type(parent.parent.parent) is not ast.KernelFunction:
+                        parent = parent.parent
+                    parent.parent.insert_after(NontemporalFence(), parent, if_not_exists=True)
+                    # insert CachelineSize at the beginning of the kernel
+                    parent.parent.insert_front(CachelineSize(), if_not_exists=True)
         if not successful:
             warnings.warn("Could not vectorize loop because of non-consecutive memory access")
             continue
 
         loop_node.step = vector_width
         loop_node.subs(substitutions)
+        arg_1 = CastFunc(loop_counter_symbol, VectorType(loop_counter_symbol.dtype, vector_int_width))
+        arg_2 = CastFunc(tuple(range(vector_int_width if type(vector_int_width) is int else 2)),
+                         VectorType(loop_counter_symbol.dtype, vector_int_width))
+        vector_loop_counter = arg_1 + arg_2
+
+        fast_subs(loop_node, {loop_counter_symbol: vector_loop_counter},
+                  skip=lambda e: isinstance(e, ast.ResolvedFieldAccess) or isinstance(e, VectorMemoryAccess))
+
+        mask_conditionals(loop_node)
+
+        from pystencils.rng import RNGBase
+        substitutions = {}
+        for rng in loop_node.atoms(RNGBase):
+            new_result_symbols = [TypedSymbol(s.name, VectorType(s.dtype, width=vector_width))
+                                  for s in rng.result_symbols]
+            substitutions.update({s[0]: s[1] for s in zip(rng.result_symbols, new_result_symbols)})
+            rng._symbols_defined = set(new_result_symbols)
+        fast_subs(loop_node, substitutions, skip=lambda e: isinstance(e, RNGBase))
+        insert_vector_casts(loop_node, vector_is, default_float_type)
 
 
-def insert_vector_casts(ast_node):
+def mask_conditionals(loop_body):
+    def visit_node(node, mask):
+        if isinstance(node, ast.Conditional):
+            cond = node.condition_expr
+            skip = (loop_body.loop_counter_symbol not in cond.atoms(sp.Symbol)) or cond.func in (vec_all, vec_any)
+            cond = True if skip else cond
+
+            true_mask = sp.And(cond, mask)
+            visit_node(node.true_block, true_mask)
+            if node.false_block:
+                false_mask = sp.And(sp.Not(node.condition_expr), mask)
+                visit_node(node, false_mask)
+            if not skip:
+                node.condition_expr = vec_any(node.condition_expr)
+        elif isinstance(node, ast.SympyAssignment):
+            if mask is not True:
+                s = {ma: VectorMemoryAccess(*ma.args[0:4], sp.And(mask, ma.args[4]), *ma.args[5:])
+                     for ma in node.atoms(VectorMemoryAccess)}
+                node.subs(s)
+        else:
+            for arg in node.args:
+                visit_node(arg, mask)
+
+    visit_node(loop_body, mask=True)
+
+
+def insert_vector_casts(ast_node, instruction_set, default_float_type='double'):
     """Inserts necessary casts from scalar values to vector values."""
 
-    handled_functions = (sp.Add, sp.Mul, fast_division, fast_sqrt, fast_inv_sqrt, vec_any, vec_all)
+    handled_functions = (sp.Add, sp.Mul, vec_any, vec_all, DivFunc, sp.Abs)
 
-    def visit_expr(expr):
+    def is_scalar(expr) -> bool:
+        if hasattr(expr, "dtype"):
+            if type(expr.dtype) is VectorType:
+                return False
+            # Else branch: If expr is a CastFunc, then whether the expression
+            # is scalar is determined by the argument (remember: vector casts
+            # are not inserted yet). Therefore, we must recurse into the args of
+            # expr below. Otherwise, this expression is atomic and in that case
+            # it is assumed to be scalar below.
 
-        if isinstance(expr, cast_func) or isinstance(expr, vector_memory_access):
-            return expr
-        elif expr.func in handled_functions or isinstance(expr, sp.Rel) or isinstance(expr, sp.boolalg.BooleanFunction):
-            new_args = [visit_expr(a) for a in expr.args]
-            arg_types = [get_type_of_expression(a) for a in new_args]
+        if isinstance(expr, ast.ResolvedFieldAccess):
+            # expr.field is not in expr.args
+            return is_scalar(expr.field)
+        elif isinstance(expr, (vec_any, vec_all)):
+            return True
+
+        if not hasattr(expr, "args"):
+            return True
+
+        return all(is_scalar(arg) for arg in expr.args)
+
+    # TODO Vectorization Revamp: get rid of default_type
+    def visit_expr(expr, default_type='double', force_vectorize=False):
+        if isinstance(expr, VectorMemoryAccess):
+            return VectorMemoryAccess(*expr.args[0:4], visit_expr(expr.args[4], default_type, force_vectorize),
+                                      *expr.args[5:])
+        elif isinstance(expr, CastFunc):
+            cast_type = expr.args[1]
+            arg = visit_expr(expr.args[0], default_type, force_vectorize)
+            assert cast_type in [BasicType('float32'), BasicType('float64')], \
+                f'Vectorization cannot vectorize type {cast_type}'
+            return expr.func(arg, VectorType(cast_type, instruction_set['width']))
+        elif expr.func is sp.Abs and 'abs' not in instruction_set:
+            new_arg = visit_expr(expr.args[0], default_type, force_vectorize)
+            base_type = get_type_of_expression(expr.args[0]).base_type if type(expr.args[0]) is VectorMemoryAccess \
+                else get_type_of_expression(expr.args[0])
+            pw = sp.Piecewise((-new_arg, new_arg < CastFunc(0, base_type.numpy_dtype)),
+                              (new_arg, True))
+            return visit_expr(pw, default_type, force_vectorize)
+        elif expr.func in handled_functions or isinstance(expr, sp.Rel) or isinstance(expr, BooleanFunction):
+            if expr.func is sp.Mul and expr.args[0] == -1:
+                # special treatment for the unary minus: make sure that the -1 has the same type as the argument
+                dtype = int
+                for arg in expr.atoms(VectorMemoryAccess):
+                    if arg.dtype.base_type.is_float():
+                        dtype = arg.dtype.base_type.numpy_dtype.type
+                for arg in expr.atoms(TypedSymbol):
+                    if type(arg.dtype) is VectorType and arg.dtype.base_type.is_float():
+                        dtype = arg.dtype.base_type.numpy_dtype.type
+                if dtype is not int:
+                    if dtype is np.float32:
+                        default_type = 'float'
+                    expr = sp.Mul(dtype(expr.args[0]), *expr.args[1:])
+            new_args = [visit_expr(a, default_type, force_vectorize) for a in expr.args]
+            arg_types = [get_type_of_expression(a, default_float_type=default_type) for a in new_args]
             if not any(type(t) is VectorType for t in arg_types):
                 return expr
             else:
                 target_type = collate_types(arg_types)
-                casted_args = [cast_func(a, target_type) if t != target_type else a
-                               for a, t in zip(new_args, arg_types)]
+                casted_args = [
+                    CastFunc(a, target_type) if t != target_type and not isinstance(a, VectorMemoryAccess) else a
+                    for a, t in zip(new_args, arg_types)]
                 return expr.func(*casted_args)
+        elif expr.func is sp.UnevaluatedExpr:
+            assert expr.args[0].is_Pow or expr.args[0].is_Mul, "UnevaluatedExpr only implemented holding Mul or Pow"
+            # TODO this is only because cut_loop evaluates the multiplications again due to deepcopy. All this should
+            # TODO be fixed for real at some point.
+            if expr.args[0].is_Pow:
+                base = expr.args[0].base
+                exp = expr.args[0].exp
+                expr = sp.UnevaluatedExpr(sp.Mul(*([base] * +exp), evaluate=False))
+
+            new_args = [visit_expr(a, default_type, force_vectorize) for a in expr.args[0].args]
+            arg_types = [get_type_of_expression(a, default_float_type=default_type) for a in new_args]
+
+            target_type = collate_types(arg_types)
+            if not any(type(t) is VectorType for t in arg_types):
+                target_type = VectorType(target_type, instruction_set['width'])
+
+            casted_args = [
+                CastFunc(a, target_type) if t != target_type and not isinstance(a, VectorMemoryAccess) else a
+                for a, t in zip(new_args, arg_types)]
+            return expr.func(expr.args[0].func(*casted_args, evaluate=False))
         elif expr.func is sp.Pow:
-            new_arg = visit_expr(expr.args[0])
+            new_arg = visit_expr(expr.args[0], default_type, force_vectorize)
             return expr.func(new_arg, expr.args[1])
         elif expr.func == sp.Piecewise:
-            new_results = [visit_expr(a[0]) for a in expr.args]
-            new_conditions = [visit_expr(a[1]) for a in expr.args]
+            new_results = [visit_expr(a[0], default_type, force_vectorize) for a in expr.args]
+            new_conditions = [visit_expr(a[1], default_type, force_vectorize) for a in expr.args]
             types_of_results = [get_type_of_expression(a) for a in new_results]
             types_of_conditions = [get_type_of_expression(a) for a in new_conditions]
 
@@ -163,43 +365,61 @@ def insert_vector_casts(ast_node):
             if type(condition_target_type) is not VectorType and type(result_target_type) is VectorType:
                 condition_target_type = VectorType(condition_target_type, width=result_target_type.width)
 
-            casted_results = [cast_func(a, result_target_type) if t != result_target_type else a
+            casted_results = [CastFunc(a, result_target_type) if t != result_target_type else a
                               for a, t in zip(new_results, types_of_results)]
 
-            casted_conditions = [cast_func(a, condition_target_type)
+            casted_conditions = [CastFunc(a, condition_target_type)
                                  if t != condition_target_type and a is not True else a
                                  for a, t in zip(new_conditions, types_of_conditions)]
 
             return sp.Piecewise(*[(r, c) for r, c in zip(casted_results, casted_conditions)])
-        else:
+        elif isinstance(expr, TypedSymbol):
+            if force_vectorize:
+                expr_type = get_type_of_expression(expr)
+                if type(expr_type) is not VectorType:
+                    vector_type = VectorType(expr_type, instruction_set['width'])
+                    return CastFunc(expr, vector_type)
+            return expr
+        elif isinstance(expr, (sp.Number, BooleanAtom)):
             return expr
+        else:
+            raise NotImplementedError(f'Due to defensive programming we handle only specific expressions.\n'
+                                      f'The expression {expr} of type {type(expr)} is not known yet.')
 
-    def visit_node(node, substitution_dict):
+    def visit_node(node, substitution_dict, default_type='double'):
         substitution_dict = substitution_dict.copy()
         for arg in node.args:
             if isinstance(arg, ast.SympyAssignment):
                 assignment = arg
+                # If there is a remainder loop we do not vectorise it, thus lhs will indicate this
+                # if isinstance(assignment.lhs, ast.ResolvedFieldAccess):
+                # continue
                 subs_expr = fast_subs(assignment.rhs, substitution_dict,
                                       skip=lambda e: isinstance(e, ast.ResolvedFieldAccess))
-                assignment.rhs = visit_expr(subs_expr)
-                rhs_type = get_type_of_expression(assignment.rhs)
+
+                # If either side contains a vectorized subexpression, both sides
+                # must be fully vectorized.
+                lhs_scalar = is_scalar(assignment.lhs)
+                rhs_scalar = is_scalar(subs_expr)
+
+                assignment.rhs = visit_expr(subs_expr, default_type, force_vectorize=not (lhs_scalar and rhs_scalar))
+
                 if isinstance(assignment.lhs, TypedSymbol):
-                    lhs_type = assignment.lhs.dtype
-                    if type(rhs_type) is VectorType and type(lhs_type) is not VectorType:
+                    if lhs_scalar and not rhs_scalar:
+                        lhs_type = get_type_of_expression(assignment.lhs)
+                        rhs_type = get_type_of_expression(assignment.rhs)
                         new_lhs_type = VectorType(lhs_type, rhs_type.width)
                         new_lhs = TypedSymbol(assignment.lhs.name, new_lhs_type)
                         substitution_dict[assignment.lhs] = new_lhs
                         assignment.lhs = new_lhs
-                elif isinstance(assignment.lhs.func, cast_func):
-                    lhs_type = assignment.lhs.args[1]
-                    if type(lhs_type) is VectorType and type(rhs_type) is not VectorType:
-                        assignment.rhs = cast_func(assignment.rhs, lhs_type)
+                elif isinstance(assignment.lhs, VectorMemoryAccess):
+                    assignment.lhs = visit_expr(assignment.lhs, default_type)
             elif isinstance(arg, ast.Conditional):
                 arg.condition_expr = fast_subs(arg.condition_expr, substitution_dict,
                                                skip=lambda e: isinstance(e, ast.ResolvedFieldAccess))
-                arg.condition_expr = visit_expr(arg.condition_expr)
-                visit_node(arg, substitution_dict)
+                arg.condition_expr = visit_expr(arg.condition_expr, default_type)
+                visit_node(arg, substitution_dict, default_type)
             else:
-                visit_node(arg, substitution_dict)
+                visit_node(arg, substitution_dict, default_type)
 
-    visit_node(ast_node, {})
+    visit_node(ast_node, {}, default_float_type)

pystencils/datahandling/__init__.py → src/pystencils/datahandling/__init__.py

+import warnings
+
 from typing import Tuple, Union
-from .serial_datahandling import SerialDataHandling
+
 from .datahandling_interface import DataHandling
+from ..enums import Target
+from .serial_datahandling import SerialDataHandling
 
 try:
     # noinspection PyPep8Naming
@@ -17,9 +21,10 @@ except ImportError:
 def create_data_handling(domain_size: Tuple[int, ...],
                          periodicity: Union[bool, Tuple[bool, ...]] = False,
                          default_layout: str = 'SoA',
-                         default_target: str = 'cpu',
+                         default_target: Target = Target.CPU,
                          parallel: bool = False,
-                         default_ghost_layers: int = 1) -> DataHandling:
+                         default_ghost_layers: int = 1,
+                         device_number: Union[int, None] = None) -> DataHandling:
     """Creates a data handling instance.
 
     Args:
@@ -27,10 +32,19 @@ def create_data_handling(domain_size: Tuple[int, ...],
         periodicity: either True, False for full or no periodicity or a tuple of booleans indicating periodicity
                      for each coordinate
         default_layout: default array layout, that is used if not explicitly specified in 'add_array'
-        default_target: either 'cpu' or 'gpu'
+        default_target: `Target`
         parallel: if True a parallel domain is created using walberla - each MPI process gets a part of the domain
         default_ghost_layers: default number of ghost layers if not overwritten in 'add_array'
+        device_number: If `default_target` is set to 'GPU' and `parallel` is False, a device number should be
+                       specified. If none is given, the device with the largest amount of memory is used. If multiple
+                       devices have the same amount of memory, the one with the lower number is used
     """
+    if isinstance(default_target, str):
+        new_target = Target[default_target.upper()]
+        warnings.warn(f'Target "{default_target}" as str is deprecated. Use {new_target} instead',
+                      category=DeprecationWarning)
+        default_target = new_target
+
     if parallel:
         if wlb is None:
             raise ValueError("Cannot create parallel data handling because walberla module is not available")
@@ -55,8 +69,12 @@ def create_data_handling(domain_size: Tuple[int, ...],
         return ParallelDataHandling(blocks=block_storage, dim=dim, default_target=default_target,
                                     default_layout=default_layout, default_ghost_layers=default_ghost_layers)
     else:
-        return SerialDataHandling(domain_size, periodicity=periodicity, default_target=default_target,
-                                  default_layout=default_layout, default_ghost_layers=default_ghost_layers)
+        return SerialDataHandling(domain_size,
+                                  periodicity=periodicity,
+                                  default_target=default_target,
+                                  default_layout=default_layout,
+                                  default_ghost_layers=default_ghost_layers,
+                                  device_number=device_number)
 
 
 __all__ = ['create_data_handling']

pystencils/datahandling/blockiteration.py → src/pystencils/datahandling/blockiteration.py

@@ -7,6 +7,7 @@ import numpy as np
 
 from pystencils.datahandling.datahandling_interface import Block
 from pystencils.slicing import normalize_slice
+
 try:
     # noinspection PyPep8Naming
     import waLBerla as wlb
@@ -110,15 +111,15 @@ class ParallelBlock(Block):
     def __getitem__(self, data_name):
         result = self._block[self._name_prefix + data_name]
         type_name = type(result).__name__
-        if type_name == 'GhostLayerField':
-            result = wlb.field.toArray(result, withGhostLayers=self._gls)
+        if 'GhostLayerField' in type_name:
+            result = wlb.field.toArray(result, with_ghost_layers=self._gls)
             result = self._normalize_array_shape(result)
-        elif type_name == 'GpuField':
-            result = wlb.cuda.toGpuArray(result, withGhostLayers=self._gls)
+        elif 'GpuField' in type_name:
+            result = wlb.gpu.toGpuArray(result, with_ghost_layers=self._gls)
             result = self._normalize_array_shape(result)
         return result
 
     def _normalize_array_shape(self, arr):
-        if arr.shape[-1] == 1:
+        if arr.shape[-1] == 1 and len(arr.shape) == 4:
             arr = arr[..., 0]
         return arr[self._localSlice]

pystencils/datahandling/datahandling_interface.py → src/pystencils/datahandling/datahandling_interface.py

-import numpy as np
 from abc import ABC, abstractmethod
-from typing import Optional, Callable, Sequence, Iterable, Tuple, Dict, Union
-from pystencils.field import Field
+from typing import Callable, Dict, Iterable, Optional, Sequence, Tuple, Union
+
+import numpy as np
+
+from pystencils.enums import Target, Backend
+from pystencils.field import Field, FieldType
 
 
 class DataHandling(ABC):
@@ -14,7 +17,14 @@ class DataHandling(ABC):
     'gather' function that has collects (parts of the) distributed data on a single process.
     """
 
+    _GPU_LIKE_TARGETS = [Target.GPU]
+    _GPU_LIKE_BACKENDS = [Backend.CUDA]
+
     # ---------------------------- Adding and accessing data -----------------------------------------------------------
+    @property
+    @abstractmethod
+    def default_target(self) -> Target:
+        """Target Enum indicating the target of the computation"""
 
     @property
     @abstractmethod
@@ -34,7 +44,7 @@ class DataHandling(ABC):
     @abstractmethod
     def add_array(self, name: str, values_per_cell, dtype=np.float64,
                   latex_name: Optional[str] = None, ghost_layers: Optional[int] = None, layout: Optional[str] = None,
-                  cpu: bool = True, gpu: Optional[bool] = None, alignment=False) -> Field:
+                  cpu: bool = True, gpu: Optional[bool] = None, alignment=False, field_type=FieldType.GENERIC) -> Field:
         """Adds a (possibly distributed) array to the handling that can be accessed using the given name.
 
         For each array a symbolic field is available via the 'fields' dictionary
@@ -51,12 +61,63 @@ class DataHandling(ABC):
             layout: memory layout of array, either structure of arrays 'SoA' or array of structures 'AoS'.
                     this is only important if values_per_cell > 1
             cpu: allocate field on the CPU
-            gpu: allocate field on the GPU, if None, a GPU field is allocated if default_target is 'gpu'
+            gpu: allocate field on the GPU, if None, a GPU field is allocated if default_target is 'GPU'
             alignment: either False for no alignment, or the number of bytes to align to
         Returns:
             pystencils field, that can be used to formulate symbolic kernels
         """
 
+    def add_arrays(self,
+                   description: str,
+                   dtype=np.float64,
+                   ghost_layers: Optional[int] = None,
+                   layout: Optional[str] = None,
+                   cpu: bool = True,
+                   gpu: Optional[bool] = None,
+                   alignment=False,
+                   field_type=FieldType.GENERIC) -> Tuple[Field]:
+        """Adds multiple arrays using a string description similar to :func:`pystencils.fields`
+
+
+        >>> from pystencils.datahandling import create_data_handling
+        >>> dh = create_data_handling((20, 30))
+        >>> x, y =dh.add_arrays('x, y(9)')
+        >>> print(dh.fields)
+        {'x': x: double[22,32], 'y': y(9): double[22,32]}
+        >>> assert x == dh.fields['x']
+        >>> assert dh.fields['x'].shape == (22, 32)
+        >>> assert dh.fields['y'].index_shape == (9,)
+
+        Args:
+            description (str): String description of the fields to add
+            dtype: data type of the array as numpy data type
+            ghost_layers: number of ghost layers - if not specified a default value specified in the constructor
+                         is used
+            layout: memory layout of array, either structure of arrays 'SoA' or array of structures 'AoS'.
+                    this is only important if values_per_cell > 1
+            cpu: allocate field on the CPU
+            gpu: allocate field on the GPU, if None, a GPU field is allocated if default_target is 'GPU'
+            alignment: either False for no alignment, or the number of bytes to align to
+        Returns:
+            Fields representing the just created arrays
+        """
+        from pystencils.field import _parse_part1
+
+        names = []
+        for name, indices in _parse_part1(description):
+            names.append(name)
+            self.add_array(name,
+                           values_per_cell=indices,
+                           dtype=dtype,
+                           ghost_layers=ghost_layers,
+                           layout=layout,
+                           cpu=cpu,
+                           gpu=gpu,
+                           alignment=alignment,
+                           field_type=field_type)
+
+        return (self.fields[n] for n in names)
+
     @abstractmethod
     def has_data(self, name):
         """Returns true if a field or custom data element with this name was added."""
@@ -151,6 +212,10 @@ class DataHandling(ABC):
         directly passed to the kernel function and override possible parameters from the DataHandling
         """
 
+    @abstractmethod
+    def get_kernel_kwargs(self, kernel_function, **kwargs):
+        """Returns the input arguments of a kernel"""
+
     @abstractmethod
     def swap(self, name1, name2, gpu=False):
         """Swaps data of two arrays"""
@@ -220,7 +285,7 @@ class DataHandling(ABC):
             names: what data to synchronize: name of array or sequence of names
             stencil: stencil as string defining which neighbors are synchronized e.g. 'D2Q9', 'D3Q19'
                      if None, a full synchronization (i.e. D2Q9 or D3Q27) is done
-            target: either 'cpu' or 'gpu
+            target: `Target` either 'CPU' or 'GPU'
             kwargs: implementation specific, optional optimization parameters for communication
 
         Returns:
@@ -266,6 +331,7 @@ class DataHandling(ABC):
                 b[array_name][(Ellipsis, *value_idx)].fill(val)
             else:
                 b[array_name].fill(val)
+            self.to_gpu(array_name)
 
     def min(self, array_name, slice_obj=None, ghost_layers=False, inner_ghost_layers=False, reduce=True):
         """Returns the minimum value inside the domain or slice of the domain.

pystencils/datahandling/parallel_datahandling.py → src/pystencils/datahandling/parallel_datahandling.py

 import os
-import numpy as np
 import warnings
-from pystencils import Field
-from pystencils.datahandling.datahandling_interface import DataHandling
-from pystencils.datahandling.blockiteration import sliced_block_iteration, block_iteration
-from pystencils.kernelparameters import FieldPointerSymbol
-from pystencils.utils import DotDict
+
+import numpy as np
 # noinspection PyPep8Naming
 import waLBerla as wlb
 
+from pystencils.datahandling.blockiteration import block_iteration, sliced_block_iteration
+from pystencils.datahandling.datahandling_interface import DataHandling
+from pystencils.enums import Backend
+from pystencils.field import Field, FieldType
+from pystencils.typing.typed_sympy import FieldPointerSymbol
+from pystencils.utils import DotDict
+from pystencils import Target
+
 
 class ParallelDataHandling(DataHandling):
     GPU_DATA_PREFIX = "gpu_"
     VTK_COUNTER = 0
 
-    def __init__(self, blocks, default_ghost_layers=1, default_layout='SoA', dim=3, default_target='cpu'):
+    def __init__(self, blocks, default_ghost_layers=1, default_layout='SoA', dim=3, default_target=Target.CPU):
         """
         Creates data handling based on walberla block storage
 
@@ -25,18 +29,19 @@ class ParallelDataHandling(DataHandling):
             dim: dimension of scenario,
                  walberla always uses three dimensions, so if dim=2 the extend of the
                  z coordinate of blocks has to be 1
-            default_target: either 'cpu' or 'gpu' . If set to 'gpu' for each array also a GPU version is allocated
-                           if not overwritten in add_array, and synchronization functions are for the GPU by default
+            default_target: `Target`, either 'CPU' or 'GPU' . If set to 'GPU' for each array also a GPU version is
+                            allocated if not overwritten in add_array, and synchronization functions are for the GPU by
+                            default
         """
         super(ParallelDataHandling, self).__init__()
         assert dim in (2, 3)
-        self.blocks = blocks
-        self.default_ghost_layers = default_ghost_layers
-        self.default_layout = default_layout
+        self._blocks = blocks
+        self._default_ghost_layers = default_ghost_layers
+        self._default_layout = default_layout
         self._fields = DotDict()  # maps name to symbolic pystencils field
         self._field_name_to_cpu_data_name = {}
         self._field_name_to_gpu_data_name = {}
-        self.data_names = set()
+        self._data_names = set()
         self._dim = dim
         self._fieldInformation = {}
         self._cpu_gpu_pairs = []
@@ -50,7 +55,11 @@ class ParallelDataHandling(DataHandling):
 
         if self._dim == 2:
             assert self.blocks.getDomainCellBB().size[2] == 1
-        self.default_target = default_target
+        self._default_target = default_target
+
+    @property
+    def default_target(self):
+        return self._default_target
 
     @property
     def dim(self):
@@ -68,6 +77,22 @@ class ParallelDataHandling(DataHandling):
     def fields(self):
         return self._fields
 
+    @property
+    def blocks(self):
+        return self._blocks
+
+    @property
+    def default_ghost_layers(self):
+        return self._default_ghost_layers
+
+    @property
+    def default_layout(self):
+        return self._default_layout
+
+    @property
+    def data_names(self):
+        return self.data_names
+
     def ghost_layers_of_field(self, name):
         return self._fieldInformation[name]['ghost_layers']
 
@@ -88,18 +113,18 @@ class ParallelDataHandling(DataHandling):
         self._custom_data_names.append(name)
 
     def add_array(self, name, values_per_cell=1, dtype=np.float64, latex_name=None, ghost_layers=None,
-                  layout=None, cpu=True, gpu=None, alignment=False):
+                  layout=None, cpu=True, gpu=None, alignment=False, field_type=FieldType.GENERIC):
         if ghost_layers is None:
             ghost_layers = self.default_ghost_layers
         if gpu is None:
-            gpu = self.default_target == 'gpu'
+            gpu = self.default_target == Target.GPU
         if layout is None:
             layout = self.default_layout
         if len(self.blocks) == 0:
             raise ValueError("Data handling expects that each process has at least one block")
         if hasattr(dtype, 'type'):
             dtype = dtype.type
-        if name in self.blocks[0] or self.GPU_DATA_PREFIX + name in self.blocks[0]:
+        if name in self.blocks[0].fieldNames or self.GPU_DATA_PREFIX + name in self.blocks[0].fieldNames:
             raise ValueError("Data with this name has already been added")
 
         if alignment is False or alignment is None:
@@ -107,11 +132,14 @@ class ParallelDataHandling(DataHandling):
         if hasattr(values_per_cell, '__len__'):
             raise NotImplementedError("Parallel data handling does not support multiple index dimensions")
 
-        self._fieldInformation[name] = {'ghost_layers': ghost_layers,
-                                        'values_per_cell': values_per_cell,
-                                        'layout': layout,
-                                        'dtype': dtype,
-                                        'alignment': alignment}
+        self._fieldInformation[name] = {
+            'ghost_layers': ghost_layers,
+            'values_per_cell': values_per_cell,
+            'layout': layout,
+            'dtype': dtype,
+            'alignment': alignment,
+            'field_type': field_type,
+        }
 
         layout_map = {'fzyx': wlb.field.Layout.fzyx, 'zyxf': wlb.field.Layout.zyxf,
                       'f': wlb.field.Layout.fzyx,
@@ -123,8 +151,8 @@ class ParallelDataHandling(DataHandling):
         if gpu:
             if alignment != 0:
                 raise ValueError("Alignment for walberla GPU fields not yet supported")
-            wlb.cuda.addGpuFieldToStorage(self.blocks, self.GPU_DATA_PREFIX + name, dtype, fSize=values_per_cell,
-                                          usePitchedMem=False, ghostLayers=ghost_layers, layout=layout_map[layout])
+            wlb.gpu.addGpuFieldToStorage(self.blocks, self.GPU_DATA_PREFIX + name, dtype, fSize=values_per_cell,
+                                         usePitchedMem=False, ghostLayers=ghost_layers, layout=layout_map[layout])
 
         if cpu and gpu:
             self._cpu_gpu_pairs.append((name, self.GPU_DATA_PREFIX + name))
@@ -138,7 +166,8 @@ class ParallelDataHandling(DataHandling):
         assert all(f.name != name for f in self.fields.values()), "Symbolic field with this name already exists"
 
         self.fields[name] = Field.create_generic(name, self.dim, dtype, index_dimensions, layout,
-                                                 index_shape=(values_per_cell,) if index_dimensions > 0 else None)
+                                                 index_shape=(values_per_cell,) if index_dimensions > 0 else None,
+                                                 field_type=field_type)
         self.fields[name].latex_name = latex_name
         self._field_name_to_cpu_data_name[name] = name
         if gpu:
@@ -209,15 +238,13 @@ class ParallelDataHandling(DataHandling):
             array = array[:, :, 0]
         if last_element and self.fields[name].index_dimensions > 0:
             array = array[..., last_element[0]]
-        if self.fields[name].index_dimensions == 0:
-            array = array[..., 0]
 
         return array
 
     def _normalize_arr_shape(self, arr, index_dimensions):
-        if index_dimensions == 0:
+        if index_dimensions == 0 and len(arr.shape) > 3:
             arr = arr[..., 0]
-        if self.dim == 2:
+        if self.dim == 2 and len(arr.shape) > 2:
             arr = arr[:, :, 0]
         return arr
 
@@ -226,9 +253,9 @@ class ParallelDataHandling(DataHandling):
             kernel_function(**arg_dict)
 
     def get_kernel_kwargs(self, kernel_function, **kwargs):
-        if kernel_function.ast.backend == 'gpucuda':
+        if kernel_function.ast.backend == Backend.CUDA:
             name_map = self._field_name_to_gpu_data_name
-            to_array = wlb.cuda.toGpuArray
+            to_array = wlb.gpu.toGpuArray
         else:
             name_map = self._field_name_to_cpu_data_name
             to_array = wlb.field.toArray
@@ -240,7 +267,7 @@ class ParallelDataHandling(DataHandling):
         for block in self.blocks:
             field_args = {}
             for data_name, f in data_used_in_kernel:
-                arr = to_array(block[data_name], withGhostLayers=[True, True, self.dim == 3])
+                arr = to_array(block[data_name], with_ghost_layers=[True, True, self.dim == 3])
                 arr = self._normalize_arr_shape(arr, f.index_dimensions)
                 field_args[f.name] = arr
             field_args.update(kwargs)
@@ -253,7 +280,8 @@ class ParallelDataHandling(DataHandling):
             for block in self.blocks:
                 transfer_func(block[self.GPU_DATA_PREFIX + name], block[name])
         else:
-            wlb.cuda.copyFieldToCpu(self.blocks, self.GPU_DATA_PREFIX + name, name)
+            if self.is_on_gpu(name):
+                wlb.gpu.copyFieldToCpu(self.blocks, self.GPU_DATA_PREFIX + name, name)
 
     def to_gpu(self, name):
         if name in self._custom_data_transfer_functions:
@@ -261,28 +289,29 @@ class ParallelDataHandling(DataHandling):
             for block in self.blocks:
                 transfer_func(block[self.GPU_DATA_PREFIX + name], block[name])
         else:
-            wlb.cuda.copyFieldToGpu(self.blocks, self.GPU_DATA_PREFIX + name, name)
+            if self.is_on_gpu(name):
+                wlb.gpu.copyFieldToGpu(self.blocks, self.GPU_DATA_PREFIX + name, name)
 
     def is_on_gpu(self, name):
         return (name, self.GPU_DATA_PREFIX + name) in self._cpu_gpu_pairs
 
     def all_to_cpu(self):
         for cpu_name, gpu_name in self._cpu_gpu_pairs:
-            wlb.cuda.copyFieldToCpu(self.blocks, gpu_name, cpu_name)
+            wlb.gpu.copyFieldToCpu(self.blocks, gpu_name, cpu_name)
         for name in self._custom_data_transfer_functions.keys():
             self.to_cpu(name)
 
     def all_to_gpu(self):
         for cpu_name, gpu_name in self._cpu_gpu_pairs:
-            wlb.cuda.copyFieldToGpu(self.blocks, gpu_name, cpu_name)
+            wlb.gpu.copyFieldToGpu(self.blocks, gpu_name, cpu_name)
         for name in self._custom_data_transfer_functions.keys():
             self.to_gpu(name)
 
     def synchronization_function_cpu(self, names, stencil=None, buffered=True, stencil_restricted=False, **_):
-        return self.synchronization_function(names, stencil, 'cpu', buffered, stencil_restricted)
+        return self.synchronization_function(names, stencil, Target.CPU, buffered, stencil_restricted)
 
     def synchronization_function_gpu(self, names, stencil=None, buffered=True, stencil_restricted=False, **_):
-        return self.synchronization_function(names, stencil, 'gpu', buffered, stencil_restricted)
+        return self.synchronization_function(names, stencil, Target.GPU, buffered, stencil_restricted)
 
     def synchronization_function(self, names, stencil=None, target=None, buffered=True, stencil_restricted=False):
         if target is None:
@@ -295,13 +324,13 @@ class ParallelDataHandling(DataHandling):
             names = [names]
 
         create_scheme = wlb.createUniformBufferedScheme if buffered else wlb.createUniformDirectScheme
-        if target == 'cpu':
+        if target == Target.CPU:
             create_packing = wlb.field.createPackInfo if buffered else wlb.field.createMPIDatatypeInfo
-            if not buffered and stencil_restricted:
+            if buffered and stencil_restricted:
                 create_packing = wlb.field.createStencilRestrictedPackInfo
         else:
-            assert target == 'gpu'
-            create_packing = wlb.cuda.createPackInfo if buffered else wlb.cuda.createMPIDatatypeInfo
+            assert target == Target.GPU
+            create_packing = wlb.gpu.createPackInfo if buffered else wlb.gpu.createMPIDatatypeInfo
             names = [self.GPU_DATA_PREFIX + name for name in names]
 
         sync_function = create_scheme(self.blocks, stencil)
@@ -377,7 +406,7 @@ class ParallelDataHandling(DataHandling):
         if not os.path.exists(directory):
             os.mkdir(directory)
         if os.path.isfile(directory):
-            raise RuntimeError("Trying to save to {}, but file exists already".format(directory))
+            raise RuntimeError(f"Trying to save to {directory}, but file exists already")
 
         for field_name, data_name in self._field_name_to_cpu_data_name.items():
             self.blocks.writeBlockData(data_name, os.path.join(directory, field_name + ".dat"))

pystencils/datahandling/serial_datahandling.py → src/pystencils/datahandling/serial_datahandling.py

 import itertools
+import time
 from typing import Sequence, Union
+
 import numpy as np
-import time
-from pystencils import Field
-from pystencils.datahandling.datahandling_interface import DataHandling
-from pystencils.field import layout_string_to_tuple, spatial_layout_string_to_tuple, create_numpy_array_with_layout
+
 from pystencils.datahandling.blockiteration import SerialBlock
+from pystencils.datahandling.datahandling_interface import DataHandling
+from pystencils.enums import Target
+from pystencils.field import (Field, FieldType, create_numpy_array_with_layout,
+                              layout_string_to_tuple, spatial_layout_string_to_tuple)
+from pystencils.gpu.gpu_array_handler import GPUArrayHandler, GPUNotAvailableHandler
 from pystencils.slicing import normalize_slice, remove_ghost_layers
 from pystencils.utils import DotDict
 
-try:
-    import pycuda.gpuarray as gpuarray
-    import pycuda.autoinit  # NOQA
-except ImportError:
-    gpuarray = None
-
 
 class SerialDataHandling(DataHandling):
 
-    def __init__(self, domain_size: Sequence[int], default_ghost_layers: int = 1, default_layout: str = 'SoA',
-                 periodicity: Union[bool, Sequence[bool]] = False, default_target: str = 'cpu') -> None:
+    def __init__(self,
+                 domain_size: Sequence[int],
+                 default_ghost_layers: int = 1,
+                 default_layout: str = 'SoA',
+                 periodicity: Union[bool, Sequence[bool]] = False,
+                 default_target: Target = Target.CPU,
+                 array_handler=None,
+                 device_number=None) -> None:
         """
         Creates a data handling for single node simulations.
 
@@ -27,8 +31,17 @@ class SerialDataHandling(DataHandling):
             domain_size: size of the spatial domain as tuple
             default_ghost_layers: default number of ghost layers used, if not overridden in add_array() method
             default_layout: default layout used, if  not overridden in add_array() method
-            default_target: either 'cpu' or 'gpu' . If set to 'gpu' for each array also a GPU version is allocated
-                            if not overwritten in add_array, and synchronization functions are for the GPU by default
+            periodicity: List of booleans that indicate which dimensions have periodic boundary conditions.
+                         Alternatively, a single boolean can be given, which is used for all dimensions. Defaults to
+                         False (non-periodic)
+            default_target: `Target` either 'CPU' or 'GPU'. If set to 'GPU' for each array also a GPU version is
+                            allocated if not overwritten in add_array, and synchronization functions are for the GPU by
+                            default
+            array_handler: An object that provides the same interface as `GPUArrayHandler`, which is used for creation
+                           and transferring of GPU arrays. Default is to construct a fresh `GPUArrayHandler`
+            device_number: If `default_target` is set to 'GPU', a device number should be specified. If none is given,
+                           the device with the largest amount of memory is used. If multiple devices have the same
+                           amount of memory, the one with the lower number is used
         """
         super(SerialDataHandling, self).__init__()
         self._domainSize = tuple(domain_size)
@@ -41,6 +54,19 @@ class SerialDataHandling(DataHandling):
         self.custom_data_gpu = DotDict()
         self._custom_data_transfer_functions = {}
 
+        if not array_handler:
+            try:
+                if device_number is None:
+                    import cupy.cuda.runtime
+                    if cupy.cuda.runtime.getDeviceCount() > 0:
+                        device_number = sorted(range(cupy.cuda.runtime.getDeviceCount()),
+                                               key=lambda i: cupy.cuda.Device(i).mem_info[1], reverse=True)[0]
+                self.array_handler = GPUArrayHandler(device_number)
+            except ImportError:
+                self.array_handler = GPUNotAvailableHandler()
+        else:
+            self.array_handler = array_handler
+
         if periodicity is None or periodicity is False:
             periodicity = [False] * self.dim
         if periodicity is True:
@@ -48,9 +74,13 @@ class SerialDataHandling(DataHandling):
 
         self._periodicity = periodicity
         self._field_information = {}
-        self.default_target = default_target
+        self._default_target = default_target
         self._start_time = time.perf_counter()
 
+    @property
+    def default_target(self):
+        return self._default_target
+
     @property
     def dim(self):
         return len(self._domainSize)
@@ -74,13 +104,13 @@ class SerialDataHandling(DataHandling):
         return self._field_information[name]['values_per_cell']
 
     def add_array(self, name, values_per_cell=1, dtype=np.float64, latex_name=None, ghost_layers=None, layout=None,
-                  cpu=True, gpu=None, alignment=False):
+                  cpu=True, gpu=None, alignment=False, field_type=FieldType.GENERIC):
         if ghost_layers is None:
             ghost_layers = self.default_ghost_layers
         if layout is None:
             layout = self.default_layout
         if gpu is None:
-            gpu = self.default_target == 'gpu'
+            gpu = self.default_target in self._GPU_LIKE_TARGETS
 
         kwargs = {
             'shape': tuple(s + 2 * ghost_layers for s in self._domainSize),
@@ -88,7 +118,7 @@ class SerialDataHandling(DataHandling):
         }
 
         if not hasattr(values_per_cell, '__len__'):
-            values_per_cell = (values_per_cell, )
+            values_per_cell = (values_per_cell,)
         if len(values_per_cell) == 1 and values_per_cell[0] == 1:
             values_per_cell = ()
 
@@ -98,6 +128,7 @@ class SerialDataHandling(DataHandling):
             'layout': layout,
             'dtype': dtype,
             'alignment': alignment,
+            'field_type': field_type,
         }
 
         index_dimensions = len(values_per_cell)
@@ -108,10 +139,14 @@ class SerialDataHandling(DataHandling):
         else:
             layout_tuple = spatial_layout_string_to_tuple(layout, self.dim)
 
-        # cpu_arr is always created - since there is no create_pycuda_array_with_layout()
+        # cpu_arr is always created - since there is no create_gpu_array_with_layout()
         byte_offset = ghost_layers * np.dtype(dtype).itemsize
-        cpu_arr = create_numpy_array_with_layout(layout=layout_tuple, alignment=alignment,
-                                                 byte_offset=byte_offset, **kwargs)
+
+        if gpu:
+            cpu_arr = self.array_handler.pinned_numpy_array(shape=kwargs['shape'], layout=layout_tuple, dtype=dtype)
+        else:
+            cpu_arr = create_numpy_array_with_layout(layout=layout_tuple, alignment=alignment,
+                                                     byte_offset=byte_offset, **kwargs)
 
         if alignment and gpu:
             raise NotImplementedError("Alignment for GPU fields not supported")
@@ -123,10 +158,11 @@ class SerialDataHandling(DataHandling):
         if gpu:
             if name in self.gpu_arrays:
                 raise ValueError("GPU Field with this name already exists")
-            self.gpu_arrays[name] = gpuarray.to_gpu(cpu_arr)
+            self.gpu_arrays[name] = self.array_handler.to_gpu(cpu_arr)
 
         assert all(f.name != name for f in self.fields.values()), "Symbolic field with this name already exists"
-        self.fields[name] = Field.create_from_numpy_array(name, cpu_arr, index_dimensions=index_dimensions)
+        self.fields[name] = Field.create_from_numpy_array(name, cpu_arr, index_dimensions=index_dimensions,
+                                                          field_type=field_type)
         self.fields[name].latex_name = latex_name
         return self.fields[name]
 
@@ -205,7 +241,7 @@ class SerialDataHandling(DataHandling):
 
     def swap(self, name1, name2, gpu=None):
         if gpu is None:
-            gpu = self.default_target == "gpu"
+            gpu = self.default_target in self._GPU_LIKE_TARGETS
         arr = self.gpu_arrays if gpu else self.cpu_arrays
         arr[name1], arr[name2] = arr[name2], arr[name1]
 
@@ -218,12 +254,12 @@ class SerialDataHandling(DataHandling):
             self.to_gpu(name)
 
     def run_kernel(self, kernel_function, **kwargs):
-        arrays = self.gpu_arrays if kernel_function.ast.backend == 'gpucuda' else self.cpu_arrays
-        kernel_function(**arrays, **kwargs)
+        arrays = self.gpu_arrays if kernel_function.ast.backend in self._GPU_LIKE_BACKENDS else self.cpu_arrays
+        kernel_function(**{**arrays, **kwargs})
 
     def get_kernel_kwargs(self, kernel_function, **kwargs):
         result = {}
-        result.update(self.gpu_arrays if kernel_function.ast.backend == 'gpucuda' else self.cpu_arrays)
+        result.update(self.gpu_arrays if kernel_function.ast.backend in self._GPU_LIKE_BACKENDS else self.cpu_arrays)
         result.update(kwargs)
         return [result]
 
@@ -232,28 +268,30 @@ class SerialDataHandling(DataHandling):
             transfer_func = self._custom_data_transfer_functions[name][1]
             transfer_func(self.custom_data_gpu[name], self.custom_data_cpu[name])
         else:
-            self.gpu_arrays[name].get(self.cpu_arrays[name])
+            if name in self.cpu_arrays.keys() & self.gpu_arrays.keys():
+                self.array_handler.download(self.gpu_arrays[name], self.cpu_arrays[name])
 
     def to_gpu(self, name):
         if name in self._custom_data_transfer_functions:
             transfer_func = self._custom_data_transfer_functions[name][0]
             transfer_func(self.custom_data_gpu[name], self.custom_data_cpu[name])
         else:
-            self.gpu_arrays[name].set(self.cpu_arrays[name])
+            if name in self.cpu_arrays.keys() & self.gpu_arrays.keys():
+                self.array_handler.upload(self.gpu_arrays[name], self.cpu_arrays[name])
 
     def is_on_gpu(self, name):
         return name in self.gpu_arrays
 
     def synchronization_function_cpu(self, names, stencil_name=None, **_):
-        return self.synchronization_function(names, stencil_name, 'cpu')
+        return self.synchronization_function(names, stencil_name, target=Target.CPU)
 
     def synchronization_function_gpu(self, names, stencil_name=None, **_):
-        return self.synchronization_function(names, stencil_name, 'gpu')
+        return self.synchronization_function(names, stencil_name, target=Target.GPU)
 
-    def synchronization_function(self, names, stencil=None, target=None, **_):
+    def synchronization_function(self, names, stencil=None, target=None, functor=None, **_):
         if target is None:
             target = self.default_target
-        assert target in ('cpu', 'gpu')
+        assert target in (Target.CPU, Target.GPU)
         if not hasattr(names, '__len__') or type(names) is str:
             names = [names]
 
@@ -282,25 +320,28 @@ class SerialDataHandling(DataHandling):
             gls = self._field_information[name]['ghost_layers']
             values_per_cell = self._field_information[name]['values_per_cell']
             if values_per_cell == ():
-                values_per_cell = (1, )
+                values_per_cell = (1,)
             if len(values_per_cell) == 1:
                 values_per_cell = values_per_cell[0]
-            else:
-                raise NotImplementedError("Synchronization of this field is not supported: " + name)
 
             if len(filtered_stencil) > 0:
-                if target == 'cpu':
-                    from pystencils.slicing import get_periodic_boundary_functor
-                    result.append(get_periodic_boundary_functor(filtered_stencil, ghost_layers=gls))
+                if target == Target.CPU:
+                    if functor is None:
+                        from pystencils.slicing import get_periodic_boundary_functor
+                        functor = get_periodic_boundary_functor
+                    result.append(functor(filtered_stencil, ghost_layers=gls))
                 else:
-                    from pystencils.gpucuda.periodicity import get_periodic_boundary_functor as boundary_func
-                    result.append(boundary_func(filtered_stencil, self._domainSize,
-                                                index_dimensions=self.fields[name].index_dimensions,
-                                                index_dim_shape=values_per_cell,
-                                                dtype=self.fields[name].dtype.numpy_dtype,
-                                                ghost_layers=gls))
-
-        if target == 'cpu':
+                    if functor is None:
+                        from pystencils.gpu.periodicity import get_periodic_boundary_functor as functor
+                        target = Target.GPU
+                    result.append(functor(filtered_stencil, self._domainSize,
+                                          index_dimensions=self.fields[name].index_dimensions,
+                                          index_dim_shape=values_per_cell,
+                                          dtype=self.fields[name].dtype.numpy_dtype,
+                                          ghost_layers=gls,
+                                          target=target))
+
+        if target == Target.CPU:
             def result_functor():
                 for arr_name, func in zip(names, result):
                     func(pdfs=self.cpu_arrays[arr_name])
@@ -351,6 +392,7 @@ class SerialDataHandling(DataHandling):
                     raise NotImplementedError("VTK export for fields with more than one index "
                                               "coordinate not implemented")
             image_to_vtk(full_file_name, cell_data=cell_data)
+
         return writer
 
     def create_vtk_writer_for_flag_array(self, file_name, data_name, masks_to_name, ghost_layers=False):
@@ -382,7 +424,7 @@ class SerialDataHandling(DataHandling):
 
         time_running = time.perf_counter() - self._start_time
         spacing = 7 - len(str(int(time_running)))
-        message = "[{: <8}]{}({:.3f} sec) {} ".format(level, spacing * '-', time_running, message)
+        message = f"[{level: <8}]{spacing * '-'}({time_running:.3f} sec) {message} "
         print(message, flush=True)
 
     def log_on_root(self, *args, level='INFO'):
@@ -396,18 +438,28 @@ class SerialDataHandling(DataHandling):
     def world_rank(self):
         return 0
 
-    def save_all(self, file):
-        np.savez_compressed(file, **self.cpu_arrays)
+    def save_all(self, filename, compressed=True, synchronise_data=True):
+        if synchronise_data:
+            for name in (self.cpu_arrays.keys() & self.gpu_arrays.keys()):
+                self.to_cpu(name)
+        if compressed:
+            np.savez_compressed(filename, **self.cpu_arrays)
+        else:
+            np.savez(filename, **self.cpu_arrays)
 
-    def load_all(self, file):
-        file_contents = np.load(file)
+    def load_all(self, filename, synchronise_data=True):
+        if '.npz' not in filename:
+            filename += '.npz'
+        file_contents = np.load(filename)
         for arr_name, arr_contents in self.cpu_arrays.items():
             if arr_name not in file_contents:
-                print("Skipping read data {} because there is no data with this name in data handling".format(arr_name))
+                print(f"Skipping read data {arr_name} because there is no data with this name in data handling")
                 continue
             if file_contents[arr_name].shape != arr_contents.shape:
-                print("Skipping read data {} because shapes don't match. "
-                      "Read array shape {}, existing array shape {}".format(arr_name, file_contents[arr_name].shape,
-                                                                            arr_contents.shape))
+                print(f"Skipping read data {arr_name} because shapes don't match. "
+                      f"Read array shape {file_contents[arr_name].shape}, existing array shape {arr_contents.shape}")
                 continue
             np.copyto(arr_contents, file_contents[arr_name])
+            if synchronise_data:
+                if arr_name in self.gpu_arrays.keys():
+                    self.to_gpu(arr_name)

pystencils/datahandling/vtk.py → src/pystencils/datahandling/vtk.py

-from pyevtk.vtk import VtkFile, VtkImageData
 from pyevtk.hl import _addDataToFile, _appendDataToFile
+from pyevtk.vtk import VtkFile, VtkImageData
 
 
 def image_to_vtk(path, cell_data, origin=(0.0, 0.0, 0.0), spacing=(1.0, 1.0, 1.0)):

pystencils/display_utils.py → src/pystencils/display_utils.py

+from typing import Any, Dict, Optional, Union
+
 import sympy as sp
-from typing import Any, Dict, Optional
+
 from pystencils.astnodes import KernelFunction
+from pystencils.enums import Backend
+from pystencils.kernel_wrapper import KernelWrapper
 
 
 def to_dot(expr: sp.Expr, graph_style: Optional[Dict[str, Any]] = None, short=True):
     """Show a sympy or pystencils AST as dot graph"""
     from pystencils.astnodes import Node
-    import graphviz
+    try:
+        import graphviz
+    except ImportError:
+        print("graphviz is not installed. Visualizing the AST is not available")
+        return
+
     graph_style = {} if graph_style is None else graph_style
 
     if isinstance(expr, Node):
@@ -27,29 +36,69 @@ def highlight_cpp(code: str):
     from pygments.lexers import CppLexer
 
     css = HtmlFormatter().get_style_defs('.highlight')
-    css_tag = "<style>{css}</style>".format(css=css)
+    css_tag = f"<style>{css}</style>"
     display(HTML(css_tag))
     return HTML(highlight(code, CppLexer(), HtmlFormatter()))
 
 
-def show_code(ast: KernelFunction):
+def get_code_obj(ast: Union[KernelFunction, KernelWrapper], custom_backend=None):
     """Returns an object to display generated code (C/C++ or CUDA)
 
-    Can either  be displayed as HTML in Jupyter notebooks or printed as normal string.
+    Can either be displayed as HTML in Jupyter notebooks or printed as normal string.
     """
     from pystencils.backends.cbackend import generate_c
-    dialect = 'cuda' if ast.backend == 'gpucuda' else 'c'
+
+    if isinstance(ast, KernelWrapper):
+        ast = ast.ast
+
+    if ast.backend not in {Backend.C, Backend.CUDA}:
+        raise NotImplementedError(f'get_code_obj is not implemented for backend {ast.backend}')
+    dialect = ast.backend
 
     class CodeDisplay:
         def __init__(self, ast_input):
             self.ast = ast_input
 
         def _repr_html_(self):
-            return highlight_cpp(generate_c(self.ast, dialect=dialect)).__html__()
+            return highlight_cpp(generate_c(self.ast, dialect=dialect, custom_backend=custom_backend)).__html__()
 
         def __str__(self):
-            return generate_c(self.ast, dialect=dialect)
+            return generate_c(self.ast, dialect=dialect, custom_backend=custom_backend)
 
         def __repr__(self):
-            return generate_c(self.ast, dialect=dialect)
+            return generate_c(self.ast, dialect=dialect, custom_backend=custom_backend)
     return CodeDisplay(ast)
+
+
+def get_code_str(ast, custom_backend=None):
+    return str(get_code_obj(ast, custom_backend))
+
+
+def _isnotebook():
+    try:
+        shell = get_ipython().__class__.__name__
+        if shell == 'ZMQInteractiveShell':
+            return True   # Jupyter notebook or qtconsole
+        elif shell == 'TerminalInteractiveShell':
+            return False  # Terminal running IPython
+        else:
+            return False  # Other type (?)
+    except NameError:
+        return False
+
+
+def show_code(ast: Union[KernelFunction, KernelWrapper], custom_backend=None):
+    code = get_code_obj(ast, custom_backend)
+
+    if _isnotebook():
+        from IPython.display import display
+        display(code)
+    else:
+        try:
+            import rich.syntax
+            import rich.console
+            syntax = rich.syntax.Syntax(str(code), "c++", theme="monokai", line_numbers=True)
+            console = rich.console.Console()
+            console.print(syntax)
+        except ImportError:
+            print(code)

src/pystencils/enums.py

+from enum import Enum, auto
+
+
+class Target(Enum):
+    """
+    The Target enumeration represents all possible targets that can be used for the code generation.
+    """
+    CPU = auto()
+    """
+    Target CPU architecture.
+    """
+    GPU = auto()
+    """
+    Target GPU architecture.
+    """
+
+
+class Backend(Enum):
+    """
+    The Backend enumeration represents all possible backends that can be used for the code generation.
+    Backends and targets must be combined with care. For example CPU as a target and CUDA as a backend makes no sense.
+    """
+    C = auto()
+    """
+    Use the C Backend of pystencils.
+    """
+    CUDA = auto()
+    """
+    Use the CUDA backend to generate code for NVIDIA GPUs.
+    """

pystencils/fast_approximation.py → src/pystencils/fast_approximation.py

-import sympy as sp
 from typing import List, Union
 
+import sympy as sp
+
 from pystencils.astnodes import Node
 from pystencils.simp import AssignmentCollection
+from pystencils.assignment import Assignment
 
 
 # noinspection PyPep8Naming
 class fast_division(sp.Function):
+    """
+    Produces special float instructions for CUDA kernels
+    """
     nargs = (2,)
 
 
 # noinspection PyPep8Naming
 class fast_sqrt(sp.Function):
+    """
+    Produces special float instructions for CUDA kernels
+    """
     nargs = (1, )
 
 
 # noinspection PyPep8Naming
 class fast_inv_sqrt(sp.Function):
+    """
+    Produces special float instructions for CUDA kernels
+    """
     nargs = (1, )
 
 
@@ -31,7 +42,7 @@ def _run(term, visitor):
         return visitor(term)
 
 
-def insert_fast_sqrts(term: Union[sp.Expr, List[sp.Expr], AssignmentCollection]):
+def insert_fast_sqrts(term: Union[sp.Expr, List[sp.Expr], AssignmentCollection, Assignment]):
     def visit(expr):
         if isinstance(expr, Node):
             return expr
@@ -47,7 +58,7 @@ def insert_fast_sqrts(term: Union[sp.Expr, List[sp.Expr], AssignmentCollection])
     return _run(term, visit)
 
 
-def insert_fast_divisions(term: Union[sp.Expr, List[sp.Expr], AssignmentCollection]):
+def insert_fast_divisions(term: Union[sp.Expr, List[sp.Expr], AssignmentCollection, Assignment]):
 
     def visit(expr):
         if isinstance(expr, Node):

pystencils/fd/__init__.py → src/pystencils/fd/__init__.py

-from .derivative import Diff, DiffOperator, \
-    diff_terms, collect_diffs, zero_diffs, evaluate_diffs, normalize_diff_order, \
-    expand_diff_full, expand_diff_linear, expand_diff_products, combine_diff_products, \
-    functional_derivative, diff
-from .finitedifferences import advection, diffusion, transient, Discretization2ndOrder
+from .derivative import (
+    Diff, DiffOperator, collect_diffs, combine_diff_products, diff, diff_terms, evaluate_diffs,
+    expand_diff_full, expand_diff_linear, expand_diff_products, functional_derivative,
+    normalize_diff_order, zero_diffs)
+from .finitedifferences import Discretization2ndOrder, advection, diffusion, transient
+from .finitevolumes import FVM1stOrder, VOF
 from .spatial import discretize_spatial, discretize_spatial_staggered
 
 __all__ = ['Diff', 'diff', 'DiffOperator', 'diff_terms', 'collect_diffs',
            'zero_diffs', 'evaluate_diffs', 'normalize_diff_order', 'expand_diff_full', 'expand_diff_linear',
            'expand_diff_products', 'combine_diff_products', 'functional_derivative',
            'advection', 'diffusion', 'transient', 'Discretization2ndOrder', 'discretize_spatial',
-           'discretize_spatial_staggered']
+           'discretize_spatial_staggered', 'FVM1stOrder', 'VOF']

pystencils/fd/derivation.py → src/pystencils/fd/derivation.py

-import sympy as sp
+import itertools
 from collections import defaultdict
+
+import numpy as np
+import sympy as sp
+
 from pystencils.field import Field
-from pystencils.sympyextensions import prod, multidimensional_sum
-from pystencils.utils import fully_contains, LinearEquationSystem
+from pystencils.stencil import direction_string_to_offset
+from pystencils.sympyextensions import multidimensional_sum, prod
+from pystencils.utils import LinearEquationSystem, fully_contains
 
 
 class FiniteDifferenceStencilDerivation:
@@ -102,7 +107,7 @@ class FiniteDifferenceStencilDerivation:
     @staticmethod
     def symbolic_weight(*args):
         str_args = [str(e) for e in args]
-        return sp.Symbol("w_({})".format(",".join(str_args)))
+        return sp.Symbol(f"w_({','.join(str_args)})")
 
     def error_term_dict(self, order):
         error_terms = defaultdict(lambda: 0)
@@ -121,7 +126,6 @@ class FiniteDifferenceStencilDerivation:
 
     def isotropy_equations(self, order):
         def cycle_int_sequence(sequence, modulus):
-            import numpy as np
             result = []
             arr = np.array(sequence, dtype=int)
             while True:
@@ -166,15 +170,168 @@ class FiniteDifferenceStencilDerivation:
             f = field_access
             return sum(f.get_shifted(*offset) * weight for offset, weight in zip(self.stencil, self.weights))
 
-        def as_matrix(self):
+        def __array__(self):
+            return np.array(self.as_array().tolist())
+
+        def as_array(self):
             dim = len(self.stencil[0])
-            assert dim == 2
+            assert (dim == 2 or dim == 3), "Only 2D or 3D matrix representations are available"
             max_offset = max(max(abs(e) for e in direction) for direction in self.stencil)
-            result = sp.Matrix(2 * max_offset + 1, 2 * max_offset + 1, lambda i, j: 0)
-            for direction, weight in zip(self.stencil, self.weights):
-                result[max_offset - direction[1], max_offset + direction[0]] = weight
+            shape_list = []
+            for i in range(dim):
+                shape_list.append(2 * max_offset + 1)
+
+            number_of_elements = np.prod(shape_list)
+            shape = tuple(shape_list)
+            result = sp.MutableDenseNDimArray([0] * number_of_elements, shape)
+
+            if dim == 2:
+                for direction, weight in zip(self.stencil, self.weights):
+                    result[max_offset - direction[1], max_offset + direction[0]] = weight
+            if dim == 3:
+                for direction, weight in zip(self.stencil, self.weights):
+                    result[max_offset - direction[1], max_offset + direction[0], max_offset + direction[2]] = weight
+
             return result
 
+        def rotate_weights_and_apply(self, field_access: Field.Access, axes):
+            """derive gradient weights of other direction with already calculated weights of one direction
+               via rotation and apply them to a field."""
+            dim = len(self.stencil[0])
+            assert (dim == 2 or dim == 3), "This function is only for 2D or 3D stencils available"
+            rotated_weights = np.rot90(np.array(self.__array__()), 1, axes)
+
+            result = []
+            max_offset = max(max(abs(e) for e in direction) for direction in self.stencil)
+            if dim == 2:
+                for direction in self.stencil:
+                    result.append(rotated_weights[max_offset - direction[1],
+                                                  max_offset + direction[0]])
+            if dim == 3:
+                for direction in self.stencil:
+                    result.append(rotated_weights[max_offset - direction[1],
+                                                  max_offset + direction[0],
+                                                  max_offset + direction[2]])
+
+            f = field_access
+            return sum(f.get_shifted(*offset) * weight for offset, weight in zip(self.stencil, result))
+
         def __repr__(self):
             return "Finite difference stencil of accuracy {}, isotropic error: {}".format(self.accuracy,
                                                                                           self.is_isotropic)
+
+
+class FiniteDifferenceStaggeredStencilDerivation:
+    """Derives a finite difference stencil for application at a staggered position
+
+    Args:
+        neighbor: the neighbor direction string or vector at whose staggered position to calculate the derivative
+        dim: how many dimensions (2 or 3)
+        derivative: a tuple of directions over which to perform derivatives
+        free_weights_prefix: a string to prefix to free weight symbols. If None, do not return free weights
+    """
+
+    def __init__(self, neighbor, dim, derivative=tuple(), free_weights_prefix=None):
+        if type(neighbor) is str:
+            neighbor = direction_string_to_offset(neighbor)
+        if dim == 2:
+            assert neighbor[dim:] == 0
+        assert derivative is tuple() or max(derivative) < dim
+        neighbor = sp.Matrix(neighbor[:dim])
+        pos = neighbor / 2
+
+        def unitvec(i):
+            """return the `i`-th unit vector in three dimensions"""
+            a = np.zeros(dim, dtype=int)
+            a[i] = 1
+            return a
+
+        def flipped(a, i):
+            """return `a` with its `i`-th element's sign flipped"""
+            a = a.copy()
+            a[i] *= -1
+            return a
+
+        # determine the points to use, coordinates are relative to position
+        points = []
+        if np.linalg.norm(neighbor, 1) == 1:
+            main_points = [neighbor / 2, neighbor / -2]
+        elif np.linalg.norm(neighbor, 1) == 2:
+            nonzero_indices = [i for i, v in enumerate(neighbor) if v != 0 and i < dim]
+            main_points = [neighbor / 2, neighbor / -2, flipped(neighbor / 2, nonzero_indices[0]),
+                           flipped(neighbor / -2, nonzero_indices[0])]
+        else:
+            main_points = [sp.Matrix(np.multiply(neighbor, sp.Matrix(c) / 2))
+                           for c in itertools.product([-1, 1], repeat=3)]
+        points += main_points
+        zero_indices = [i for i, v in enumerate(neighbor) if v == 0 and i < dim]
+        for i in zero_indices:
+            points += [point + sp.Matrix(unitvec(i)) for point in main_points]
+            points += [point - sp.Matrix(unitvec(i)) for point in main_points]
+        points_tuple = tuple([tuple(p) for p in points])
+        self._stencil = points_tuple
+
+        # determine the stencil weights
+        if len(derivative) == 0:
+            weights = None
+        else:
+            derivation = FiniteDifferenceStencilDerivation(derivative, points_tuple).get_stencil()
+            if not derivation.accuracy:
+                raise Exception('the requested derivative cannot be performed with the available neighbors')
+            weights = derivation.weights
+
+            # if the weights are underdefined, we can choose the free symbols to find the sparsest stencil
+            free_weights = set(itertools.chain(*[w.free_symbols for w in weights]))
+            if free_weights_prefix is not None:
+                weights = [w.subs({fw: sp.Symbol(f"{free_weights_prefix}_{i}") for i, fw in enumerate(free_weights)})
+                           for w in weights]
+            elif len(free_weights) > 0:
+                zero_counts = defaultdict(list)
+                for values in itertools.product([-1, -sp.Rational(1, 2), 0, 1, sp.Rational(1, 2)],
+                                                repeat=len(free_weights)):
+                    subs = {free_weight: value for free_weight, value in zip(free_weights, values)}
+                    weights = [w.subs(subs) for w in derivation.weights]
+                    if not all(a == 0 for a in weights):
+                        zero_count = sum([1 for w in weights if w == 0])
+                        zero_counts[zero_count].append(weights)
+                best = zero_counts[max(zero_counts.keys())]
+                if len(best) > 1:  # if there are multiple, pick the one that contains a nonzero center weight
+                    center = [tuple(p + pos) for p in points].index((0, 0, 0)[:dim])
+                    best = [b for b in best if b[center] != 0]
+                if len(best) > 1:  # if there are still multiple, they are equivalent, so we average
+                    weights = [sum([b[i] for b in best]) / len(best) for i in range(len(weights))]
+                else:
+                    weights = best[0]
+                assert weights
+
+        points_tuple = tuple([tuple(p + pos) for p in points])
+        self._points = points_tuple
+        self._weights = weights
+
+    @property
+    def points(self):
+        """return the points of the stencil"""
+        return self._points
+
+    @property
+    def stencil(self):
+        """return the points of the stencil relative to the staggered position specified by neighbor"""
+        return self._stencil
+
+    @property
+    def weights(self):
+        """return the weights of the stencil"""
+        assert self._weights is not None
+        return self._weights
+
+    def visualize(self):
+        if self._weights is None:
+            ws = None
+        else:
+            ws = np.array([w for w in self.weights if w != 0], dtype=float)
+        pts = np.array([p for i, p in enumerate(self.points) if self.weights[i] != 0], dtype=int)
+        from pystencils.stencil import plot
+        plot(pts, data=ws)
+
+    def apply(self, access: Field.Access):
+        return sum([access.get_shifted(*point) * weight for point, weight in zip(self.points, self.weights)])

pystencils/fd/derivative.py → src/pystencils/fd/derivative.py

+from collections import defaultdict, namedtuple
+
 import sympy as sp
-from collections import namedtuple, defaultdict
-from pystencils import Field
+
+from pystencils.field import Field
 from pystencils.sympyextensions import normalize_product, prod
 
 
@@ -107,7 +109,17 @@ class Diff(sp.Expr):
         return result
 
     def __str__(self):
-        return "D(%s)" % self.arg
+        return f"D({self.arg})"
+
+    def interpolated_access(self, offset, **kwargs):
+        """Represents an interpolated access on a spatially differentiated field
+
+        Args:
+            offset (Tuple[sympy.Expr]): Absolute position to determine the value of the spatial derivative
+        """
+        from pystencils.interpolation_astnodes import DiffInterpolatorAccess
+        assert isinstance(self.arg.field, Field), "Must be field to enable interpolated accesses"
+        return DiffInterpolatorAccess(self.arg.field.interpolated_access(offset, **kwargs).symbol, self.target, *offset)
 
 
 class DiffOperator(sp.Expr):
@@ -216,7 +228,9 @@ def diff_terms(expr):
 
     Example:
         >>> x, y = sp.symbols("x, y")
-        >>> diff_terms( diff(x, 0, 0)  )
+        >>> diff_terms( diff(x, 0, 0) )
+        {Diff(Diff(x, 0, -1), 0, -1)}
+        >>> diff_terms( diff(x, 0, 0) + y )
         {Diff(Diff(x, 0, -1), 0, -1)}
     """
     result = set()
@@ -304,7 +318,8 @@ def expand_diff_full(expr, functions=None, constants=None):
             functions.difference_update(constants)
 
     def visit(e):
-        e = e.expand()
+        if not isinstance(e, sp.Tuple):
+            e = e.expand()
 
         if e.func == Diff:
             result = 0
@@ -329,6 +344,9 @@ def expand_diff_full(expr, functions=None, constants=None):
             return result
         elif isinstance(e, sp.Piecewise):
             return sp.Piecewise(*((expand_diff_full(a, functions, constants), b) for a, b in e.args))
+        elif isinstance(expr, sp.Tuple):
+            new_args = [visit(arg) for arg in e.args]
+            return sp.Tuple(*new_args)
         else:
             new_args = [visit(arg) for arg in e.args]
             return e.func(*new_args) if new_args else e
@@ -368,6 +386,9 @@ def expand_diff_linear(expr, functions=None, constants=None):
                 return diff.split_linear(functions)
     elif isinstance(expr, sp.Piecewise):
         return sp.Piecewise(*((expand_diff_linear(a, functions, constants), b) for a, b in expr.args))
+    elif isinstance(expr, sp.Tuple):
+        new_args = [expand_diff_linear(e, functions) for e in expr.args]
+        return sp.Tuple(*new_args)
     else:
         new_args = [expand_diff_linear(e, functions) for e in expr.args]
         result = sp.expand(expr.func(*new_args) if new_args else expr)