Newer
Older
import numpy as np
from abc import ABC, abstractmethod, abstractproperty
from collections import defaultdict
from contextlib import contextmanager
from lbmpy.stencils import getStencil
from pystencils import Field, makeSlice
from pystencils.parallel.blockiteration import BlockIterationInfo
from pystencils.slicing import normalizeSlice, removeGhostLayers
from pystencils.utils import DotDict
try:
import pycuda.gpuarray as gpuarray
except ImportError:
gpuarray = None
class DataHandling(ABC):
"""
Manages the storage of arrays and maps them to a symbolic field.
Two versions are available: a simple, pure Python implementation for single node
simulations :py:class:SerialDataHandling and a distributed version using waLBerla in :py:class:ParallelDataHandling
Keep in mind that the data can be distributed, so use the 'access' method whenever possible and avoid the
'gather' function that has collects (parts of the) distributed data on a single process.
"""
def __init__(self):
self._preAccessFunctions = defaultdict(list)
self._postAccessFunctions = defaultdict(list)
# ---------------------------- Adding and accessing data -----------------------------------------------------------
@property
@abstractmethod
def dim(self):
"""Dimension of the domain, either 2 or 3"""
@abstractmethod
def add(self, name, fSize=1, dtype=np.float64, latexName=None, ghostLayers=None, layout=None, cpu=True, gpu=False):
"""
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
:param name: unique name that is used to access the field later
:param fSize: shape of the dim+1 coordinate. DataHandling supports zero or one index dimensions, i.e. scalar
fields and vector fields. This parameter gives the shape of the index dimensions. The default
value of 1 means no index dimension
:param dtype: data type of the array as numpy data type
:param latexName: optional, name of the symbolic field, if not given 'name' is used
:param ghostLayers: number of ghost layers - if not specified a default value specified in the constructor
is used
:param layout: memory layout of array, either structure of arrays 'SoA' or array of structures 'AoS'.
this is only important if fSize > 1
:param cpu: allocate field on the CPU
:param gpu: allocate field on the GPU
"""
@abstractmethod
def hasData(self, name):
"""
Returns true if a field or custom data element with this name was added
"""
@abstractmethod
def addLike(self, name, nameOfTemplateField, latexName=None, cpu=True, gpu=False):
"""
Adds an array with the same parameters (number of ghost layers, fSize, dtype) as existing array
:param name: name of new array
:param nameOfTemplateField: name of array that is used as template
:param latexName: see 'add' method
:param cpu: see 'add' method
:param gpu: see 'add' method
"""
@property
@abstractmethod
def fields(self):
"""Dictionary mapping data name to symbolic pystencils field - use this to create pystencils kernels"""
@abstractmethod
def access(self, name, sliceObj=None, innerGhostLayers=None, outerGhostLayers=0):
"""
Generator yielding locally stored sub-arrays together with information about their place in the global domain
:param name: name of data to access
:param sliceObj: optional rectangular sub-region to access
:param innerGhostLayers: how many inner (not at domain border) ghost layers to include
:param outerGhostLayers: how many ghost layers at the domain border to include
Yields a numpy array with local part of data and a BlockIterationInfo object containing geometric information
"""
@abstractmethod
def gather(self, name, sliceObj=None, allGather=False):
"""
Gathers part of the domain on a local process. Whenever possible use 'access' instead, since this method copies
the distributed data to a single process which is inefficient and may exhaust the available memory
:param name: name of the array to gather
:param sliceObj: slice expression of the rectangular sub-part that should be gathered
:param allGather: if False only the root process receives the result, if True all processes
:return: generator expression yielding the gathered field, the gathered field does not include any ghost layers
"""
def registerPreAccessFunction(self, name, function):
self._preAccessFunctions[name].append(function)
def registerPostAccessFunction(self, name, function):
self._postAccessFunctions[name].append(function)
@contextmanager
def accessWrapper(self, name):
for func in self._preAccessFunctions[name]:
func()
yield
for func in self._postAccessFunctions[name]:
func()
# ------------------------------- CPU/GPU transfer -----------------------------------------------------------------
@abstractmethod
def toCpu(self, name):
"""Copies GPU data of array with specified name to CPU.
Works only if 'cpu=True' and 'gpu=True' has been used in 'add' method"""
pass
@abstractmethod
def toGpu(self, name):
"""Copies GPU data of array with specified name to GPU.
Works only if 'cpu=True' and 'gpu=True' has been used in 'add' method"""
pass
@abstractmethod
def allToCpu(self, name):
"""Copies data from GPU to CPU for all arrays that have a CPU and a GPU representation"""
pass
@abstractmethod
def allToGpu(self, name):
"""Copies data from CPU to GPU for all arrays that have a CPU and a GPU representation"""
pass
# ------------------------------- Communication --------------------------------------------------------------------
def synchronizationFunctionCPU(self, names, stencil=None, **kwargs):
"""
Synchronizes ghost layers for distributed arrays - for serial scenario this has to be called
for correct periodicity handling
:param names: what data to synchronize: name of array or sequence of names
:param 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
:param kwargs: implementation specific, optional optimization parameters for communication
:return: function object to run the communication
"""
def synchronizationFunctionGPU(self, names, stencil=None, **kwargs):
"""
Synchronization of GPU fields, for documentation see CPU version above
"""
class SerialDataHandling(DataHandling):
class _PassThroughContextManager:
def __init__(self, arr):
self.arr = arr
def __enter__(self, *args, **kwargs):
return self.arr
def __init__(self, domainSize, defaultGhostLayers=1, defaultLayout='SoA', periodicity=False):
"""
Creates a data handling for single node simulations
:param domainSize: size of the spatial domain as tuple
:param defaultGhostLayers: nr of ghost layers used if not specified in add() method
:param defaultLayout: layout used if no layout is given to add() method
"""
super(SerialDataHandling, self).__init__()
self._domainSize = tuple(domainSize)
self.defaultGhostLayers = defaultGhostLayers
self.defaultLayout = defaultLayout
self._fields = DotDict()
self.cpuArrays = DotDict()
self.gpuArrays = DotDict()
if periodicity is None or periodicity is False:
periodicity = [False] * self.dim
if periodicity is True:
periodicity = [True] * self.dim
self._periodicity = periodicity
self._fieldInformation = {}
@property
def dim(self):
return len(self._domainSize)
@property
def fields(self):
return self._fields
def add(self, name, fSize=1, dtype=np.float64, latexName=None, ghostLayers=None, layout=None, cpu=True, gpu=False):
if ghostLayers is None:
ghostLayers = self.defaultGhostLayers
if layout is None:
layout = self.defaultLayout
if latexName is None:
latexName = name
assert layout in ('SoA', 'AoS')
kwargs = {
'shape': tuple(s + 2 * ghostLayers for s in self._domainSize),
'dtype': dtype,
'order': 'C' if layout == 'AoS' else 'F',
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
}
self._fieldInformation[name] = {
'ghostLayers': ghostLayers,
'fSize': fSize,
'layout': layout,
'dtype': dtype,
}
if fSize > 1:
kwargs['shape'] = kwargs['shape'] + (fSize,)
indexDimensions = 1
else:
indexDimensions = 0
if cpu:
if name in self.cpuArrays:
raise ValueError("CPU Field with this name already exists")
self.cpuArrays[name] = np.empty(**kwargs)
if gpu:
if name in self.gpuArrays:
raise ValueError("GPU Field with this name already exists")
self.gpuArrays[name] = gpuarray.empty(**kwargs)
assert all(f.name != latexName for f in self.fields.values()), "Symbolic field with this name already exists"
self.fields[name] = Field.createFixedSize(latexName, shape=kwargs['shape'], indexDimensions=indexDimensions,
dtype=kwargs['dtype'], layout=kwargs['order'])
def hasData(self, name):
return name in self.fields
def addLike(self, name, nameOfTemplateField, latexName=None, cpu=True, gpu=False):
self.add(name,latexName=latexName, cpu=cpu, gpu=gpu, **self._fieldInformation[nameOfTemplateField])
def access(self, name, sliceObj=None, outerGhostLayers='all', **kwargs):
if outerGhostLayers == 'all':
outerGhostLayers = self._fieldInformation[name]['ghostLayers']
if sliceObj is None:
sliceObj = [slice(None, None)] * self.dim
with self.accessWrapper(name):
arr = self.cpuArrays[name]
glToRemove = self._fieldInformation[name]['ghostLayers'] - outerGhostLayers
assert glToRemove >= 0
arr = removeGhostLayers(arr, indexDimensions=self.fields[name].indexDimensions, ghostLayers=glToRemove)
sliceObj = normalizeSlice(sliceObj, arr.shape[:self.dim])
yield arr[sliceObj], BlockIterationInfo(None, tuple(s.start for s in sliceObj), sliceObj)
def gather(self, name, sliceObj=None, **kwargs):
with self.accessWrapper(name):
gls = self._fieldInformation[name]['ghostLayers']
arr = self.cpuArrays[name]
arr = removeGhostLayers(arr, indexDimensions=self.fields[name].indexDimensions, ghostLayers=gls)
if sliceObj is not None:
arr = arr[sliceObj]
yield arr
def swap(self, name1, name2, gpu=False):
if not gpu:
self.cpuArrays[name1], self.cpuArrays[name2] = self.cpuArrays[name2], self.cpuArrays[name1]
else:
self.gpuArrays[name1], self.gpuArrays[name2] = self.gpuArrays[name2], self.gpuArrays[name1]
def allToCpu(self):
for name in self.cpuArrays.keys() & self.gpuArrays.keys():
self.toCpu(name)
def allToGpu(self):
for name in self.cpuArrays.keys() & self.gpuArrays.keys():
self.toGpu(name)
def toCpu(self, name):
self.gpuArrays[name].get(self.cpuArrays[name])
def toGpu(self, name):
self.gpuArrays[name].set(self.cpuArrays[name])
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
def synchronizationFunctionCPU(self, names, stencilName=None, **kwargs):
return self._synchronizationFunctor(names, stencilName, 'cpu')
def synchronizationFunctionGPU(self, names, stencilName=None, **kwargs):
return self._synchronizationFunctor(names, stencilName, 'gpu')
def _synchronizationFunctor(self, names, stencil, target):
if stencil is None:
stencil = 'D3Q27' if self.dim == 3 else 'D2Q9'
assert stencil in ("D2Q9", 'D3Q27'), "Serial scenario support only D2Q9 or D3Q27 for periodicity sync"
assert target in ('cpu', 'gpu')
if not hasattr(names, '__len__') or type(names) is str:
names = [names]
filteredStencil = []
for direction in getStencil(stencil):
useDirection = True
if direction == (0, 0) or direction == (0, 0, 0):
useDirection = False
for component, periodicity in zip(direction, self._periodicity):
if not periodicity and component != 0:
useDirection = False
if useDirection:
filteredStencil.append(direction)
resultFunctors = []
for name in names:
gls = self._fieldInformation[name]['ghostLayers']
if len(filteredStencil) > 0:
if target == 'cpu':
from pystencils.slicing import getPeriodicBoundaryFunctor
resultFunctors.append(getPeriodicBoundaryFunctor(filteredStencil, ghostLayers=gls))
else:
from pystencils.gpucuda.periodicity import getPeriodicBoundaryFunctor
resultFunctors.append(getPeriodicBoundaryFunctor(filteredStencil, self._domainSize,
indexDimensions=self.fields[name].indexDimensions,
indexDimShape=self._fieldInformation[name]['fSize'],
dtype=self.fields[name].dtype.numpyDtype,
ghostLayers=gls))
if target == 'cpu':
def resultFunctor():
for func in resultFunctors:
func(pdfs=self.cpuArrays[name])
else:
def resultFunctor():
for func in resultFunctors:
func(pdfs=self.gpuArrays[name])
return resultFunctor