- SSA form is checked
- loop independence condition is checked
- bug fix in Field.create_generic when using index_shape

- new kernel creation function for kernels that write to staggered fields
- simplification of conditionals with integers uses ISL library now
  via islpy bindings

- removed warnings
- added flake8 as CI target

- test run again
- notebooks not yet

- assignment collection
- sympyextensions

- Previously sympy.Eq was used to represent assignments. However Eq
  represents equality not assignment. This means that sometimes sympy
  "simplified" an equation like a = a  to True,
-> replaced sp.Eq by pystencils.Assignment everywhere
- renamed EquationCollection to AssignmentCollection

João Victor Tozatti Risso authored 7 years ago and Martin Bauer committed 7 years ago

Concept: Generate code involving the (un)packing of fields (from)to linear
(1D) arrays, i.e. (de)serialization of the field values for buffered
communication.

A linear index is generated for the buffer, by inferring the strides and
variables of the loops over fields in the AST. In the CPU, this information is
obtained through the makeLoopOverDomain function, in
pystencils/transformations/transformations.py. On CUDA, the strides of
the fields (excluding buffers) are combined with the indexing variables to infer
the indexing of the buffer.

What is supported:
    - code generation for both CPU and GPU
    - (un)packing of fields with all the memory layouts supported by
    pystencils
    - (un)packing slices of fields (from)into the buffer
    - (un)packing subsets of cell values from the fields (from)into the buffer

Limitations:

- assumes that only one buffer and one field are being operated within
each kernel, however multiple equations involving the buffer and the
field are supported.

- (un)packing multiple cell values (from)into the buffer is supported,
however it is limited to the fields with indexDimensions=1. The same
applies to (un)packing subset of cell values of each cell.

Changes in this commit:

- add the FieldType enumeration to pystencils/field.py, to mark fields
of various types. This is replaces and is a generalization of the
isIndexedField boolean flag of the Field class. For now, the types
supported are: generic, indexed and buffer fields.

- add the fieldType property to the Field class, which indicates the
type of the field. Modifications were also performed to the member
functions of the Field class to add this property.

- add resolveBufferAccesses function, which replaces the fields marked
as buffers with the actual field access in the AST traversal.

Miscelaneous changes:

- add blockDim and gridDim variables as CUDA indexing variables.

- loop cutting
- simplification of conditionals inside loop

Added LLVM: CodePrinter and a compiler
Updated data_types
Added tests
Added jupyter notebooks
Fixed bugs
Restructured transformation functions

- activated vectorization for LBM kernels

- renaming because of clashes with types.py from other packages

- first vectorization tests are running
- type system: use memoized getTypeOfExpression
- casts are done using sp.Function('cast')
- C backend adapted for vectorization support
- AST nodes can required optional headers

- support for kernels that run over walberla ghost layers

- old method produced unused variables
- old method was not deterministic in the output code i.e. ordering of
  the introduced constants
- moveConstantsBeforeLoops transformation was also not deterministic

-> reading from arrays was slower with some compilers

- pystencils can create now a non-compilable kernel that can be
  analyzed by kerncraft

- abstraction layer for selecting CUDA block and grid sizes
  - line based (was implemented before)
  - block based (new, more flexible)
-  new conditional (if/else) ast node, which is necessary for indexing schemes (guarding if)

- this extension is necessary for more generic boundary treatment
- cells can now be structs, i.e. contain different data types
- instead of having numeric index dimensions, one can use the index per cell to adress struct elements

- use data type class consistently instead of strings (in TypedSymbol, Field and jit module)
- new datatype class is based on numpy types with additional specifier information (const and restrict)
- translation between data type class and other modules (numpy, ctypes)

- due to unordered containers  the same method could produce different but functionally equivalent code
- this behaviour is bad for caching

-> switched to ordered containers