- backend, target and compile are now normal members of the
  KernelFunction node and populated in constructor

This restructuring allows for easier separation of modules into
separate repositories later. Also, now pip install with repo url can be
used.

The setup.py files have also been updated to correctly reference each
other. Module versions are not extracted from git state

- small (length < 5) arrays with shape and stride information had to be
  memcpy'd to the GPU before every kernel call
- instead of passing the information as arrays, the single elements are
  passed
- leads to more function arguments, but simplifies GPU kernel calls

-> changes in all backends required

- put all buffer related stuff into separate functions
- should be functionally equivalent

- different approach in pystencils: absolute indexing

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

- removed warnings
- added flake8 as CI target

- test run again
- notebooks not yet

- 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

- now an lbMethod or updateRule can be passed in directly
- made method/update rule/ast function independent i.e. no function
  uses the same parameters as on of the others

- 3 phase model works now with step sytem

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.

Fixed demo_heat_equation notebook.

- single function to create kernel for specified target
- data type creation from string - reuse numpy functionality
- bugfixes in dot display

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

- 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

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

- periodic kernels not yet OpenMP parallel

- 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)