- was not used consistently before
- symbol names are expected to be valid C identifiers
- for complicated field names, the latex_name of field should be used

- 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

- better latex display for indirect accesses
- new field type 'custom': only custom fields can be accessed indirectly
  no static bounds check possible for custom fields

- 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

- 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

- 3 phase model works now with step sytem

- previously all objects where cached by id()
- for waLBerla simulations in each time step a new np.array view
  is created from the waLBerla field. Each of these views has a
  different id -> caching did not work for waLBerla setups
- changed hash for numpy arrays: instead of id, a tuple of
  (dataPtr, strides, shapes) is used as hash input

- scaling interface width eta instead of surface tensions tau to correct
  interface profile & surface tensions

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...

- 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

- 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

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

- cache relied on uniqueness of  python id()
- id may be reused if object is freed
-> object must be held alive
-> kernel keeps all it arguments it was ever called with, alive (problematic in terms of memory consumption)

-> smaller block