- 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

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

- test run again
- notebooks not yet

João Victor Tozatti Risso authored Dec 13, 2017 and Martin Bauer committed Jan 19, 2018

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 toDot.

Remove unnecessary comments.

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

Testcases test_mu_equivalence, test_phi_equivalence are passing.

Fixed function bug.

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

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

- 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)

Move my llvm demo notebook in the correct folder

and not as a string.
Changed name of the file TypedSymbol to types.
Fixed usage of dtype accordingly, however i might not have found every
usage of dtype.

- support for different #ghost layers at each coordinate limit

- LoopOverDomain changed to be able to loop over rectangular sub-region of field
- support for slicing with makeSlice

- added sphinx files for documentation generation
- collected kernel creation functions in new "cpu" and "cudagpu" modules