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Goal of compyte

Make a common GPU ndarray(matrix/tensor or n dimensions) that can be reused by all projects.

Mailing list

• Development/user mailing list: http://lists.tiker.net/listinfo/gpundarray
• Announce mailing list(low volume): http://lists.tiker.net/listinfo/gpundarray-announce

Comparison of existing implementation

Branch

• The current development that include a real C back-end and full support for OpenCL is in this branch: https://github.com/abergeron/compyte/tree/reorg

Motivation

• Currently there are at least 6 different gpu arrays in python
  • CudaNdarray(Theano), GPUArray(pycuda), CUDAMatrix(cudamat), GPUArray(pyopencl), Clyther, Copperhead, ...
  • There are even more if we include other languages.
• They are incompatible
  • None have the same properties and interface
• All of them are a subset of numpy.ndarray on the gpu!

Lack of Standard Creates Problems:

• Duplicates work
  • GPU code is harder/slower to do correctly and fast than on the CPU/python
• Harder to port/reuse code
• Harder to find/distribute code
• Divides development work

Pitfalls to Avoid

• Start alone
  • We need different people/groups to "adopt" the new GpuNdArray
• Too simple - other projects won't adopt
• Too general - other projects will implement "light" versions... and not adopt
  • Having an easy way to convert/check conditions as numpy could alleviate this.

The preferred option is to have a general version with easy check/conversion to allow supporting only a subset!

Design Goals

• Make it VERY similar to numpy.ndarray
  • Easier to attract other people from python community
• Have the base object in C to allow collaboration with more projects.
  • We want people from C, C++, ruby, R, ... all use the same base Gpu ndarray.
• Be compatible with CUDA and OpenCL

Current behavior not wanted

• No CPU code generated from the python interface (for PyOpenCL and PyCUDA). Gpu code is OK.

Implementation plan

All of the basic C code is done. Currently working on elementwise functionality in prevision of a PyOpenCL/PyCUDA integration.

Sketch of the file structure and the reasoning behind it

This section will detail the file structure and give you a hint of what to expect if you intent on shipping a project integrating this code. Also this applies to the code in the reorg branch which will become the mainline soon. It is located here: http://github.com/abergeron/compyte/tree/reorg

Some of these files are not in the repository yet, which means that this functionality is being worked on.

The main files are:

• ndarray/compyte_buffer.h:
  • Defines the base compyte_buffer object
  • Also defines the structure for GpuArray and GpuKernel
• ndarray/compyte_buffer_cuda.c:
  • Implements the CUDA version of the compyte_buffer API
• ndarray/compyte_buffer_opencl.c:
  • Implements the OpenCL version of the compyte_buffer API
• ndarray/pygpu_ndarray.pyx
  • Define a Cython wrapper that exposes the GpuArray object and a couple of function to mimic the interface of numpy.ndarray
• elemwise.py:
  • Support running arbitrary elementwise kernels on GpuArray of arbitrary memory layout (python-only).

These files serve as support for the functionality above:

• ndarray/compyte_types.{c,h}:
  • generated by ndarray/gen_types.py
  • serve as a type table for operations that need to know some information about types involved
• ndarray/compyte_util.{c,h}:
  • some generally useful functions that don't really fit anywhere else.
• ndarray/setup.py:
  • Builds the python module implemented in pygpu_ndarray.pyx along with all the supporting code

These files serve for portability (mainly to support windows):

• ndarray/compyte_compat.h
• ndarray/compyte_mkstemp.c
• ndarray/compyte_strl.c
• ndarray/wincompat/*

Some tests for the python interface (that also test the underlying C code):

• ndarray/test_gpu_ndarray.py (test basic functionality: init, copy, indexing, ...)
• tests/test_elemwise.py (test that the numpy-like elemwise operations on array work correctly)

Some gotchas and differences from numpy

• We have the updateifcopy flag as numpy, but it is always False and we expect it is False.
• Buffer offsets (like what is generated when you do a[1:3]), are only partially supported under OpenCL 1.0. You cannot run kernels on them without copying them beforehand.