This might be of interest to members of this group:
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Compiling Python to a hybrid execution environment
April 12th, 2010
Abstract:
A new compilation framework enables the execution of
numerical-intensive applications, written in Python, on a hybrid
execution environment formed by a CPU and a GPU. This compiler
automatically computes the set of memory locations that need to be
transferred to the GPU, and produces the correct mapping between the CPU
and the GPU address spaces. Thus, the programming model implements a
virtual shared address space. This framework is implemented as a
combination of unPython, an ahead-of-time compiler from Python/NumPy to
the C++ programming language, and jit4GPU, a just-in-time compiler to
the AMD CAL interface using CAL pixel shaders. Jit4GPU includes an
optimizer that performs several loop transformations and reduces the
number of texture instructions. Experimental evaluation was done on a
Radeon 4850 and demonstrates that for some benchmarks the generated GPU
code is 50 times faster than generated OpenMP code. The GPU performance
also compares favorably with optimized CPU BLAS code for
single-precision computations in most cases. Code transformations
performed by Jit4GPU on GPU code were also shown to produce considerable
speedup compared to unoptimized GPU code.
http://gpgpu.org/tag/compilers
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