%T A Comparison Of Data\-Parallel Programming Systems With Accelerator
%A Alex Cole, Alistair A. McEwan, Satnam Singh
%E Peter H. Welch, Adam T. Sampson, Jan Bækgaard Pedersen, Jon Kerridge, Jan F. Broenink, Frederick R. M. Barnes
%B Communicating Process Architectures 2011
%X Data parallel programming provides an accessible model for
exploiting the power of parallel computing elements without
resorting to the explicit use of low level programming
techniques based on locks, threads and monitors. The
emergence of GPUs with hundreds or thousands of
processing cores has made data parallel computing available
to a wider class of programmers. GPUs can be used not only
for accelerating the processing of computer graphics but
also for general purpose data\-parallel programming. Low
level data\-parallel programming languages based on the CUDA
provide an approach for developing programs for GPUs but
these languages require explicit creation and coordination
of threads and careful data layout and movement. This
has created a demand for higher level programming languages
and libraries which raise the abstraction level of
data\-parallel programming and increase programmer
productivity. The Accelerator system was developed by
Microsoft for writing data parallel code in a high level
manner which can execute on GPUs, multicore processors using
SSE3 vector instructions and FPGA chips. This paper compares
the performance and development effort of the high level
Accelerator system against lower level systems which
are more difficult to use but may yield better results.
Specifically, we compare against the NVIDIA CUDA compiler
and sequential C++ code considering both the level of
abstraction in the implementation code and the execution
models. We compare the performance of these systems using
several case studies. For some classes of problems,
Accelerator has a performance comparable to CUDA, but for
others its performance is significantly reduced however in
all cases it provides a model which is easier to use
and allows for greater programmer productivity.
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