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Paper Details

%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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