Paper Details

@InProceedings{Cole11,
  title = "{A} {C}omparison {O}f {D}ata-{P}arallel {P}rogramming {S}ystems {W}ith {A}ccelerator",
  author= "Cole, Alex and McEwan, Alistair A. and Singh, Satnam",
  editor= "Welch, Peter H. and Sampson, Adam T. and Pedersen, Jan Bækgaard and Kerridge, Jon and Broenink, Jan F. and Barnes, Frederick R. M.",
  pages = "111--130",
  booktitle= "{C}ommunicating {P}rocess {A}rchitectures 2011",
  isbn= "978-1-60750-773-4",
  year= "2011",
  month= "jun",
  abstract= "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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