OpenMPC: extended OpenMP for efficient programming and tuning on GPUs

Seyong Lee, Rudolf Eigenmann
<span title="">2013</span> <i title="Inderscience Publishers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mvlpi4a7izattgygry5kh4ruaq" style="color: black;">International Journal of Computational Science and Engineering (IJCSE)</a> </i> &nbsp;
General-Purpose Graphics Processing Units (GPGPUs) provide inexpensive, high performance platforms for compute-intensive applications. However, their programming complexity poses a significant challenge to developers. Even though the CUDA (Compute Unified Device Architecture) programming model offers better abstraction, developing efficient GPGPU code is still complex and error-prone. This paper proposes a directive-based, high-level programming model, called OpenMPC, which addresses both
more &raquo; ... mmability and tunability issues on GPGPUs. We have developed a fully automatic compilation and user-assisted tuning system supporting OpenMPC. In addition to a range of compiler transformations and optimizations, the system includes tuning capabilities for generating, pruning, and navigating the search space of compilation variants. Evaluation using fourteen applications shows that our system achieves 75% of the performance of the hand-coded CUDA programs (92% if excluding one exceptional case).
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1504/ijcse.2013.052110">doi:10.1504/ijcse.2013.052110</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eipvcpeaejghnl73jler5jafky">fatcat:eipvcpeaejghnl73jler5jafky</a> </span>
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