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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/auf4c3co6zegri7sh6i5vkpu2i" style="color: black;">2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS)</a>
Tapas is our new C++ programming framework for hierarchical algorithms such as n-body, on large scale heterogeneous supercomputers. Although n-body and their variants are widely used in scientific applications, their correct implementations are often difficult on such modern machines, as the algorithms are irregular, complex, and involve explicit task parallel programming over distributed nodes. Encapsulating the complexities in a library or a framework has been challenging due to irregular<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icpads.2016.0145">doi:10.1109/icpads.2016.0145</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icpads/FukudaMMYTM16.html">dblp:conf/icpads/FukudaMMYTM16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fiuddf2ce5b4dnd7x7nbyvl4ny">fatcat:fiuddf2ce5b4dnd7x7nbyvl4ny</a> </span>
more »... access over massively distributed memory. Tapas solves this by converting the users clean implicit-style parallel program into an inspector-executor style code on heterogeneous multi-core, multi-node environment solely by the use of C++ template metaprogramming. Prototype implementation of a Fast Multipole Method (FMM) on Tapas demonstrates 85% to 115% of the performance of ExaFMM, the fastest handtuned implementation of FMM to date, and scales to over 1,500 of CPU cores while efficiently utilizing hundreds of GPUs on heterogeneous machines such as TSUBAME2.5.
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