Efficient Hybrid Execution of C++ Applications using Intel(R) Xeon Phi(TM) Coprocessor

Jiri Dokulil, Enes Bajrovic, Siegfried Benkner, Sabri Pllana, Martin Sandrieser, Beverly Bachmayer
Research Group Scientific Computing, University of Vienna, Austria
arXiv:1211.5530 [cs.DC] (23 Nov 2012)


   author={Dokulil}, J. and {Bajrovic}, E. and {Benkner}, S. and {Pllana}, S. and {Sandrieser}, M. and {Bachmayer}, B.},

   title={"{Efficient Hybrid Execution of C++ Applications using Intel(R) Xeon Phi(TM) Coprocessor}"},

   journal={ArXiv e-prints},




   keywords={Computer Science – Distributed, Parallel, and Cluster Computing, Computer Science – Software Engineering},




   adsnote={Provided by the SAO/NASA Astrophysics Data System}


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The introduction of Intel(R) Xeon Phi(TM) coprocessors opened up new possibilities in development of highly parallel applications. The familiarity and flexibility of the architecture together with compiler support integrated into the Intel C++ Composer XE allows the developers to use familiar programming paradigms and techniques, which are usually not suitable for other accelerated systems. It is now easy to use complex C++ template-heavy codes on the coprocessor, including for example the Intel Threading Building Blocks (TBB) parallelization library. These techniques are not only possible, but usually efficient as well, since host and coprocessor are of the same architectural family, making optimization techniques designed for the Xeon CPU also beneficial on Xeon Phi. As a result, highly optimized Xeon codes (like the TBB library) work well on both. In this paper we present a new parallel library construct, which makes it easy to apply a function to every member of an array in parallel, dynamically distributing the work between the host CPUs and one or more coprocessor cards. We describe the associated runtime support and use a physical simulation example to demonstrate that our library construct can be used to quickly create a C++ application that will significantly benefit from hybrid execution, simultaneously exploiting CPU cores and coprocessor cores. Experimental results show that one optimized source code is sufficient to make the host and the coprocessors run efficiently.
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