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Mozart : Efficient Composition of Library Functions for Heterogeneous Execution

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Languages and Compilers for Parallel Computing (LCPC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11403))

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Abstract

Current processor trend is to couple a commodity processor with a GPU, a co-processor, or an accelerator. To unleash the full computational power of such heterogeneous systems is a daunting task: programmers often resort to heterogeneous scheduling runtime frameworks that use device specific library routines. However, highly-tuned libraries do not compose very well across heterogeneous architectures. That is, important performance-oriented optimizations such as data locality and reuse “across” library calls is not fully exploited. In this paper, we present a framework, called Mozart, to extend existing library frameworks to efficiently compose a sequence of library calls for heterogeneous execution. Mozart consists of two components: library description (LD) and library composition runtime. We advocate library writers to wrap existing libraries using LD in order to provide their performance parameters on heterogeneous cores, no programmer intervention is necessary. Our runtime performs composition of libraries via task-fission, load balances among heterogeneous cores using information from LD, and automatically adapts to runtime behavior of an application. We evaluate Mozart on a Xeon + 2 Xeon Phi system using the High Performance Linpack benchmark which is the most popular benchmark to rank supercomputers in TOP500 and show GFLOPS improvement of 31.7 % over MKL with Automatic Offload and 6.7 % over hand-optimized ninja code.

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Notes

  1. 1.

    Note that we specifically choose the blocked version of HPL to highlight the contribution of this paper, which is library composition. We do not directly use the LU factorization algorithm provided by MKL.

  2. 2.

    Hand-tuned implementations are rarely performance portable.

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Authors and Affiliations

  1. Intel Corporation, Santa Clara, CA, USA

    Rajkishore Barik, Tatiana Shpeisman, Hongbo Rong, Chunling Hu, Victor W. Lee, Todd A. Anderson, Greg Henry, Hai Liu, Youfeng Wu, Paul Petersen & Geoff Lowney

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  1. Rajkishore Barik
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  1. Texas A&M University, College Station, TX, USA

    Lawrence Rauchwerger

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Barik, R. et al. (2019). Mozart : Efficient Composition of Library Functions for Heterogeneous Execution. In: Rauchwerger, L. (eds) Languages and Compilers for Parallel Computing. LCPC 2017. Lecture Notes in Computer Science(), vol 11403. Springer, Cham. https://doi.org/10.1007/978-3-030-35225-7_13

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  • DOI: https://doi.org/10.1007/978-3-030-35225-7_13

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