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Using OpenMP to Detect and Speculate Dynamic DOALL Loops

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OpenMP: Portable Multi-Level Parallelism on Modern Systems (IWOMP 2020)

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Abstract

Production compilers such as GCC, Clang, IBM XL and the Intel C Compiler employ multiple loop parallelization techniques that help in the task of parallel programming. Although very effective, these techniques are only applicable to loops that the compiler can statically determine to have no loop-carried dependences (DOALL). Because of this restriction, a plethora of Dynamic DOALL (D-DOALL) loops are outright ignored, leaving the parallelism potential of many computationally intensive applications unexplored. This paper proposes a new analysis tool based on OpenMP clauses that allow the programmer to generate detailed profiling of any given loop by identifying its loop-carried dependences and producing carefully selected execution time metrics. The paper also proposes a set of heuristics to be used in conjunction with the analysis tool metrics to properly select loops which could be parallelized through speculative execution, even in the presence of loop-carried dependences. A thorough analysis of 180 loops from 45 benchmarks of three different suites (cBench, Parboil, and Rodinia) was realized using the Intel C Compiler and the proposed approach. Experimental results using static analysis from the Intel C Compiler showed that only 7.8% of the loops are DOALL. The proposed analysis tool exposed 39.5% May DOALL (M-DOALL) loops which could be eventually parallelized using speculative execution, as exemplified by loops from the Parboil sad program which produced a speedup of 1.92x.

Supported by CCES (Center for Computing in Engineering and Sciences) and FAPESP (São Paulo Research Foundation). Grant Numbers: 2013/08293-7, 2019/04536-9, 2016/15337-9 and 2019/01110-0.

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Notes

  1. 1.

    Dependences that arise across different loop iterations.

  2. 2.

    Code duplication can be avoided in some cases, but not always  [13].

  3. 3.

    a.k.a. Flow or Read-After-Write (RAW) dependence.

  4. 4.

    a.k.a. Write-After-Read (WAR) dependence.

  5. 5.

    a.k.a. Write-After-Write (WAW) dependence.

  6. 6.

    -q-opt-report5 and -qopt-report-phase=vec.

  7. 7.

    A loop is canonical if and only if it has a single induction variable, a simple test expression, and its induction variable is never modified in the loop body.

  8. 8.

    Average variance across measurements was lower than 0.5% of the mean.

  9. 9.

    12 from cBench, 12 from Parboil and 20 from Rodinia.

  10. 10.

    57 from cBench, 25 from Parboil and 41 from Rodinia.

  11. 11.

    These loops were chosen because they are representative of the values of the presented metrics.

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Authors and Affiliations

  1. IC and IQ – UNICAMP, Campinas, Brazil

    Bruno Chinelato Honorio, João P. L. de Carvalho, Munir Skaf & Guido Araujo

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  1. Bruno Chinelato Honorio
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Correspondence to Bruno Chinelato Honorio .

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Editors and Affiliations

  1. Texas Advanced Computing Center (TACC), Austin, TX, USA

    Kent Milfeld

  2. Lawrence Livermore National Laboratory, Livermore, CA, USA

    Bronis R. de Supinski

  3. Texas Advanced Computing Center (TACC), Austin, TX, USA

    Lars Koesterke

  4. RWTH Aachen University, Aachen, Germany

    Jannis Klinkenberg

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Honorio, B.C., de Carvalho, J.P.L., Skaf, M., Araujo, G. (2020). Using OpenMP to Detect and Speculate Dynamic DOALL Loops. In: Milfeld, K., de Supinski, B., Koesterke, L., Klinkenberg, J. (eds) OpenMP: Portable Multi-Level Parallelism on Modern Systems. IWOMP 2020. Lecture Notes in Computer Science(), vol 12295. Springer, Cham. https://doi.org/10.1007/978-3-030-58144-2_15

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