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Performance Portability Across Heterogeneous SoCs Using a Generalized Library-Based Approach

Authors:

Lieven Eeckhout,

Chengyong WuAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 11, Issue 2

Article No.: 21, Pages 1 - 25

https://doi.org/10.1145/2608253

Published: 01 June 2014 Publication History

Abstract

Because of tight power and energy constraints, industry is progressively shifting toward heterogeneous system-on-chip (SoC) architectures composed of a mix of general-purpose cores along with a number of accelerators. However, such SoC architectures can be very challenging to efficiently program for the vast majority of programmers, due to numerous programming approaches and languages. Libraries, on the other hand, provide a simple way to let programmers take advantage of complex architectures, which does not require programmers to acquire new accelerator-specific or domain-specific languages. Increasingly, library-based, also called algorithm-centric, programming approaches propose to generalize the usage of libraries and to compose programs around these libraries, instead of using libraries as mere complements.

In this article, we present a software framework for achieving performance portability by leveraging a generalized library-based approach. Inspired by the notion of a component, as employed in software engineering and HW/SW codesign, we advocate nonexpert programmers to write simple wrapper code around existing libraries to provide simple but necessary semantic information to the runtime. To achieve performance portability, the runtime employs machine learning (simulated annealing) to select the most appropriate accelerator and its parameters for a given algorithm. This selection factors in the possibly complex composition of algorithms used in the application, the communication among the various accelerators, and the tradeoff between different objectives (i.e., accuracy, performance, and energy).

Using a set of benchmarks run on a real heterogeneous SoC composed of a multicore processor and a GPU, we show that the runtime overhead is fairly small at 5.1% for the GPU and 6.4% for the multi-core. We then apply our accelerator selection approach to a simulated SoC platform containing multiple inexact accelerators. We show that accelerator selection together with hardware parameter tuning achieves an average 46.2% energy reduction and a speedup of 2.1× while meeting the desired application error target.

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Cited By

Stanley-Marbell PAlaghi ACarbin MDarulova EDolecek LGerstlauer AGillani GJevdjic DMoreau TCacciotti MDaglis AJerger NFalsafi BMisailovic SSampson AZufferey D(2020)Exploiting Errors for EfficiencyACM Computing Surveys10.1145/339489853:3(1-39)Online publication date: 12-Jun-2020
https://dl.acm.org/doi/10.1145/3394898
Gadioli DVitali EPalermo GSilvano C(2019)mARGOt: A Dynamic Autotuning Framework for Self-Aware Approximate ComputingIEEE Transactions on Computers10.1109/TC.2018.288359768:5(713-728)Online publication date: 1-May-2019
https://doi.org/10.1109/TC.2018.2883597
Ahmad MDogan HMichael CKhan O(2019)HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS.2019.00039(268-281)Online publication date: Mar-2019
https://doi.org/10.1109/ISPASS.2019.00039
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Index Terms

Performance Portability Across Heterogeneous SoCs Using a Generalized Library-Based Approach
1. Software and its engineering
  1. Software notations and tools
    1. Compilers

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cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 11, Issue 2

June 2014

210 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/2639036

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Copyright © 2014 ACM.

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Publication History

Published: 01 June 2014

Accepted: 01 January 2014

Revised: 01 December 2013

Received: 01 June 2013

Published in TACO Volume 11, Issue 2

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Stanley-Marbell PAlaghi ACarbin MDarulova EDolecek LGerstlauer AGillani GJevdjic DMoreau TCacciotti MDaglis AJerger NFalsafi BMisailovic SSampson AZufferey D(2020)Exploiting Errors for EfficiencyACM Computing Surveys10.1145/339489853:3(1-39)Online publication date: 12-Jun-2020
https://dl.acm.org/doi/10.1145/3394898
Gadioli DVitali EPalermo GSilvano C(2019)mARGOt: A Dynamic Autotuning Framework for Self-Aware Approximate ComputingIEEE Transactions on Computers10.1109/TC.2018.288359768:5(713-728)Online publication date: 1-May-2019
https://doi.org/10.1109/TC.2018.2883597
Ahmad MDogan HMichael CKhan O(2019)HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS.2019.00039(268-281)Online publication date: Mar-2019
https://doi.org/10.1109/ISPASS.2019.00039
Sedova AEblen JBudiardja RTharrington ASmith J(2018)High-Performance Molecular Dynamics Simulation for Biological and Materials Sciences: Challenges of Performance Portability2018 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC)10.1109/P3HPC.2018.00004(1-13)Online publication date: Nov-2018
https://doi.org/10.1109/P3HPC.2018.00004
Sui XLenharth AFussell DPingali K(2016)Proactive Control of Approximate ProgramsACM SIGARCH Computer Architecture News10.1145/2980024.287240244:2(607-621)Online publication date: 25-Mar-2016
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Sui XLenharth AFussell DPingali K(2016)Proactive Control of Approximate ProgramsACM SIGOPS Operating Systems Review10.1145/2954680.287240250:2(607-621)Online publication date: 25-Mar-2016
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Sui XLenharth AFussell DPingali K(2016)Proactive Control of Approximate ProgramsACM SIGPLAN Notices10.1145/2954679.287240251:4(607-621)Online publication date: 25-Mar-2016
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Sui XLenharth AFussell DPingali KConte TZhou Y(2016)Proactive Control of Approximate ProgramsProceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/2872362.2872402(607-621)Online publication date: 25-Mar-2016
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Ding YAnsel JVeeramachaneni KShen XO’Reilly UAmarasinghe S(2015)Autotuning algorithmic choice for input sensitivityACM SIGPLAN Notices10.1145/2813885.273796950:6(379-390)Online publication date: 3-Jun-2015
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Ding YAnsel JVeeramachaneni KShen XO’Reilly UAmarasinghe SGrove DBlackburn S(2015)Autotuning algorithmic choice for input sensitivityProceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation10.1145/2737924.2737969(379-390)Online publication date: 3-Jun-2015
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