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International Workshop on Languages and Compilers for Parallel Computing

LCPC 2016: Languages and Compilers for Parallel Computing pp 73–87Cite as

Language Support for Reliable Memory Regions

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10136))

Abstract

The path to exascale computational capabilities in high-performance computing (HPC) systems is challenged by the inadequacy of present software technologies to adapt to the rapid evolution of architectures of supercomputing systems. The constraints of power have driven system designs to include increasingly heterogeneous architectures and diverse memory technologies and interfaces. Future systems are also expected to experience an increased rate of errors, such that the applications will no longer be able to assume correct behavior of the underlying machine. To enable the scientific community to succeed in scaling their applications, and to harness the capabilities of exascale systems, we need software strategies that enable explicit management of resilience to errors in the system, in addition to locality of reference in the complex memory hierarchies of future HPC systems.

In prior work, we introduced the concept of explicitly reliable memory regions, called havens. Memory management using havens supports reliability management through a region-based approach to memory allocations. Havens enable the creation of robust memory regions, whose resilient behavior is guaranteed by software-based protection schemes. In this paper, we propose language support for havens through type annotations that make the structure of a program’s havens more explicit and convenient for HPC programmers to use. We describe how the extended haven-based memory management model is implemented, and demonstrate the use of the language-based annotations to affect the resiliency of a conjugate gradient solver application.

This work was sponsored by the U.S. Department of Energy’s Office of Advanced Scientific Computing Research. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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Authors and Affiliations

  1. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

    Saurabh Hukerikar & Christian Engelmann

Authors
  1. Saurabh Hukerikar
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  2. Christian Engelmann
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Correspondence to Saurabh Hukerikar .

Editor information

Editors and Affiliations

  1. University of Rochester , Rochester, New York, USA

    Chen Ding

  2. University of Rochester , Rochester, New York, USA

    John Criswell

  3. Huawei Inc. , Santa Clara, California, USA

    Peng Wu

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Hukerikar, S., Engelmann, C. (2017). Language Support for Reliable Memory Regions. In: Ding, C., Criswell, J., Wu, P. (eds) Languages and Compilers for Parallel Computing. LCPC 2016. Lecture Notes in Computer Science(), vol 10136. Springer, Cham. https://doi.org/10.1007/978-3-319-52709-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-52709-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52708-6

  • Online ISBN: 978-3-319-52709-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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