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Verifying quantitative reliability for programs that execute on unreliable hardware

Authors:

Michael Carbin,

Sasa Misailovic,

Martin C. RinardAuthors Info & Claims

ACM SIGPLAN Notices, Volume 48, Issue 10

Pages 33 - 52

https://doi.org/10.1145/2544173.2509546

Published: 29 October 2013 Publication History

Abstract

Emerging high-performance architectures are anticipated to contain unreliable components that may exhibit soft errors, which silently corrupt the results of computations. Full detection and masking of soft errors is challenging, expensive, and, for some applications, unnecessary. For example, approximate computing applications (such as multimedia processing, machine learning, and big data analytics) can often naturally tolerate soft errors.

We present Rely a programming language that enables developers to reason about the quantitative reliability of an application -- namely, the probability that it produces the correct result when executed on unreliable hardware. Rely allows developers to specify the reliability requirements for each value that a function produces.

We present a static quantitative reliability analysis that verifies quantitative requirements on the reliability of an application, enabling a developer to perform sound and verified reliability engineering. The analysis takes a Rely program with a reliability specification and a hardware specification that characterizes the reliability of the underlying hardware components and verifies that the program satisfies its reliability specification when executed on the underlying unreliable hardware platform. We demonstrate the application of quantitative reliability analysis on six computations implemented in Rely.

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Cited By

Seyedi AGunnar Kjeldsberg PBirkeland R(2024)System-Scenario Methodology to Design a Highly Reliable Radiation-Hardened Memory for Space ApplicationsComputer Memory and Data Storage10.5772/intechopen.113327Online publication date: 10-Jan-2024
https://doi.org/10.5772/intechopen.113327
Renda ADing YCarbin M(2023)Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of ProgramsProceedings of the ACM on Programming Languages10.1145/36228567:OOPSLA2(1648-1676)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622856
Deng HPeng YHicks MWu X(2023)Automating NISQ Application Design with Meta Quantum Circuits with Constraints (MQCC)ACM Transactions on Quantum Computing10.1145/35793694:3(1-29)Online publication date: 8-Apr-2023
https://dl.acm.org/doi/10.1145/3579369
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Index Terms

Verifying quantitative reliability for programs that execute on unreliable hardware
1. Theory of computation
  1. Logic
    1. Programming logic
  2. Semantics and reasoning
    1. Program reasoning

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Information & Contributors

Information

Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 48, Issue 10

OOPSLA '13

October 2013

867 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/2544173

Editor:
Mark W. Bailey
Hamilton College, Clinton, NY

Issue’s Table of Contents

OOPSLA '13: Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
October 2013
904 pages
ISBN:9781450323741
DOI:10.1145/2509136
Co-chair:
Antony Hosking
Purdue University, USA
,
General Chair:
Patrick Eugster
Purdue University, USA
,
Program Chair:
Cristina V. Lopes
University of California, Irvine, USA

Copyright © 2013 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2013

Published in SIGPLAN Volume 48, Issue 10

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Cited By

Seyedi AGunnar Kjeldsberg PBirkeland R(2024)System-Scenario Methodology to Design a Highly Reliable Radiation-Hardened Memory for Space ApplicationsComputer Memory and Data Storage10.5772/intechopen.113327Online publication date: 10-Jan-2024
https://doi.org/10.5772/intechopen.113327
Renda ADing YCarbin M(2023)Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of ProgramsProceedings of the ACM on Programming Languages10.1145/36228567:OOPSLA2(1648-1676)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622856
Deng HPeng YHicks MWu X(2023)Automating NISQ Application Design with Meta Quantum Circuits with Constraints (MQCC)ACM Transactions on Quantum Computing10.1145/35793694:3(1-29)Online publication date: 8-Apr-2023
https://dl.acm.org/doi/10.1145/3579369
Ma DZhang XHuang KJiang YChang WJiao X(2022)DEVoT: Dynamic Delay Modeling of Functional Units Under Voltage and Temperature VariationsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.307697041:4(827-839)Online publication date: Apr-2022
https://doi.org/10.1109/TCAD.2021.3076970
Parasyris KGeorgakoudis GMenon HDiffenderfer JLaguna IOsei-Kuffuor DSchordan Mde Supinski BHall MGamblin T(2021)HPACProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476216(1-14)Online publication date: 14-Nov-2021
https://dl.acm.org/doi/10.1145/3458817.3476216
Jiang HZhang HTang XGovindaraj VSampson JKandemir MZhang DFreund SYahav E(2021)Fluid: a framework for approximate concurrency via controlled dependency relaxationProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454042(252-267)Online publication date: 19-Jun-2021
https://dl.acm.org/doi/10.1145/3453483.3454042
Anglada MCanal RAragon JGonzalez A(2021)Fast and Accurate SER Estimation for Large Combinational Blocks in Early Stages of the DesignIEEE Transactions on Sustainable Computing10.1109/TSUSC.2018.28866406:3(427-440)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TSUSC.2018.2886640
Traiola MVirazel AGirard PBarbareschi MBosio A(2020)A Survey of Testing Techniques for Approximate Integrated CircuitsProceedings of the IEEE10.1109/JPROC.2020.2999613108:12(2178-2194)Online publication date: Dec-2020
https://doi.org/10.1109/JPROC.2020.2999613
Fabrício Filho JFelzmann IAzevedo RWanner L(2020)AxRAM: A lightweight implicit interface for approximate data accessFuture Generation Computer Systems10.1016/j.future.2020.07.029113(556-570)Online publication date: Dec-2020
https://doi.org/10.1016/j.future.2020.07.029
Felzmann ISusin MDuenha LAzevedo RWanner L(2020)ADeLeFuture Generation Computer Systems10.1016/j.future.2019.07.073102:C(245-258)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1016/j.future.2019.07.073
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