Verifying quantitative reliability for programs that execute on unreliable hardware

Michael Carbin, Sasa Misailovic, Martin C. Rinard
2013 SIGPLAN notices  
Creative Commons Attribution-Noncommercial-Share Alike Detailed Terms http://creativecommons.org/licenses/by-nc-sa/4.0/ The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. 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
more » ... ensive, 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.
doi:10.1145/2544173.2509546 fatcat:6pujz4n4jnhhbhnl6gzrraitb4