Title

FT-ISort: Efficient Fault Tolerance for Introsort

Abstract

Introspective sorting is a ubiquitous sorting algorithm which underlies many large scale distributed systems. Hardware-mediated soft errors can result in comparison and memory errors, and thus cause introsort to generate incorrect output, which in turn disrupts systems built upon introsort; hence, it is critical to incorporate fault tolerance capability within introsort. This paper proposes the first theoretically-sound, practical fault tolerant introsort with negligible overhead: FT-iSort. To tolerate comparison errors, we use minimal TMR protection via exploiting the properties of the effects of soft errors on introsort. This algorithm-based selective protection incurs far less overhead than nave TMR protection designed to protect an entire application. To tolerate memory errors that escape DRAM error correcting code, we propose XOR-based re-execution. We incorporate our fault tolerance method into the well-known parallel sorting implementation HykSort, and we find that fault tolerant HykSort incurs negligible overhead and obtains nearly the same scalability as unprotected HykSort.

Meeting Name

International Conference for High Performance Computing, Networking, Storage and Analysis, SC '19 (2019: Nov. 17-19, Denver, CO)

Department(s)

Computer Science

Comments

The material was supported by the U.S. Department of Energy, Office of Science, under contract DE-AC02-06CH11357, and supported by the National Science Foundation under Grant No. 1619253. This work was also supported by National Science Foundation CCF 1513201 and National Key Research and Development Programs No. 2017YFB0202100.

Keywords and Phrases

Algorithm based Fault Tolerance; Comparison Errors; Fault Tolerant Sorting; Introsort; Soft Errors

International Standard Book Number (ISBN)

978-145036229-0

International Standard Serial Number (ISSN)

2167-4329; 2167-4337

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

17 Nov 2019

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