{"title":"DSGEN","authors":"Xiaofan Sun, Rajiv Gupta","doi":"10.1145/3447818.3460962","DOIUrl":null,"url":null,"abstract":"Concolic testing combines concrete execution with symbolic execution along the executed path to automatically generate new test inputs that exercise program paths and deliver high code coverage during testing. The GKLEE tool uses this approach to expose data races in CUDA programs written for execution of GPGPUs. In programs employing concurrent dynamic data structures, automatic generation of data structures with appropriate shapes that cause threads to follow selected, possibly divergent, paths is a challenge. Moreover, a single non-conflicting data structure must be generated for multiple threads, that is, a single shape must be found that simultaneously causes all threads to follow their respective chosen paths. When an execution exposes a bug (e.g., a data race), the generated data structure shape helps the programmer understand the cause of the bug. Because GKLEE does not permit pointers that construct dynamic data structures to be made symbolic, it cannot automatically generate data structures of different shapes and must rely on the user to write code that constructs them to exercise desired paths. We have developed DSGEN for automatically generating non-conflicting dynamic data structures with different shapes and integrated it with GKLEE to uncover and facilitate understanding of data races in programs that employ complex concurrent dynamic data structures. In comparison to GKLEE, DSGEN increases the number of races detected from 10 to 25 by automatically generating a total of 1,897 shapes in implementations of four complex concurrent dynamic data structures -- B-Tree, Hash-Array Mapped Trie, RRB-Tree, and Skip List.","PeriodicalId":73273,"journal":{"name":"ICS ... : proceedings of the ... ACM International Conference on Supercomputing. International Conference on Supercomputing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICS ... : proceedings of the ... ACM International Conference on Supercomputing. International Conference on Supercomputing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3447818.3460962","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Concolic testing combines concrete execution with symbolic execution along the executed path to automatically generate new test inputs that exercise program paths and deliver high code coverage during testing. The GKLEE tool uses this approach to expose data races in CUDA programs written for execution of GPGPUs. In programs employing concurrent dynamic data structures, automatic generation of data structures with appropriate shapes that cause threads to follow selected, possibly divergent, paths is a challenge. Moreover, a single non-conflicting data structure must be generated for multiple threads, that is, a single shape must be found that simultaneously causes all threads to follow their respective chosen paths. When an execution exposes a bug (e.g., a data race), the generated data structure shape helps the programmer understand the cause of the bug. Because GKLEE does not permit pointers that construct dynamic data structures to be made symbolic, it cannot automatically generate data structures of different shapes and must rely on the user to write code that constructs them to exercise desired paths. We have developed DSGEN for automatically generating non-conflicting dynamic data structures with different shapes and integrated it with GKLEE to uncover and facilitate understanding of data races in programs that employ complex concurrent dynamic data structures. In comparison to GKLEE, DSGEN increases the number of races detected from 10 to 25 by automatically generating a total of 1,897 shapes in implementations of four complex concurrent dynamic data structures -- B-Tree, Hash-Array Mapped Trie, RRB-Tree, and Skip List.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Accelerating BWA-MEM Read Mapping on GPUs. Dynamic Memory Management in Massively Parallel Systems: A Case on GPUs. Priority Algorithms with Advice for Disjoint Path Allocation Problems From Data of Internet of Things to Domain Knowledge: A Case Study of Exploration in Smart Agriculture On Two Variants of Induced Matchings
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1