SHF: Small: Indy: Toward Safe and Fast Compiler Flags

SHF：小：Indy：迈向安全快速的编译器标志

• 批准号: 1817073
• 负责人: Ganesh Gopalakrishnan
• 金额: $ 48.14万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1817073&HistoricalAwards=false
• 关键词: SHF; Small; Indy; Toward; Safe

Rapid advances in computer architecture are necessitated by the growing demand for computing speed and also energy efficiency. Unfortunately, these advances require significant adaptations of existing software and their recompilation, and when applied to software in scientific simulations and engineering domains, the numeric outputs of these software can change. This issue is of significant concern, as the whole scientific and engineering enterprise rests on reproducible results building on top of prior contributions. This research project develops methods to detect and isolate those software components responsible for changing answers, and produces actionable evidence in terms of the software routines that need to be rewritten. This ensures that scientific software can truly become an asset that serves generations of research, and not be obviated every three or so years when hardware changes.The project develops methods to compile a users' software application or library using different compilers and executing on different platforms. Given a collection of acceptance tests, the tools developed in this work (collectively called "Indy", connoting "Fast and Safe Compiler Flags") help detect those components that produce unacceptably deviant answers. Then, employing code bisection methods, the Indy tools root-cause the source of variability to the level of single file or even to a single function symbol. Given that the methods used by Indy can be computationally intensive, the project also explores machine-learning methods and other static analysis methods to avoid wholesale recompilation and re-execution. The Indy tools are maintained on public repositories, and the investigative team maintains strong connections with user-groups to ensure tool adoption, feedback, and refinement. The project will release Indy as open source and build a user community around Indy by ensuring that interested researchers are able to contribute to the code-base. This will allow a wider growth of the project. This aspect is of special interest to the software cluster in the NSF Office of Advanced Cyberinfrastructure, which is co-funding this award.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

计算机体系结构的快速发展是由于对计算速度和能源效率不断增长的需求所必需的。不幸的是，这些进展需要对现有软件及其重新编译进行重大调整，当应用于科学模拟和工程领域的软件时，这些软件的数字输出可能会发生变化。这一问题非常令人关切，因为整个科学和工程事业依赖于建立在先前贡献基础上的可重复的结果。这个研究项目开发了一些方法来检测和隔离那些负责更改答案的软件组件，并根据需要重写的软件例程产生可操作的证据。这确保了科学软件能够真正成为服务于几代研究的资产，而不是每隔三年左右硬件发生变化就被淘汰。该项目开发了使用不同的编译器编译用户的软件应用程序或库并在不同平台上执行的方法。在给定一组验收测试的情况下，在这项工作中开发的工具(统称为“Indy”，暗指“快速和安全的编译器标志”)有助于检测那些产生不可接受的异常答案的组件。然后，采用代码一分为二的方法，Indy工具将可变性的根源根源于单个文件甚至单个函数符号的级别。考虑到Indy使用的方法可能是计算密集型的，该项目还探索了机器学习方法和其他静态分析方法，以避免大规模重新编译和重新执行。Indy工具维护在公共存储库上，调查团队与用户组保持着牢固的联系，以确保工具的采用、反馈和改进。该项目将Indy作为开源发布，并通过确保感兴趣的研究人员能够为代码库做出贡献来围绕Indy建立一个用户社区。这将使该项目有更广泛的增长。这方面对NSF高级网络基础设施办公室的软件集群特别感兴趣，该办公室是该奖项的共同资助者。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Keeping science on keel when software moves

• DOI: 10.1145/3382037
• 发表时间: 2021-01
• 期刊: Communications of the ACM
• 影响因子: 22.7
• 作者: D. Ahn;A. Baker;Michael Bentley;Ian Briggs;G. Gopalakrishnan;D. Hammerling;I. Laguna;Gregory L. Lee;Daniel J. Milroy;M. Vertenstein

GoAT: Automated Concurrency Analysis and Debugging Tool for Go

GoAT：Go 的自动并发分析和调试工具

• DOI: 10.1109/iiswc53511.2021.00023
• 发表时间: 2022
• 期刊: 2021 IEEE International Symposium on Workload Characterization (IISWC
• 作者: Taheri, Saeed;Gopalakrishnan, Ganesh
• 通讯作者: Gopalakrishnan, Ganesh

Robustness Analysis of Loop-Free Floating-Point Programs via Symbolic Automatic Differentiation

通过符号自动微分进行无循环浮点程序的鲁棒性分析

• 发表时间: 2021
• 期刊: IEEE Cluster 2021
• 作者: Das, Arnab;Tirpankar, Tanmay;Gopalakrishnan, Ganesh;Krishnamoorthy, Sriram
• 通讯作者: Krishnamoorthy, Sriram

DiffTrace: Efficient Whole-Program Trace Analysis and Diffing for Debugging

DiffTrace：高效的全程序跟踪分析和差异调试

• DOI: 10.1109/cluster.2019.8891027
• 发表时间: 2019
• 期刊: 2019 IEEE International Conference on Cluster Computing (CLUSTER
• 作者: Taheri, Saeed;Briggs, Ian;Burtscher, Martin;Gopalakrishnan, Ganesh
• 通讯作者: Gopalakrishnan, Ganesh

Multi-Level Analysis of Compiler-Induced Variability and Performance Tradeoffs

编译器引起的可变性和性能权衡的多级分析

• DOI: 10.1145/3307681.3325960
• 发表时间: 2019
• 期刊: HPDC '19 Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing
• 作者: Bentley, Michael;Briggs, Ian;Gopalakrishnan, Ganesh;Ahn, Dong H.;Laguna, Ignacio;Lee, Gregory L.;Jones, Holger E.
• 通讯作者: Jones, Holger E.