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.

由于对计算速度以及能源效率的不断增长的需求，计算机架构的快速进步是必要的。不幸的是，这些进步需要对现有软件及其重新编译进行大量改编，并且在科学模拟和工程领域中应用于软件时，这些软件的数字输出可能会改变。这个问题引起了重大关注，因为整个科学和工程企业都基于先前贡献之外的可再现结果。该研究项目开发了检测和隔离负责更改答案的软件组件的方法，并根据需要重写的软件例程产生可行的证据。这样可以确保科学软件可以真正成为一家人的资产，该资产可以为几代研究提供服务，并且在硬件变化时不会每三年避免每三年撤离。该项目开发了使用不同的编译器来编译用户软件应用程序或库的方法，并在不同的平台上执行。 。考虑到一系列接受测试，这项工作中开发的工具（统称为“ Indy”，表示“快速安全的编译器标志”）有助于检测那些产生不可接受的偏差答案的组件。然后，采用代码二次方法，Indy工具根本原因是单个文件级别甚至单个函数符号的可变性来源。鉴于Indy使用的方法可以是计算密集型的，因此该项目还探索了机器学习方法和其他静态分析方法，以避免批发重新编译和重新执行。 Indy工具是在公共存储库中维护的，调查团队与用户组保持牢固的联系，以确保工具采用，反馈和改进。该项目将通过确保感兴趣的研究人员能够为代码库做出贡献，从而将Indy释放为开放源代码，并在Indy周围建立一个用户社区。这将允许该项目的更大增长。 NSF高级网络基础设施办公室的软件集群特别感兴趣，该奖项正在共同资助该奖项。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响评论来评估值得支持的。标准。

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

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.

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