CAREER: Understanding and Combating Numerical Bugs for Reliable and Efficient Software Systems

职业：理解和对抗数字错误以实现可靠和高效的软件系统

• 批准号: 1750983
• 负责人: Cindy Rubio Gonzalez
• 金额: $ 53.74万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750983&HistoricalAwards=false
• 关键词: CAREER; Understanding; Combating; Numerical; Bugs

The use of numerical software has grown rapidly over the past few years. From machine learning to safety-critical systems, a large variety of applications today make heavy use of floating point. Unfortunately, floating point introduces imprecision in numericalcalculations. Analyzing, testing, and optimizing floating-point programs are difficult tasks. There is a large variety of numerical errors that can occur in such programs, including extreme sensitivity to roundoff, incorrectly handled exceptions, and nonreproducibility. This has led to numerous software bugs that have caused catastrophic failures. The goal of this research is to understand and combat numerical bugs. The intellectual merits are to advance the state of the art in analysis, testing and optimization of numerical software, and in extending these techniques to new domains beyond scientific applications. The importance of the research lies in the impact of the developed techniques and tools on improving the reliability and performance of real-world numerical programs, on which many other applications depend.This research develops program analysis techniques and tools to (1) find frequent and impactful numerical bugs in programs, (2) proposefixes for these bugs, and (3) optimize numerical programs in different application domains to improve their performance. The research is driven by empirical studies that encompass several aspects of numerical software. First, a large-scale empirical study of numerical software is conducted to categorize real-world numerical bugs and their fixes. Second, an empirical study of test suites for numerical software is conducted to determine the effectiveness of testing in real-world numerical software. Based on the observations made through these empirical studies, a series of dynamic and static analyses are designed to detect and fix a variety of numerical bugs. These analyses are made available as part of an analysis and testing framework for numerical software. Novel precision tuning techniques are developed to enable scalable optimizations that lead to higher speedups, and to extend the scope of precision tuning to new application domains such as machine learning. The research has strong broader impacts in education and outreach. These include the development of new courses on software engineering and testing with a focus on numerical software, a Computer Science summer boot camp, and a mentoring program for underrepresented minorities especially focused on Latino students.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.

在过去的几年里，数字软件的使用迅速增长。从机器学习到安全关键型系统，当今各种各样的应用程序都大量使用浮点。不幸的是，浮点数在数值计算中引入了不精确性。分析、测试和优化浮点程序是一项艰巨的任务。在这样的程序中可能会出现各种各样的数值错误，包括对循环的极端敏感性，错误处理的异常和不可再现性。这导致了许多软件错误，导致了灾难性的故障。这项研究的目的是了解和打击数字错误。智力的优点是推进最先进的分析，测试和优化的数值软件，并在扩展这些技术到新的领域超出科学应用。研究的重要性在于所开发的技术和工具对提高真实世界数值程序的可靠性和性能的影响，许多其他应用程序依赖于这些技术和工具。这项研究开发了程序分析技术和工具，以（1）发现程序中频繁和有影响力的数值错误，（2）提出这些错误的修复方法，（3）优化不同应用领域的数值计算程序，提高其性能。这项研究是由实证研究驱动的，包括数值软件的几个方面。首先，数值软件进行了大规模的实证研究，分类现实世界的数值错误和他们的修复。其次，数值软件测试套件进行实证研究，以确定在现实世界的数值软件测试的有效性。基于这些实证研究所取得的观察，一系列的动态和静态分析，旨在检测和修复各种数值错误。这些分析可作为数值软件的分析和测试框架的一部分。开发新型精确调优技术是为了实现可扩展的优化，从而实现更高的加速比，并将精确调优的范围扩展到机器学习等新应用领域。这项研究在教育和推广方面产生了广泛的影响。其中包括开发软件工程和测试的新课程，重点是数字软件，计算机科学夏令营靴子，并为代表性不足的少数民族，特别是拉丁美洲学生的辅导计划。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Efficient Generation of Error-Inducing Floating-Point Inputs via Symbolic Execution

• DOI: 10.1145/3377811.3380359
• 发表时间: 2020-06
• 期刊: 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)
• 作者: Hui Guo;Cindy Rubio-González

PLINER: Isolating Lines of Floating-Point Code for Compiler-Induced Variability

• DOI: 10.1109/sc41405.2020.00053
• 发表时间: 2020-11
• 期刊: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: Hui Guo;I. Laguna;Cindy Rubio-González

Exploiting community structure for floating-point precision tuning

• DOI: 10.1145/3213846.3213862
• 发表时间: 2018-07
• 期刊: Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis
• 作者: Hui Guo;Cindy Rubio-González

Effective error-specification inference via domain-knowledge expansion

• DOI: 10.1145/3338906.3338960
• 发表时间: 2019-08
• 期刊: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: Daniel DeFreez;Haaken Martinson Baldwin;Cindy Rubio-González;Aditya V. Thakur

Detecting and reproducing error-code propagation bugs in MPI implementations

• DOI: 10.1145/3332466.3374515
• 发表时间: 2020-02
• 期刊: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
• 作者: Daniel DeFreez;Antara Bhowmick;I. Laguna;Cindy Rubio-González