SI2-SSE: Scalable Multifaceted Graphical Processing Unit (GPU) Program Debugging

SI2-SSE：可扩展多方面图形处理单元 (GPU) 程序调试

• 批准号: 1535032
• 负责人: Ganesh Gopalakrishnan
• 金额: $ 41.75万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1535032&HistoricalAwards=false
• 关键词: SI2; SSE; Scalable; Multifaceted; Graphical

Modern scientific research crucially depends on software simulations that help model scientific phenomena, and accelerate the process of discoveries, and communal result sharing. With the availability of affordable computational accelerators known as GPUs, the scientific community has begun migrating their existing CPU codes as well as creating new codes targeting GPUs. Unfortunately, this has resulted in a situation where the generated scientific results do not often agree across CPUs and GPUs. This exacerbates the danger of drawing wrong conclusions in crucial areas such as physics, weather simulations, drug discovery, and engineering computations. This project offers a combination of existing and new techniques in dissecting scientific experiments conducted through simulations, obtaining believable results, finding the root causes of varying results, and developing best practices to ensure higher result fidelity. Its techniques have special emphasis on GPUs, given their often poorly specified and evolving nature.Result variability has many causes, including evolving, incorrect, or ambiguous specifications of computer hardware and software, racing data accesses, varying floating point precision standards, and incorrect result association within compound computational steps. This project develops methods that help a scientist systematically search through and eliminate these causes, thus accelerating the process of debugging result variability. The produced tools and exemplars of known erroneous behaviors allow a scientist to avoid the use of incorrect specifications, isolate and eliminate data races, and isolate and eliminate unreliable numerical steps. It also develops methods that help a scientist maintain focus on their basic scientific pursuits while still keeping up with technology evolution. It trains students in critical software engineering techniques that help the nation build the talent pool necessary for the extreme scale computing era.The project will combine six research thrusts (GPU concurrency; challenge problems and develop user interfaces; pedagogy for domain scientists; improved GPU concurrency debugging tool support; more reproducible simulation results; and evolving and scaling tools with standards) to build and deliver open source software that incorporates proven stress-testing methods into tools; builds challenge problems, supports formalization support, and designs the user interface; delivers demos, books, and tutorials that help illustrate concurrency nuances; exploits symbolic analysis for input generation in mixed formal and GPU runs; develops stress testing inputs for round-off errors and separable verification to root-cause roundoff; and componentizes the symbolic verifier to enable parallelism, targeting from new APIs.

现代科学研究在很大程度上依赖于帮助模拟科学现象的软件模拟，并加速发现和公共结果共享的进程。随着负担得起的计算加速器(称为GPU)的出现，科学界已经开始迁移他们现有的CPU代码，并创建针对GPU的新代码。不幸的是，这导致了一种情况，即生成的科学结果在CPU和GPU之间往往不一致。这加剧了在物理、天气模拟、药物发现和工程计算等关键领域得出错误结论的危险。该项目结合了现有技术和新技术，对通过模拟进行的科学实验进行了剖析，获得了可信的结果，找到了不同结果的根本原因，并开发了最佳实践，以确保更高的结果保真度。它的技术特别侧重于图形处理器，因为它们经常被错误地指定和演变。结果变化有许多原因，包括计算机硬件和软件的演变、不正确或不明确的规范、竞相的数据访问、不同的浮点精度标准以及复合计算步骤中的不正确的结果关联。该项目开发了帮助科学家系统地搜索和消除这些原因的方法，从而加快了调试结果的可变性的进程。所产生的工具和已知错误行为的样本允许科学家避免使用不正确的规范、隔离和消除数据竞争以及隔离和消除不可靠的数值步骤。它还开发了一些方法，帮助科学家在保持专注于他们的基本科学追求的同时，仍然跟上技术发展的步伐。它在关键软件工程技术方面培训学生，帮助国家建立极端规模计算时代所需的人才库。该项目将结合六个研究主题(GPU并发性；挑战问题和开发用户界面；领域科学家的教学；改进的GPU并发调试工具支持；更具重复性的模拟结果；以及随标准发展和扩展工具)，以构建和交付开放源码软件，将经过验证的压力测试方法纳入工具中；构建挑战问题，支持形式化支持，并设计用户界面；提供有助于说明并发性的演示、书籍和教程；利用符号分析在正式和GPU混合运行中生成输入；开发用于四舍五入误差的压力测试输入和用于根本原因四舍五入的可分离验证；并对符号验证器进行组件化，以实现并行性，从新的API瞄准目标。