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

现代科学研究至关重要地依赖于软件模拟，这些软件模拟有助于为科学现象建模，加速发现过程，以及公共成果共享。随着廉价计算加速器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。这加剧了在物理学、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，通过模拟分析科学实验，获得可信的结果，找到不同结果的根本原因，并制定最佳实践，以确保更高的结果保真度。它的技术特别强调gpu，因为它们通常没有明确的规定和不断发展的性质。结果的可变性有很多原因，包括不断发展的、不正确的或模糊的计算机硬件和软件规范、快速的数据访问、不同的浮点精度标准，以及复合计算步骤中不正确的结果关联。本项目开发的方法可以帮助科学家系统地搜索并消除这些原因，从而加快调试结果可变性的过程。生成的工具和已知错误行为的示例允许科学家避免使用不正确的规范，隔离和消除数据竞争，隔离和消除不可靠的数值步骤。它还开发了一些方法，帮助科学家专注于他们的基础科学追求，同时仍然跟上技术的发展。它训练学生掌握关键的软件工程技术，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重点（GPU并发性；挑战问题和开发用户界面；领域科学家的教学方法；改进的GPU并发调试工具支持；更多可重复的模拟结果；以及使用标准发展和扩展工具）来构建和交付开源软件，将经过验证的压力测试方法集成到工具中；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细微差别的演示、书籍和教程；利用符号分析在混合形式和GPU运行中生成输入；开发压力测试输入，以解决舍入错误和可分离验证的根本原因舍入；并将符号验证器组件化以启用并行性，并针对新的api。

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.