Deterministic Parallel Programming for High Performance Computing

高性能计算的确定性并行编程

• 批准号: 0833128
• 负责人: Marc Snir
• 金额: $ 62.5万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0833128&HistoricalAwards=false
• 关键词: Deterministic; Parallel; Programming; Performance; Computing

Hardware for High-Performance Computing is advancing at a relentless pace: In the not too distant future we can expect to see systems with over a million of concurrently executing threads, with hardware support for global memory access. On the other hand, we continue to use today the same low-level parallel message passing libraries that we have used in the last 15 years. This causes lower user productivity and does not leverage well modern communication hardware. We propose to explore new language designs that address both problems.It is generally accepted that programming in a shared memory model is easier (at least for initial program development): the ability of each thread to access each variable, using a common name space, reduces much of the burden of distributed memory programming. On the other hand, shared memory programming languages generally allow users to write nondeterministic code (where different outcomes are possible) and do not protect the user from memory races (where accesses to shared variables are not synchronized). This results in subtle bugs that are not reproducible and hard to detect. Furthermore, shared memory languages provide limited support for locality control ? resulting in lack of scalability. Nondeterminism is rarely needed in scientific computing, and scalability is essential. The PIs believe it is possible to develop languages that will support the large majority of programming patterns used in high-performance-computing; will provide the convenience of a shared-memory model; will prevent, by design, nondeterminism and detect races; and will provide user control of locality. The proposed research will explore the design for such a language and the required support technologies.

用于高性能计算的硬件正在以无情的速度发展：在不久的将来，我们可以期望看到拥有超过一百万个并发执行线程的系统，并且硬件支持全局内存访问。另一方面，我们今天继续使用过去15年来使用的低级并行消息传递库。这会降低用户的工作效率，并且不能很好地利用现代通信硬件。我们建议探索解决这两个问题的新语言设计。人们普遍认为，在共享内存模型中编程更容易（至少对于初始程序开发来说是这样）：每个线程使用公共名称空间访问每个变量的能力大大减轻了分布式内存编程的负担。另一方面，共享内存编程语言通常允许用户编写不确定的代码（可能有不同的结果），并且不保护用户免受内存竞争（对共享变量的访问不同步）的影响。这将导致不可复制且难以检测的细微错误。此外，共享内存语言对局部性控制的支持有限。导致缺乏可伸缩性。在科学计算中很少需要不确定性，而可伸缩性是必不可少的。pi们相信开发支持高性能计算中使用的绝大多数编程模式的语言是可能的；将提供共享内存模型的便利性；将通过设计防止非决定论和检测种族；并将为用户提供位置控制。本研究将探讨这种语言的设计和所需的支持技术。