Deterministic Parallel Programming for High Performance Computing

高性能计算的硬件正在以无情的步伐前进：在不久的将来，我们可以期待看到具有超过一百万个并发执行线程的系统，以及对全局内存访问的硬件支持。另一方面，我们今天继续使用我们在过去15年中使用的相同的低级并行消息传递库。这导致用户生产力降低，并且不能很好地利用现代通信硬件。我们建议探索新的语言设计，解决这两个问题。它被普遍接受，在共享内存模型编程更容易（至少在初始程序开发）：每个线程访问每个变量的能力，使用一个共同的名称空间，减少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性代码（其中可能有不同的结果），并且不保护用户免受内存竞争（其中对共享变量的访问不同步）。这会导致不可重现且难以检测的细微错误。此外，共享内存语言提供有限的支持，局部控制？导致缺乏可扩展性。在科学计算中很少需要非确定性，可扩展性是必不可少的。PI相信有可能开发出支持高性能计算中使用的大多数编程模式的语言;将提供共享内存模型的便利;将通过设计防止不确定性并检测竞争;并将提供用户对局部性的控制。拟议的研究将探讨这种语言的设计和所需的支持技术。

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.