CSR-PSCE, TM: A Declarative Approach to Managing the Complexity of Massively Parallel Programs

并行程序通常使用顺序表示独立进程的风格编写，并使用本地表达的消息传递作为数据交换的手段以及进程间同步的手段。从进程间的消息传递所隐含的网络关系中，产生了隐式数据结构和隐式算法——也就是说，没有显式全局表示的全局并行数据结构和全局并行算法。由于只限于创建并行程序的局部方法，实现大规模并行算法的正确性和性能正迅速超出我们的推理能力。为了解决这个缺点，这个项目开发了一种描述新的高级通信操作的声明性语言方法，一种用计算组合这些操作的方法，以及一种表达转换以优化结果组合的方法。该项目的实施计划是基于目前在大规模并行计算中最有效的技术的积极发展，即显式管理共享数据。在分布式内存情况下，这是通过消息传递实现的，但同样的原则也可以应用于共享内存情况。这个项目的方法可以为数据管理、通信和协调提供更高层次的表达性和抽象。此外，在通信和计算之间自然分离的关注点极大地简化了程序员的心智模型和实现工作。为了不因为这种划分而牺牲性能，该方案的组合和转换机制将允许以优化的方式（自动）结合通信和计算，从而实现高性能和高可伸缩性。为了促进实践程序员对所开发的范例和工具的采用，与教育的集成是必不可少的。因此，该项目将直接培训本科生、研究生和博士后研究人员，并将开发配套课程和材料，以培训学生和实践程序员。与大规模现实世界科学应用的密切合作将进一步增加这项工作的实际相关性。

Parallel programs are typically written using a style in which independent processes are expressed sequentially and use locally-expressed message passing as a means of data exchange as well as a means of inter-process synchronization. From the network relationships that are implied by messaging among processes arise implicit data structures and implicit algorithms - that is, global parallel data structures and global parallel algorithms for which there is no explicit global representation. Limited only to a local approach for creating parallel programs, achieving correctness and performance of large-scale parallel algorithms is rapidly moving beyond our reasoning abilities. To address this shortcoming, this project develops a declarative language approach for describing new high-level communication operations, a means for composing these operations with computations, and a means for expressing transformations for optimizing the resulting compositions.This project's implementation plan is based on aggressive evolution of the technology that is currently most effective in large-scale parallel computing - namely, explicitly managed shared data. In the distributed-memory case this is achieved with message passing, but the same discipline can be applied in the shared memory case as well. This project's approach may enable higher levels of expressiveness and abstraction for data management, communication, and coordination. Moreover, the separation of concerns that is naturally imposed between communication and computation greatly simplifies the mental model and implementation effort for programmers. In order not to sacrifice performance because of this division, this scheme's composition and transformation mechanisms will allow communication and computation to be (automatically) combined in an optimized fashion so that high performance and high scalability are achieved. To facilitate adoption by practicing programmers of the paradigms and tools that are developed, integration with education is essential. Accordingly, this project will directly train undergraduate, graduate, and post-doctoral researchers and will develop supporting curricula and materials to train both students and practicing programmers. Close collaboration with large-scale real-world scientific applications will further increase the practical relevance of this work.