Simplifying Parallel Programming for CSE Applications using a Multi-Paradigm Approach

科学应用可以模拟药物与蛋白质的相互作用，预测纳米材料的行为，模拟气候，并导致对物理现象的更好理解。这些应用程序需要越来越多的计算资源，而这些资源只能由性能和复杂性不断提高的新型并行计算机提供。只有克服高效并行编程的复杂性，并行计算才能带来新的突破。然而，开发并行应用程序仍然比串行开发困难得多。拥有数十万（甚至可能数百万）处理器的千兆级机器，以及新的复杂算法和多物理场应用程序，都增加了复杂性。这个项目正在开发一种新的并行编程方法，它建立在自动资源管理和charm++框架的可组合性之上。这种方法包括开发多个单独不完整的编程模型。每个模型都简化了并行编程，同时仍然涵盖了重要的应用程序类别。这个可互操作的模型集合，由包括Adaptive MPI和Charm++在内的完整模型支持，为开发未来的千兆级应用程序提供了一个强大的环境。正在开发一个编译器框架，它提供了一个通用的表示并促进了模型之间的兼容性。此外，该愿景还包括库支持的用于常用数据类型和功能的抽象。这些抽象将支持领域特定框架并与之互操作。这个项目的成果将使庞大的计算科学家和工程师社区能够相对容易地利用千万亿次机器，从而在科学发现和工程设计方面取得突破。

Scientific applications can model interactions of medicines with proteins, predict the behavior of nano-materials, model the climate, and lead to better understanding of physical phenomenon. These applications demand ever greater computational resources, which can only be supplied by new parallel computers with ever increasing capability and complexity. Parallel computing can bring about new breakthroughs only if the complexity of efficient parallel programming can be overcome. Yet developing parallel applications remains significantly more difficult than serial development. Petascale machines with hundreds of thousands(and possibly millions) of processors add to the complexity, as do new sophisticated algorithms and multi-physics applications. This project is developing a new approach to parallel programming which builds upon the automatic resource management and composibility of the Charm++ framework. This approach includes development of multiple, individually incomplete, programming models. Each model simplifies parallel programming while still covering significant categories of applications. This collection of interoperable models, supported by complete models including Adaptive MPI and Charm++, provides a powerful environment for developing future petascale applications. A compiler framework is being developed which provides a common representation and facilitates compatibility between models. In addition, the vision includes abstractions supported by libraries for commonly needed data types and functionalities. These abstractions will support and interoperate with domain specific frameworks. The results of this project will enable the large community of computational scientists and engineers to harness petascale machines with relative ease in order to generate breakthroughs in scientific discovery and engineering design.