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，并导致更好地理解物理现象。这些应用程序需要更大的计算资源，这只能由新的并行计算机提供不断增加的能力和复杂性。 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。具有数十万（甚至数百万）处理器的Petascale机器增加了复杂性，新的复杂算法和多物理场应用也是如此。该项目正在开发一种新的并行编程方法，它建立在Charm++框架的自动资源管理和可组合性的基础上。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，同时仍然覆盖了重要的应用类别。 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，为开发未来的千万亿次应用程序提供了强大的环境。正在开发一个编译器框架，它提供了一个共同的表示，并促进模型之间的兼容性。此外，该愿景还包括由库支持的抽象，用于通常需要的数据类型和功能。这些抽象将支持特定领域的框架并与之互操作。该项目的结果将使大型计算科学家和工程师社区能够相对轻松地利用千万亿次机器，以便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.