CSR---AES: Application Development for Architecturally Diverse Computing Systems

针对计算科学问题的性能改进的一种方法是使用诸如现场可编程门阵列和图形处理单元之类的替代计算引擎来执行应用的部分。我们将这样的系统称为架构上多样化的计算系统。 我们正在通过在两个重要领域取得重大进展来应对架构多样化计算系统的有效应用程序开发的挑战：1）编译器和2）性能监视器和调试器。 首先，我们正在研究编译技术，用于将分区的应用程序映射到不同系统的不同计算资源上，并支持这些资源之间所需的通信。 这项调查包括对常用库的遗留API的自动化支持。 其次，我们正在建立一个性能监控和调试环境，具有以下属性的不同系统：它是非侵入性的应用程序被监视，它支持有关罕见事件的数据收集。 这项研究的结果将大大提高在不同架构的计算平台上设计、开发和成功部署广泛的通用应用程序的能力。提高计算能力将对利用计算科学技术的每一个科学学科产生巨大的影响。计算科学已经对自然科学、国防、天气预报、以及其他许多领域。 这项研究的目的是明确地提高计算科学家的能力，以实现他们的目标。

One approach to performance improvements for computational science problems is the use of alternative computing engines, such as field programmable gate arrays and graphics processing units, to execute portions of an application. We refer to such systems as architecturally diverse computing systems. We are attacking the challenge of effective application development for architecturally diverse computing systems by making significant progress in two essential areas: 1) compilers and 2) performance monitors and debuggers. First, we are investigating compilation techniques for mapping partitioned applications onto the disparate computational resources of a diverse system and supporting the required communications between those resources. This investigation includes the automated support of legacy APIs for commonly used libraries. Second, we are building a performance monitoring and debugging environment for diverse systems that has the followingproperties: it is non-intrusive with respect to the application being monitored and it supports data collection concerning rare events. The result of this research will be a significantly increased ability to design, develop, and successfully deploy a wide set of general-purpose applications on architecturally diverse computational platforms.Improved computational capabilities will have a dramatic impact on every scientific discipline that utilizes computational science techniques.Computational science has already had a significant, broad impact on the natural sciences, national defense, weather forecasting, and a host of other areas. This research is aimed explicitly at improving the ability of computational scientists to accomplish their goals.