SI2-SSI: Collaborative Research: A Glass Box Approach to Enabling Open, Deep Interactions in the HPC Toolchain

随着越来越大的多核处理器和强大的加速器设备，并行计算已经进入主流。这些计算引擎，加上更快的互连结构的更紧密集成，是下一代高端计算（HEC）机器的驱动力。然而，HEC机器的计算潜力只能通过高效的并行程序开发和高效的并行执行来实现。该项目使应用程序开发人员能够提高未来HEC机器的性能，以用于其科学和工程过程。该项目通过“黑盒”工具和服务挑战当前的并行应用程序开发模型。相反，该项目提供了一个开放、透明的软件基础设施--一个玻璃盒系统--用于创建和调优大规模的并行应用程序。 “开放”用于创建和评估千万亿次和百万次规模代码的工具和服务，涉及开发接口和方法，使工具内部信息可用于新的性能管理服务，提高开发人员的生产力和代码效率。 将开发用于分析程序信息、性能数据和工具知识的方法。由此产生的Glass Box系统将允许开发人员更好地评估其并行代码的性能。 工具创建者可以使用性能数据来创建新的分析和优化技术。系统开发人员还可以在运行时更好地管理多核和机器资源，使用JIT编译和二进制代码编辑来利用不断发展的硬件。 该项目将与“Keeneland”NSF Track II机器和我们的工业伙伴合作，创造新的性能监测工具、编译方法和系统级资源管理技术。这项工作是由今天的千万亿次机器上运行的大规模代码驱动的。 其更广泛的影响来自于与技术开发人员和应用科学家的互动，以及其在三所拥有不同学生群体的大学的基地。

Parallel computing has entered the mainstream with increasingly large multicore processors and powerful accelerator devices. These compute engines, coupled with tighter integration of faster interconnection fabrics, are drivers for the next-generation high end computing (HEC) machines. However, the computing potential of HEC machines is delivered only through productive parallel program development and efficient parallel execution. This project enables application developers to improve performance on future HEC machines for their scientific and engineering processes. This project challenges the current model for parallel application development via "black box" tools and services. Instead, the project offers an open, transparent software infrastructure -- a Glass Box system -- for creating and tuning large-scale, parallel applications. `Opening up' the tools and services used to create and evaluate peta- and exa-scale codes involves developing interfaces and methods that make tool-internal information and available for new performance management services that improve developer productivity and code efficiency.The project will explore the information that can be shared 'across the software stack'. Methods will be developed for analyzing program information, performance data and tool knowledge. The resulting Glass Box system will allow developers to better assess the performance of their parallel codes. Tool creators can use the performance data to create new analysis and optimization techniques. System developers can also better manage multicore and machine resources at runtime, using JIT compilation and binary code editing to exploit the evolving hardware. Working with the `Keeneland' NSF Track II machine and our industry partners, the project will create new performance monitoring tools, compiler methods and system-level resource management techniques. The effort is driven by the large-scale codes running on today's petascale machines. Its broader impact is derived from the interactions with technology developers and application scientists as well as from its base in three universities with diverse student populations.