Collaborative Research: Performance Toolset for Dynamic Optimization of High-End Hybrid Applications

目前的高端应用程序通常只利用了大型平台理论性能的一小部分。硬件、系统软件、编程接口和算法之间的交互非常复杂，对于开发混合MPI+OpenMP Fortran/C/ c++应用程序具有挑战性。新一代的机器将更加复杂，利用它们的应用程序也将更加复杂。典型的应用程序开发和调优场景涉及手动和单独使用编译器和性能工具，以及基于费力地从它们的输出中收集到的见解对程序进行修改。在这个建议中，我们打算通过为程序优化创建一个集成的环境来提高应用程序开发和调优过程的质量，该环境减少了现有方法的手工劳动和猜测。我们将开发策略和相应的接口，使应用程序开发人员、编译器和性能工具能够协作，根据各种反馈来源（包括来自.offline的性能数据）生成优化的代码。开发可以从。online运行。生产运行。我们将构建和部署一个灵活的、可工作的系统，该系统结合了强大的、现有的、开源的软件。一个编译器，一个程序分析工具和两个具有互补功能的性能工具-整合到一个统一的环境中，用于协作的静态和动态应用程序调优。我们的应用程序合作伙伴提供的不同复杂性的应用程序代码将激励我们的开发工作以及测试和演示我们的结果。其结果将是一个强大的集成环境，可用于通过程序监视、事件跟踪和/或提取硬件计数器信息来获取传统的性能数据，并获得对应用程序代码的静态或动态调优的支持。提出的环境集成了几种不同的现有技术，为优化混合MPI+OpenMP代码提供了新的支持。对混合编程模型的实验提供了支持。编译器用于优化代码的信息的范围被扩展到涵盖许多系统和应用程序级现象以及各种优化场景。工具之间的交互将有助于提供一种能够处理极端规模计算的方法。集成问题将以创建可部署、可扩展的系统为目标来解决。更广泛的影响这个项目的工具、想法和结果将免费分发给HPC社区，在国内和国际上都可以使用。除了结果的普遍传播外，该项目还与NCSA的性能工程专家有着密切的联系。这些工具将安装在NCSA系统上进行测试和评估，并将提供给其他用户。该研究汇集了编译器、性能工具和应用程序开发人员，丰富了研究生的研究经验，以创建一个全面的IT劳动力。PI活跃于OpenMP社区，项目团队与美国能源部和国防部有着密切的工作关系。所产生的新知识将整合到高级研究生课程中。

Current high-end applications usually exploit just a fraction of the theoretical performance of large platforms. Interactions among the hardware, system software, programming interface, and algorithm are extremely complex, and the implications for development of hybrid MPI+OpenMP Fortran/C/C++ applications are challenging. The emerging generation of machines will be even more complex, as will the applications that exploit them. Typical application development and tuning scenarios involve the manual and separate use of compilers and performance tools, and program modifications based upon insights laboriously gleaned from their output. In this proposal, we intend to raise the quality of the application development and tuning process by creating an integrated environment for program optimization that reduces the manual labor and guesswork of existing approaches. We will develop strategies and the corresponding interfaces that enable the application developer, compiler and performance tools to collaborate to generate optimized code based upon a variety of sources of feedback, including performance data from .offline. development runs as well as from .online. production runs. We will build and deploy a flexible, working system that combines robust, existing, open source software . a compiler, a program analysis tool and two performance tools with complementary features - into a single, coherent environment for collaborative static and dynamic application tuning. Application codes of varying complexity supplied by our application partner will motivate our development work as well as test and demonstrate our results. The result will be a powerful, integrated environment that can be used to obtain traditional performance data via program monitoring, event tracing and/or the extraction of hardware counter information, and to obtain support for the static or dynamic tuning of an application code. Intellectual Merit The proposed environment integrates several different existing technologies to provide a new level of support for optimizing hybrid MPI+OpenMP codes. Support for experimentation with the hybrid programming model is provided. The range of information that may be exploited by the compiler to optimize code is expanded to cover many system and application-level phenomena and a variety of optimization scenarios. Interactions between tools will facilitate the provision of an approach that is able to handle extreme-scale computations. Integration issues will be addressed with the goal of creating a deployable, extensible system. Broader Impacts The tools, ideas, and results of this project will be freely distributed and made available to the HPC community, nationally and internationally. Besides the general dissemination of results, the project has strong ties to performance engineering experts at NCSA. The tools will be installed on NCSA systems for testing and evaluation and will be made available to other users. The research brings together compiler, performance tool, and application developers, enriching the research experience of graduate students to create a well-rounded IT workforce. The PI is active in the OpenMP community and the project team has close working relationships with DOE and DOD. New knowledge generated will be integrated into advanced graduate coursework.