SHF:Small:Performance Portable Parallel Programming on Extremely Heterogeneous Systems

当今部署的计算机越来越复杂，因为它们的设计者努力提高执行计算的速度，同时保持甚至降低功耗。 其中许多包括节能加速器装置。调整现有的应用程序，使它们能够在配置了这种设备的新计算机系统上良好地执行，是一项劳动密集型和容易出错的活动，需要大量的专业知识。 此外，除非使用可移植的标准，否则可能需要为不同的硬件创建不同版本的程序。这样做所需的努力可能会延迟甚至阻止许多代码充分利用新系统。该项目将学习如何有效地利用机器学习方法来帮助自动化适应过程。具体来说，它将学习如何修改已经在多核平台上运行的应用程序，以便它们可以有效地利用加速器设备。 同时，研究开发在提高应用程序性能的背景下利用机器学习的最佳实践。本项目将研究开发基于机器学习（ML）的策略和技术，以识别和提取技术应用程序中适合映射到异构架构上配置的设备的代码区域。此外，它还将开发管理结果代码执行所需的运行时技术。为了实现这一目标，该项目将重点关注已经并行化的应用程序代码，以利用广泛采用的便携式行业标准OpenMP的多个处理核心，并将使用和扩展最新的OpenMP规范的功能，以表达设备代码和数据映射的方式是可移植的，并允许随后的手动优化。在代码中嵌入关键选择将有助于性能的可移植性。这项研究的一个关键要素是研究最先进的机器学习方法，包括经典的机器学习和深度学习技术，以及它们对增强编译器和工具的适用性。 在编译器中使用它们的相对优点的探索包括如何将编译器问题表示为回归或分类问题。研究还将研究代码表示和生成足够数据以训练高质量ML模型的方法。 一组基准和迷你应用程序将用于指导和评估研究。该项目将参与OpenMP语言委员会的工作，将通过开源LLVM基础设施提供实际结果，将为教学和培训材料做出贡献，并将利用这一努力丰富与HBCU的持续合作。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The computers that are deployed today are increasingly complex as their designers strive to increase the speed with which computations are performed, while simultaneously maintaining or even reducing their power consumption. Many of them include energy-efficient accelerator devices. Adapting existing application programs so that they can execute well on new computer systems where such devices are configured is a labor-intensive and error-prone activity that requires significant expertise. Moreover, unless portable standards are used, different versions of a program may need to be created for different hardware. The effort required to do so may delay, or even prevent, many codes from fully exploiting new systems. This project will learn how to effectively utilize Machine Learning methods to help automate the adaptation process. Specifically, it will learn how to modify applications that already run on multicore platforms so that they can effectively exploit accelerator devices. At the same time, it will study and develop best practices with respect to utilizing Machine Learning in the context of improving the performance of applications.This project will study and develop Machine Learning (ML)-based strategies and techniques to identify and extract code regions in technical applications that are suitable for mapping to the devices configured on a heterogeneous architecture. It will moreover develop the runtime technology needed to manage the execution of the resulting code. To accomplish this, the project will focus on application codes that have been parallelized to exploit multiple processing cores using the widely adopted, portable industry standard OpenMP and will use and extend features of the most recent OpenMP specification to express the device code and data mappings in a manner that is portable and permits subsequent manual optimization. The embedding of key choices in the code will aid performance portability. A key element of this research is the study of state-of-the-art ML methods, including classical ML and Deep-Learning techniques, with respect to their suitability for enhancing compilers and tools. An exploration of their relative merits for use in the compiler includes how to represent a compiler problem as a regression or classification problem. Research will also study approaches to code representation and the generation of sufficient data to train quality ML models. A set of benchmarks and mini-apps will be used to guide and evaluate the research. The project will participate in the work of the OpenMP Language Committee, will make practical results available via the open source LLVM infrastructure, will contribute to teaching and training materials, and will use this effort to enrich an ongoing collaboration with an HBCU.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.