Collaborative Research: Frameworks: Performance Engineering Scientific Applications with MVAPICH and TAU using Emerging Communication Primitives

合作研究：框架：使用新兴通信原语的 MVAPICH 和 TAU 的性能工程科学应用

• 批准号: 2311830
• 负责人: Dhabaleswar Panda
• 金额: $ 90万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311830&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; Performance; Engineering

Earthquake hazards pose potentially life-threatening risks to communities and cause significant economic damage. Numerical simulations of earthquakes on large-scale supercomputers are emerging as key to guiding the infrastructure and policy decisions as a result of earthquake modeling. These seismic and other codes including simulations involving Fast Fourier Transform (FFT) distribute the processing across a large number of compute nodes in a supercomputer. Optimizing the communication between nodes is key to achieving good performance but it is a daunting task given the scale of execution. The MVAPICH communication library that implements the Message Passing Interface (MPI) and the TAU Performance System, a profiling tool to observe the communication, will be tightly coupled to assess the performance impact of tuning these codes during execution. These libraries will share key performance parameters and optimize the communication in these applications to improve the time to solution. Performance-engineered versions of these codes will help drive the next generation of earthquake forecasting and help improve our understanding of seismic events to reduce risks to population centers and the environment. The research will enable undergraduate and graduate curriculum advancements via research in pedagogy for High Performance Computing (HPC), Deep/Machine Learning, and Data Analytics courses. The results will also be disseminated to the collaborating organizations of the investigators to impact their HPC software applications.Emerging HPC systems---driven by many-core processors and accelerator architectures--- require innovations in existing infrastructure to deliver the best performance for science domains. The MPI 4.0 standard has also brought forward new opportunities for co-designing applications. These include partitioned point-to-point and collective operations, and neighborhood collectives. With these advances, there is a critical need to update the commonly used tools and libraries that form the basis for the NSF’s HPC cyberinfrastructure. The research undertakes this challenge and pursues new performance engineering avenues---by exploiting a co-design approach using the MPI_T API---in the MVAPICH2 and TAU libraries with scientific applications. The project focuses on two popular HPC applications spanning multiple domains and representing various communication patterns - Anelastic Wave Propagation (AWP-ODC) and Highly efficient FFTs for Exascale (heFFTe). AWP-ODC is a highly scalable parallel finite-difference application with point-to-point operations that enables 3D earthquake calculations. HeFFTe, dominated by collective operations, is a massively parallel application that provides a scalable and efficient implementation of the widely used Fast Fourier Transform (FFT) operations. The research aims to investigate and develop the following innovations by co-designing MVAPICH2 and TAU libraries to scale driving science domains---including AWP-ODC and heFFTe: 1) Load-aware designs for MPI asynchronous communication, 2) Cross runtime coordination for MPI+X applications, 3) Partitioned point-to-point primitives, 4) Application-aware neighborhood collective communication, 5) Support for adaptive persistent collective communication, and 6) Coordinating communication kernels on GPUs. Integrated development and evaluation are carried out to ensure proper integration of proposed designs with the driving applications, and closely work with internal and external collaborators to facilitate wide deployment and adoption of the released software. The transformative impact of the proposed effort is to extract the performance and scalability of HPC applications in next-generation HPC architectures through intelligent performance engineering.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.

地震灾害给社区带来潜在的危及生命的风险，并造成重大的经济损失，由于地震建模和其他规范，大型超级计算机上的地震数值模拟正在成为指导基础设施和政策决策的关键。快速傅里叶变换 (FFT) 将处理分布在超级计算机中的大量计算节点上，优化节点之间的通信是实现良好性能的关键，但考虑到执行规模，这是一项艰巨的任务。实现消息传递接口 (MPI) 的 MVAPICH 通信库和 TAU 性能系统（一种用于观察通信的分析工具）将紧密耦合，以评估在执行期间调整这些代码的性能影响。这些库将共享关键性能参数和性能。优化这些应用程序中的通信以缩短解决时间，这些代码的性能设计版本将有助于推动下一代地震预报，并有助于提高我们对地震事件的了解，从而减少对人口中心和环境的风险。使本科生和研究生能够通过高性能计算 (HPC)、深度/机器学习和数据分析课程的教学研究来推动课程进步。研究结果还将传播给研究人员的合作组织，以影响他们的 HPC 软件应用。新兴的 HPC 系统——由多核处理器和加速器架构驱动——需要对现有基础设施进行创新，以便为科学领域提供最佳性能。MPI 4.0 标准还为协同设计应用程序带来了新的机会。随着这些进步，迫切需要更新构成 NSF HPC 网络基础设施基础的常用工具和库。该研究应对这一挑战并追求新的性能工程。通过在 MVAPICH2 和 TAU 库中利用 MPI_T API 的协同设计方法与科学应用程序，该项目重点关注跨越多个领域并代表各种通信模式的两种流行的 HPC 应用程序 - Anelastic Wave。传播 (AWP-ODC) 和百亿亿级高效 FFT (heFFTe) 是一种高度可扩展的并行有限差分应用程序，具有点对点运算，可实现以集体运算为主的 3D 地震计算。大规模并行应用程序，为广泛使用的快速傅立叶变换 (FFT) 运算提供可扩展且高效的实现。该研究旨在通过共同设计来研究和开发以下创新。用于扩展驾驶科学领域的 MVAPICH2 和 TAU 库 — 包括 AWP-ODC 和 heFFTe：1) MPI 异步通信的负载感知设计，2) MPI+X 应用程序的跨运行时协调，3) 分区点对点原语，4）应用程序感知的邻里集体通信，5）支持自适应持久集体通信，以及6）协调GPU上的通信内核进行集成开发和评估，以确保所提出的建议的正确集成。与驱动应用程序一起进行设计，并与内部和外部合作者密切合作，以促进已发布软件的广泛部署和采用。拟议工作的变革性影响是通过智能在下一代 HPC 架构中提取 HPC 应用程序的性能和可扩展性。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。