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; 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.

地震危害对社区构成潜在威胁生命的风险，并造成严重的经济损害。大规模超级计算机上的地震的数值模拟是指导基础设施和政策决策的关键，这是地震建模的结果。这些地震和其他代码，包括涉及快速傅立叶变换（FFT）的模拟，在超级计算机中分布了大量计算节点的处理。优化节点之间的通信是实现良好性能的关键，但鉴于执行的规模，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（一种观察通信的分析工具）将紧密耦合，以评估执行过程中调整这些代码的性能影响。这些库将共享关键性能参数并优化这些应用程序中的通信，以改善解决方案的时间。这些代码的性能工程版本将有助于推动下一代地震预测，并有助于提高我们对地震事件的理解，以减少人口中心和环境的风险。这项研究将通过高性能计算的教育学研究（HPC），深度/机器学习和数据分析课程来实现本科和研究生课程的进步。结果还将传播给研究人员的合作组织，以影响其HPC软件应用程序。发出由多核处理器和加速器体系结构驱动的HPC系统 - 需要现有基础设施中的创新，以为科学领域提供最佳性能。 MPI 4.0标准还为共同设计应用程序带来了新的机会。这些包括分区的点对点和集体操作以及邻里集体。通过这些进步，迫切需要更新构成NSF HPC Cyber​​infrstructure基础的常用工具和库。这项研究提出了这一挑战，并通过使用MPI_T API（在MVAPICH2）和具有科学应用的TAU库中利用共同设计方法来追求新的绩效工程途径。该项目着重于两个流行的HPC应用程序，这些应用程序涵盖了多个域并代表各种通信模式 - 无弹性波传播（AWP -ODC）和Exascale（HEFFTE）的高效FFT。 AWP-ODC是一种具有高度可扩展的并行有限差分应用，其点对点操作可以实现3D地震计算。赫夫特（Heffte）以集体操作为主导，是一个大规模的并行应用，可提供广泛使用的快速傅立叶变换（FFT）操作的可扩展性实现。 The research aims to investigate and develop the following innovations by co-designing MVAPICH2 and TAU libraries to scale driving science domains---includes 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持续的集体沟通，以及6）在GPU上协调沟通内核。进行集成开发和评估以确保拟议的设计与驾驶应用程序的正确集成，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。拟议的努力的变革性影响是通过智能绩效工程提取HPC应用程序在下一代HPC体系结构中的性能和可伸缩性。该奖项反映了NSF的法定任务，并通过基金会的知识分子优点和更广泛的影响标准通过评估来诚实地诚实地支持了支持。