CAREER : Towards Exascale Performance of Parallel Applications

职业：迈向并行应用的百亿亿级性能

• 批准号: 2338077
• 负责人: Amanda Bienz
• 金额: $ 55.78万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2029-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338077&HistoricalAwards=false
• 关键词: CAREER; Towards; Exascale; Performance; Parallel

Each generation of supercomputers is more powerful than the previous, with current systems capable of adding millions of millions of numbers together in a single second. These high-performance machines provide the hardware necessary for scientists and engineers to solve increasingly complex problems and make discoveries through computer simulations. These programs run across the thousands of individual compute cores that make up a supercomputer, with each core executing a portion of the program and communicating messages to other cores as needed. Often, simulations fail to efficiently use the computing power provided by current supercomputers due to significant overheads associated with communication. This project addresses this challenge by reducing communication costs within widely used parallel programs and improving a spectrum of existing applications to allow for novel scientific discoveries. Furthermore, this project will support the revitalization of the CS4ALL course along with hackathon development to introduce computing topics to a diverse student population across the University of New Mexico main and branch campuses.The goals of this project are to minimize communication costs and enhance the performance and scalability of existing parallel applications. This project will develop accurate performance models for emerging heterogeneous architectures to optimize communication within non-linear solvers, simulations, iterative methods, and neural networks. These models will be used to employ several optimization strategies, including graph partitions that minimize performance model-based functions, locality-aware partitioning throughout the widely used algebraic multigrid (AMG) preconditioner, and topology-aware MPI_Allreduce operations. Furthermore, the project will explore specialized optimizations for heterogeneous systems with multiple GPUs per node, including selecting optimal communication paths, utilizing all available CPU cores during communication via threading, and aggregating inter-node messages to reduce data injected into the network. By minimizing communication within foundational numerical methods, this project aims to yield tangible improvements in performance and scalability across a spectrum of applications reliant on these methods.This project is jointly funded by the Software and Hardware Foundations Core Program and the Established Program to Stimulate Competitive Research (EPSCoR).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.

每一代超级计算机都比以前更强大，当前系统能够在一秒钟内将数百万个数字添加在一起。这些高性能的机器为科学家和工程师提供了解决日益复杂的问题并通过计算机模拟进行发现所需的硬件。这些程序跨越组成超级计算机的数千个单独的计算内核，每个核心都执行了一部分程序，并根据需要将消息传达给其他内核。通常，由于与通信相关的重要开销，模拟无法有效地使用当前超级计算机提供的计算能力。该项目通过降低广泛使用的并行程序中的沟通成本并改善现有应用程序以允许新的科学发现来解决这一挑战。此外，该项目将支持CS4All课程的振兴以及Hackathon Development，向新墨西哥大学Main和Branch Campuses的多样化学生群体引入计算主题。该项目的目标是最大程度地降低沟通成本并提高现有平行应用的性能和可扩展性。该项目将为新兴的异质体系结构开发准确的性能模型，以优化非线性求解器，仿真，迭代方法和神经网络中的通信。这些模型将用于采用多种优化策略，包括将基于绩效模型的功能最小化的图形分区，在广泛使用的代数多机（AMG）预处理和拓扑的MPI_Allreduce操作中，对基于性能模型的函数进行了划分。此外，该项目将探索针对每个节点多个GPU的异质系统的专业优化，包括选择最佳通信路径，利用通过线程进行通信期间的所有可用CPU内核，并聚集节点信息以减少注入网络中的数据。 By minimizing communication within foundational numerical methods, this project aims to yield tangible improvements in performance and scalability across a spectrum of applications reliant on these methods.This project is jointly funded by the Software and Hardware Foundations Core Program and the Established Program to Stimulate Competitive Research (EPSCoR).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 标准。