CAREER : Towards Exascale Performance of Parallel Applications

每一代超级计算机都比上一代更强大，目前的系统能够在一秒钟内将数百万个数字相加。这些高性能的机器为科学家和工程师解决日益复杂的问题和通过计算机模拟获得发现提供了必要的硬件。这些程序在组成超级计算机的数千个单独的计算核心上运行，每个核心执行程序的一部分，并根据需要与其他核心通信。通常，由于与通信相关的大量开销，模拟无法有效地利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通信成本和改进现有应用程序的频谱来解决这一挑战，以允许新的科学发现。此外，该项目将支持CS4ALL课程的复兴以及黑客马拉松的发展，向新墨西哥大学主校区和分校的不同学生群体介绍计算主题。该项目的目标是最小化通信成本，增强现有并行应用程序的性能和可伸缩性。该项目将为新兴的异构架构开发准确的性能模型，以优化非线性求解器、模拟、迭代方法和神经网络中的通信。这些模型将用于采用几种优化策略，包括最小化基于模型的性能函数的图分区，广泛使用的代数多网格（AMG）预条件中的位置感知分区，以及拓扑感知的MPI_Allreduce操作。此外，该项目将探索具有每个节点多个gpu的异构系统的专门优化，包括选择最佳通信路径，在通过线程通信期间利用所有可用的CPU内核，以及聚合节点间消息以减少注入网络的数据。通过最小化基本数值方法中的通信，该项目旨在通过依赖于这些方法的一系列应用程序在性能和可伸缩性方面产生切实的改进。该项目由软件和硬件基金会核心项目和促进竞争研究的既定项目（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.