SPX: CISIT: Computing In Situ and In Memory for Hierarchical Numerical Algorithms

SPX：CISIT：分层数值算法的原位和内存计算

• 批准号: 1725743
• 负责人: George Biros
• 金额: $ 80万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1725743&HistoricalAwards=false
• 关键词: SPX; CISIT; Computing; Situ; Memory

High performance computing holds an enormous promise for revolutionizing science, technology, and everyday life through modeling and simulation, statistical inference, and artificial intelligence. Despite the numerous successes in software and hardware technologies, energy efficiency barriers have become a major hurdle towards more powerful computers -- from mobile devices all the way to supercomputers. The originally natural separation between the memory subsystem and the central processing unit (CPU) of a computer has emerged as one the main reasons for energy inefficiency. Data movement between the memory and the CPU requires orders of magnitude more energy than the computations themselves. To address these challenges, this project will consider novel architectural design paradigms and algorithms that are aimed at blurring these traditional boundaries between separated memory and computation subsystems and, by distributing computations to be performed directly in the memory or as part of the memory data transfers, achieve order of magnitude gains inenergy efficiency and performance. This project will investigate such novel approaches in the context of a class of methods in computational mathematics, which appear at the core of many problems in computational science, large-scale data analytics, and machine learning.Specifically, this project will focus on data-driven rather than compute-driven co-design of algorithms and architectures for the construction, approximation, and factorization of hierarchical matrices. The end-goal of the project is the design of a novel architecture, CISIT (for ``Computing In Situ and In Transit''), that specifically aims to address acceleration of both computation and data movement in the context of hierarchical matrices. CISIT will uniquely combine traditional general-purpose CPU and GPU cores with: (1) acceleration of core algorithmic primitives using custom hardware; (2) in-situ computing capabilities that will comprise both processing in or near main memory as well as computing within on-chip caches and memory close to the cores; (3) novel in-transit compute capabilities that will enable cutting down on and in many cases completely eliminating unnecessary roundtrip data transfers by processing of data transparently as it is transferred between main memory and local compute cores across the cache hierarchies. Upon success, CISIT will influence future architectural implementations. Along with the research activities, an educational and dissemination program will be designed to communicate the results of this work to both students and researchers, as well as a more general audience of computational and application scientists.

高性能计算通过建模和模拟、统计推断和人工智能，为科学、技术和日常生活带来巨大的变革。 尽管在软件和硬件技术方面取得了巨大的成功，但能源效率障碍已成为更强大的计算机（从移动设备一直到超级计算机）的主要障碍。计算机的内存子系统和中央处理单元 (CPU) 之间原本自然的分离已成为能源效率低下的主要原因之一。内存和 CPU 之间的数据移动需要的能量比计算本身多几个数量级。为了应对这些挑战，该项目将考虑新颖的架构设计范例和算法，旨在模糊分离的内存和计算子系统之间的传统界限，并通过分配直接在内存中执行或作为内存数据传输的一部分的计算，实现能源效率和性能的数量级增益。该项目将在一类计算数学方法的背景下研究此类新颖的方法，这些方法出现在计算科学、大规模数据分析和机器学习中许多问题的核心。具体来说，该项目将重点关注数据驱动而不是计算驱动的算法和架构的协同设计，用于层次矩阵的构造、近似和分解。该项目的最终目标是设计一种新颖的架构 CISIT（“原位计算和传输中计算”），其具体目标是解决分层矩阵背景下计算和数据移动的加速问题。 CISIT 将独特地将传统通用 CPU 和 GPU 内核与以下功能相结合：(1) 使用定制硬件加速核心算法原语； (2) 现场计算能力，包括在主存储器内或附近的处理以及在片上高速缓存和靠近核心的存储器内的计算； (3) 新颖的传输中计算功能，当数据在主内存和本地计算核心之间跨缓存层次结构传输时，通过透明地处理数据，可以减少甚至在许多情况下完全消除不必要的往返数据传输。一旦成功，CISIT 将影响未来的架构实施。 除了研究活动之外，还将设计一个教育和传播计划，以便向学生和研究人员以及更广泛的计算和应用科学家受众传达这项工作的结果。

项目成果

Off-Chip Congestion Management for GPU-based Non-Uniform Processing-in-Memory Networks

基于 GPU 的非均匀处理内存网络的片外拥塞管理

• DOI: 10.1109/pdp50117.2020.00050
• 发表时间: 2020
• 期刊: and Network-Based Processing (PDP
• 作者: Punniyamurthy, Kishore;Gerstlauer, Andreas
• 通讯作者: Gerstlauer, Andreas

CLAIRE: A DISTRIBUTED-MEMORY SOLVER FOR CONSTRAINED LARGE DEFORMATION DIFFEOMORPHIC IMAGE REGISTRATION.

• DOI: 10.1137/18m1207818
• 发表时间: 2019
• 期刊: SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics
• 作者: Mang A;Gholami A;Davatzikos C;Biros G
• 通讯作者: Biros G

Cacheline Utilization-Aware Link Traffic Compression for Modular GPUs

模块化 GPU 的缓存线利用率感知链路流量压缩

• DOI: 10.1109/vlsid49098.2020.00041
• 发表时间: 2020
• 期刊: IEEE International Conference on VLSI Design and Embedded Systems (VLSID
• 作者: Punniyamurthy, Kishore;Das, Shomit;Gerstlauer, Andreas
• 通讯作者: Gerstlauer, Andreas

HALO: A Hierarchical Memory Access Locality Modeling Technique For Memory System Explorations

• DOI: 10.1145/3205289.3205323
• 发表时间: 2018-06
• 期刊: Proceedings of the 2018 International Conference on Supercomputing
• 作者: Reena Panda;L. John

A Case for Granularity Aware Page Migration

• DOI: 10.1145/3205289.3208064
• 发表时间: 2018-06
• 期刊: Proceedings of the 2018 International Conference on Supercomputing
• 作者: Jee Ho Ryoo;L. John;Arkaprava Basu