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

基本信息

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

项目摘要

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彻底改变科学，技术和日常生活。尽管软件和硬件技术取得了许多成功，但能源效率障碍已成为朝着更强大的计算机迈出的主要障碍 - 从移动设备一直到超级计算机。最初自然的内存子系统与计算机中央处理单元（CPU）之间的自然分离已成为能源效率低下的主要原因之一。记忆与CPU之间的数据运动需要比计算本身更多的数量级能量。为了应对这些挑战，该项目将考虑旨在模糊分离内存和计算子系统之间的这些传统边界的新型建筑设计范式和算法，以及通过分发直接在存储器中或作为内存数据传输的一部分进行的计算，实现了数量级的增长效率和性能。该项目将在计算数学中的一类方法的背景下研究这种新颖的方法，这些方法出现在计算科学，大规模数据分析和机器学习中的许多问题的核心。特别是，该项目将集中于数据驱动而不是计算驱动的算法和体系结构的构建，近似和以上等级的算法和体系结构。该项目的最终目标是新型体系结构Cisit的设计（用于``原位计算''），该设计特别旨在解决层次矩阵中计算和数据移动的加速度。 CISIT将唯一将传统的通用CPU和GPU内核与以下方式相结合：（1）使用自定义硬件加速核心算法原始词；（2）原位计算功能，将包括主内存或附近的处理以及靠近核心的芯片缓存和内存内的计算；（3）新型的传输计算能力，可以减少削减，在许多情况下，完全消除了不必要的往返数据传输，通过透明地处理数据传输，因为它在跨高速缓存层次结构之间转移到主内存和局部计算核心之间。成功后，CISIT将影响未来的建筑实施。除研究活动外，还将设计一项教育和传播计划，以将这项工作的结果传达给学生和研究人员，以及计算和应用科学家的一般受众。