XPS: DSD: A2MA - Algorithms and Architectures for Multiresolution Applications

XPS：DSD：A2MA - 多分辨率应用的算法和架构

• 批准号: 1337393
• 负责人: George Biros
• 金额: $ 74.98万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1337393&HistoricalAwards=false
• 关键词: XPS; DSD; A2MA; Algorithms; Architectures

The goal of this research project is to devise novel methodologies anddevices for problems in computational science and engineering thatrequire high-intensity of arithmetic operations (also known asFloating-point Operations Per Second, or ``FLOPS''). Among the manyhurdles faced in research and development of such compute-intensivetechnologies is achieving energy-efficient utilization of theavailable computing resources. Similar to the miles/gallon metric usedin automotive design, one is interested in a metric that can be usedin the design of new computing technologies: optimizingFLOPS/watt. This research will be on designing novel algorithms andarchitectures that optimize this metric. These algorithms andarchitectures will be customized to a particular class of scientificcomputing problems: tree-based finite element methods and N-bodyproblems.It is possible to devise algorithms that parallelize well and are energy efficient (i.e., produce high ``percentage-of-peak''measurements). Often, however, such algorithms sacrifice workoptimality. It is much more difficult to design algorithms that do sowhile achieving both work optimality and energy efficiency. Thison-node utilization wall---a chronic problem since the earlynineties---not only remains unresolved but has become more acute withthe emergence of deeper memory hierarchies and manycore andheterogeneous architectures. At the same time, there is a largeuntapped potential by not only adapting algorithms to architecturalchanges, but instead driving architecture design from algorithmrequirements. This research will identify the design space fortree-based algorithms (under the constraints of work-optimality andmaximum concurrency), evaluate performance of state-of-the-art codes,and explore custom algorithm/hardware platforms. A number of broaderimpacts are anticipated from this project. The target methodologiesfind applications in earth sciences, engineering, cosmology, biology,and data analysis. Along with the research activities, an educationaland dissemination program will be designed to communicate the resultsof this work to both students and researchers, as well as a moregeneral audience of computational and application scientists.

这个研究项目的目标是为计算科学和工程中需要高强度算术运算（也称为每秒浮点运算，或“FLOPS”）的问题设计新的方法和设备。 在研究和开发这种计算密集型技术所面临的许多障碍之一是实现对可用计算资源的节能利用。与汽车设计中使用的英里/加仑度量标准类似，人们对一个可以用于新计算技术设计的度量标准感兴趣：优化FLOPS/瓦特。这项研究将设计新的算法和架构，优化这一指标。这些算法和架构将针对特定类别的科学计算问题进行定制：基于树的有限元方法和N体问题。产生高的“峰值保留率”测量）。然而，这种算法通常会牺牲工作最优性。设计这样做的算法同时实现工作最优性和能量效率要困难得多。这个节点上的利用率墙-自90年代初以来的一个长期问题-不仅没有得到解决，而且随着更深层次的内存层次结构和多核异构体系结构的出现而变得更加尖锐。与此同时，通过不仅使算法适应架构变化，而且从算法需求驱动架构设计，还有很大的未开发潜力。本研究将确定基于树的算法的设计空间（在工作最优性和最大并发性的约束下），评估最先进代码的性能，并探索自定义算法/硬件平台。 预计该项目将产生一些更广泛的影响。目标方法学在地球科学、工程学、宇宙学、生物学和数据分析中得到应用。 沿着研究活动，将设计一个教育和传播计划，将这项工作的结果传达给学生和研究人员，以及更广泛的计算和应用科学家。

项目成果

Hardware Accelerator Integration Tradeoffs for High-Performance Computing: A Case Study of GEMM Acceleration in N-Body Methods

高性能计算的硬件加速器集成权衡：N 体方法中 GEMM 加速的案例研究

• DOI: 10.1109/tpds.2021.3056045
• 发表时间: 2021
• 期刊: IEEE Transactions on Parallel and Distributed Systems
• 影响因子: 5.3
• 作者: Asri, Mochamad;Malhotra, Dhairya;Wang, Jiajun;Biros, George;John, Lizy K;Gerstlauer, Andreas
• 通讯作者: Gerstlauer, Andreas