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.

该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”）的问题设计新颖的方法和设备。 这种计算密集型技术的研究和开发面临的许多障碍之一是实现可用计算资源的节能利用。与汽车设计中使用的英里/加仑指标类似，人们对可用于新计算技术设计的指标感兴趣：优化 FLOPS/瓦特。这项研究将致力于设计优化该指标的新颖算法和架构。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。可以设计出并行性良好且节能的算法（即产生高“峰值百分比”测量）。然而，此类算法通常会牺牲工作优化性。设计同时实现工作优化和能源效率的算法要困难得多。这种节点上的利用壁垒——自九十年代初以来的一个长期问题——不仅仍未得到解决，而且随着更深的内存层次结构以及多核和异构架构的出现而变得更加严重。与此同时，不仅使算法适应架构变化，而且从算法需求驱动架构设计，还有巨大的未开发潜力。这项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。 预计该项目将产生许多更广泛的影响。目标方法在地球科学、工程学、宇宙学、生物学和数据分析中都有应用。 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

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