SI2-SSI: Collaborative: The XScala Project: A Community Repository for Model-Driven Design and Tuning of Data-Intensive Applications for Extreme-Scale Accelerator-Based Systems

SI2-SSI：协作：XScala 项目：用于基于超大规模加速器的系统的模型驱动设计和数据密集型应用程序调整的社区存储库

• 批准号: 1339756
• 负责人: Viktor Prasanna
• 金额: $ 74.89万
• 依托单位: University of Southern California
• 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 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1339756&HistoricalAwards=false
• 关键词: SI2; SSI; Collaborative; XScala; Project

The increasing gap between processor and memory performance -- referred to as the memory wall -- has led high-performance computing vendors to design and incorporate new accelerators into their next-generation systems. Representative accelerators include reconfigurable hardware such as FPGAs, heterogeneous processors such as CPU+GPU processors, highly multicore and multithreaded processors, and manycore co-processors and general-purpose graphics processing units, among others. These accelerators contain myriad innovative architectural features, including explicit control of data motion, large-scale SIMD/vector processing, and multithreaded stream processing. Such features provide abundant opportunities for developers to achieve high-performance for applications that were previously deemed hard to optimize. This project aims to develop tools that will assist developers in using hardware accelerators (co-processors) productively and effectively. This project's specific technical focus is on data-intensive kernels including large dictionary string matching, dynamic programming, graph theory, and sparse matrix computations that arise in the domains of biology, network security, and the social sciences. The project is developing XScala, a software framework for designing efficient accelerator kernels. The framework contains a variety of design time and run-time performance optimization tools. The project concentrates on data-intensive kernels, bound by data movement. It proposes optimization techniques including (a) enhancing and exploiting maximal concurrency to hide data movement; (b) algorithmic reorganization to improve spatial and/or temporal locality; (c) data structure transformations to improve locality or reduce the size of the data (compressed structures); and (d) prefetching, among others. The project is also developing a public software repository and forum, called the XBazaar, for community-developed accelerator kernels. This project includes workshops, tutorials, and the PIs class and summer projects as various means by which to increase community involvement. The broader impacts include productive use of emerging classes of accelerator-augmented computer systems; creation of an open and accessible community repository, the XBazaar, for distributing accelerator-tuned computational kernels, software, and models; training of graduate and undergraduate students; and dissemination through publications, presentations at scientific meetings, lectures, workshops, and tutorials. The framework itself will be released as open-source code and as precompiled binaries for several common platforms, through the XBazaar, as an initial step toward building a community around accelerator kernels.

处理器和内存性能之间的差距越来越大（称为内存墙），导致高性能计算供应商设计并将新的加速器纳入其下一代系统。代表性的促进剂包括&recon#64257;这些处理器可以包括诸如FPGA之类的可计算硬件、诸如CPU+GPU处理器之类的异构处理器、高度多核和多线程处理器、以及众核协处理器和通用图形处理单元等。这些加速器包含无数创新的架构功能，包括数据运动的显式控制，大规模SIMD/向量处理和多线程流处理。这些特性为开发人员提供了大量的机会，使以前被认为难以优化的应用程序实现高性能。该项目旨在开发工具，帮助开发人员高效地使用硬件加速器（协处理器）。该项目的具体技术重点是数据密集型内核，包括生物学，网络安全和社会科学领域中出现的大型字典字符串匹配，动态编程，图论和稀疏矩阵计算。该项目正在开发XScala，这是一个用于设计高效加速器内核的软件框架。该框架包含各种设计时和运行时性能优化工具。该项目专注于数据密集型内核，由数据移动绑定。它提出了优化技术，包括（a）增强和利用最大并发性来隐藏数据移动;（B）算法重组来提高空间和/或时间局部性;（c）数据结构变换来提高局部性或减小数据的大小（压缩结构）;以及（d）预取等。该项目还为社区开发的加速器内核开发了一个公共软件库和论坛，称为XBazaar。该项目包括研讨会，教程，以及PI类和暑期项目，通过这些方式来增加社区参与。更广泛的影响包括生产性地使用加速器增强计算机系统的新兴类别;创建一个开放和可访问的社区存储库，XBazaar，用于分发加速器调整的计算内核，软件和模型;培训研究生和本科生;并通过出版物，科学会议，讲座，研讨会和教程中的演示进行传播。该框架本身将通过XBazaar作为开源代码和几个常见平台的预编译二进制文件发布，作为围绕加速器内核构建社区的第一步。

项目成果

FASTCF: FPGA-based Accelerator for STochastic-Gradient-Descent-based Collaborative Filtering

• DOI: 10.1145/3174243.3174252
• 发表时间: 2018-02
• 期刊: Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
• 作者: Shijie Zhou;R. Kannan;Yu Min;V. Prasanna

An FPGA framework for edge-centric graph processing

• DOI: 10.1145/3203217.3203233
• 发表时间: 2018-05
• 期刊: Proceedings of the 15th ACM International Conference on Computing Frontiers
• 作者: Shijie Zhou;R. Kannan;Hanqing Zeng;V. Prasanna