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

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新的加速器，并将其集成到他们的下一代系统中。具有代表性的加速器包括可重构硬件(如FPGA)、异类处理器(如CPU GPU处理器)、高度多核和多线程处理器、多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，包括显式控制数据移动、大规模SIMD/向量处理和多线程流处理。这些功能为开发人员提供了大量机会，以实现以前被认为难以优化的应用程序的高性能。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、网络安全和社会科学领域中出现的大型词典字符串匹配、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。该项目专注于受数据移动约束的数据密集型内核。它提出了优化技术，包括(A)增强和利用最大并发来隐藏数据移动；(B)算法重组以改善空间和/或时间局部性；(C)数据结构转换以改善局部性或减小数据的大小(压缩结构)；以及(D)预取等。该项目还在开发一个公共软件库和论坛，名为XBazaar，用于社区开发的加速器内核。该项目包括研讨会、教程、PIS课程和暑期项目，以此作为增加社区参与的各种手段。更广泛的影响包括生产性地使用新兴的加速器增强的计算机系统；创建一个开放的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；对研究生和本科生的培训；以及通过出版物、在科学会议上的演讲、讲座、研讨会和教程进行传播。框架本身将通过XBazaar以开源代码和预编译二进制文件的形式发布，用于几个常见平台，作为围绕加速器内核构建社区的第一步。

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.