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越来越大的差距——被称为内存墙——导致高性能计算供应商设计并将新的加速器集成到他们的下一代系统中。代表性加速器包括recon&amp；#64257；可靠的硬件，如fpga，异构处理器，如CPU+GPU处理器，高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体系结构特性，包括数据运动的显式控制、大规模SIMD/矢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来实现以前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，包括大字典字符串匹配、动态规划、图论和稀疏矩阵计算，这些计算出现在生物学、网络安全和社会科学领域。该项目正在开发XScala，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，受数据移动的约束。它提出的优化技术包括：(a)增强和利用最大并发性来隐藏数据移动；(b)算法重组以改善空间及/或时间局部性；(c)数据结构转换，以改善局部性或减少数据（压缩结构）的大小；(d)预取等。该项目还为社区开发的加速器内核开发了一个名为XBazaar的公共软件存储库和论坛。该项目包括研讨会、教程、pi课程和夏季项目，通过各种方式增加社区参与。更广泛的影响包括对新型加速器增强计算机系统的有效使用；创建一个开放和可访问的社区存储库XBazaar，用于分发经过加速器优化的计算内核、软件和模型；研究生和本科生的培养；并通过出版物、科学会议报告、讲座、研讨会和教程进行传播。该框架本身将通过XBazaar作为几个通用平台的开源代码和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。

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.