XPS: FULL: CCA: Collaborative Research: Automatically Scalable Computation

XPS：完整：CCA：协作研究：自动可扩展计算

• 批准号: 1533663
• 负责人: Steven Homer
• 金额: $ 35万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533663&HistoricalAwards=false
• 关键词: XPS; FULL; CCA; Collaborative; Research

For over thirty years, each generation of computers has been faster than the one that preceded it. This exponential scaling transformed the way we communicate, navigate, purchase, and conduct science. More recently, this dramatic growth in single processor performance has stopped and has been replaced by new generations of computers with more processors on them; for example, even the cell phones we carry have multiple processors in them. Writing software that effectively leverages multiple processing elements is difficult, and rewriting the decades of accumulated software is both difficult and costly. This research takes a different approach -- rather than converting sequential software into parallel software, this project develops ways to store and reuse computation. Imagine computing only when computer time and energy are cheap and plentiful, storing that computation, and then using it later, when computation might be limited or expensive. The approach used involves making informed predictions about computation likely to happen in the future, proactively executing likely computations in parallel with the actual computation, and then "jumping forward in time" if the actual execution arrives at any of the predicted computations that have already been completed. This research touches many areas within Computer Science, architecture, compilers, machine learning, systems, and theory. Additionally, exploiting massively parallel computation will produce immediate returns in multiple scientific fields that rely on computation.The approach used in this research views computational execution as moving a system through the enormously high dimensional space represented by its registers and memory of a conventional single-threaded processor. It uses machine learning algorithms to observe execution patterns and make predictions about likely future states of the computation. Based on these predictions, the system launches potentially large numbers of speculative threads to execute from these likely computations, while the actual computation proceeds serially. At strategically chosen points, the main computation queries the speculative executions to determine if any of the completed computation is useful; if it is, the main thread uses the speculative computation to immediately begin execution where the speculative computation left off, achieving a speed-up over the serial execution. This approach has the potential to be extremely scalable: the more cores, memory, and communication bandwidth available, the greater the potential for performance improvement. The approach also scales across programs -- if the program running today happens upon a state encountered by a program running yesterday, the program can reuse yesterday's computation. This project has the potential to break new ground for research in many areas in Computer Science touched by it.

三十多年来，每一代计算机一直比之前的计算机要快。这种指数缩放改变了我们交流，导航，购买和进行科学的方式。最近，单个处理器性能的这种戏剧性增长已经停止，并被新一代的计算机取代，上面有更多的处理器。例如，即使我们携带的手机也有多个处理器。 编写有效利用多个处理元素的软件很困难，重写数十年的累积软件既困难又昂贵。这项研究采用了不同的方法 - 而不是将顺序软件转换为并行软件，而是开发了存储和重用计算的方法。想象一下，只有当计算机时间和能源廉价且丰富，存储该计算，然后在计算可能有限或昂贵时使用计算。 所使用的方法涉及对可能在将来发生的计算进行明智的预测，并主动执行可能与实际计算并行执行计算，然后如果实际执行到达已经完成的任何预测的计算，则“及时跳跃”。 这项研究涉及计算机科学，建筑，编译器，机器学习，系统和理论中的许多领域。 此外，利用大规模平行的计算将在依赖计算的多个科学领域中立即产生回报。本研究中使用的方法将计算执行视为将系统通过其登记册代表的极高的尺寸空间以及对传统单线读取的处理器的内存表示。 它使用机器学习算法来观察执行模式，并就计算的未来状态进行预测。 基于这些预测，该系统启动了可能从这些可能的计算中执行的大量投机线程，而实际计算进行了串行进行。 在战略性选择的点上，主要计算查询了投机执行，以确定任何完成的计算是否有用；如果是这样，主线程使用投机式计算立即开始执行，在保留投机计算的情况下，可以通过串行执行来加快速度。 这种方法有可能非常可扩展：可用的核心，内存和通信带宽越多，提高性能的可能性就越大。该方法还跨越程序扩展 - 如果今天运行的程序发生在昨天运行的程序遇到的状态下，则该程序可以重复使用昨天的计算。该项目有可能在其触及的计算机科学领域的许多领域中为研究而开展新的基础。