High Performance Computing for Learning

大挑战应用小组竞赛为支持科学家和工程师组成的多学科团队提供了一种机制，以实现1992财政年度高性能计算和通信（HPCC）计划的目标。理想的提议不仅在科学上卓越，在科学和工程的关键领域具有潜在重大影响的集中问题，但也是科学和计算活动之间的重要相互作用，通常涉及数学，计算机或计算科学家；这将对正在研究的特定科学或工程问题领域或学科之外的高性能计算活动产生影响。在授予麻省理工学院的Berwick、Bizzi、Bulthoff、Jordan、Wexler、Poggio、Rivest、Winston和Yang的奖项中，该研究项目——用于学习的高性能计算——被明确设计为通过CM-5和VLSI测试平台推动高性能计算算法和架构信封，并解决许多HPCC目标。它将为广泛的优化和学习问题提供新的算法和软件，在CM-5上进行测试，并直接推动操作系统和架构变化（与CM-5的一位关键架构师合作）。所解决的学习问题本质上是与几乎所有HPCC大挑战问题相交的一整类建模/优化问题。

The Grand Challenge Application Groups competition provides one mechanism for the support of multidisciplinary teams of scientists and engineers to meet the goals of the High Performance Computing and Communications (HPCC) Initiative in Fiscal Year 1992. The ideal proposal provided not only excellence in science: focussed problem with potential for substantial impact in a critical area of science and engineering) but also significant interactions between scientific and computational activities, usually involving mathematical, computer or computational scientists, that would have impact in high-performance computational activity beyond the specific scientific or engineering problem area(s) or discipline being studied. In the award to Berwick, Bizzi, Bulthoff, Jordan, Wexler, Poggio, Rivest, Winston, and Yang at MIT, the research project - High Performance Computing for Learning - has been designed explicitly to push the High Performance Computing algorithmic and architectural envelope via a CM-5 and VLSI testbed and to address many of the HPCC goals. It will advance new algorithms and software for a broad class of optimization and learning problems, tested on and directly driving operating system and architectural changes on the CM-5 (working with one of the CM-5's key architects). The learning problems addressed are essentially an entire class of modeling/optimization problems that intersect with nearly all HPCC Grand Challenge Problems.