High Performance Computing for Learning

大挑战应用组竞赛为科学家和工程师组成的多学科团队提供了一种支持机制，以实现1992财年高性能计算和通信(HPCC)倡议的目标。理想的建议不仅在科学方面提供了卓越的成果：可能在科学和工程的关键领域产生重大影响的集中问题)，而且还提供了科学活动和计算活动之间的重大互动，通常涉及数学、计算机或计算科学家，这将对超出特定科学或工程问题领域(S)或正在研究的学科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，研究项目--用于学习的高性能计算--被明确地设计为通过CM-5和VLSI试验台来推动高性能计算算法和体系结构的框架，并解决许多HPCC目标。它将为一大类优化和学习问题提供新的算法和软件，在CM-5上测试并直接推动操作系统和架构变化(与CM-5的S关键架构师之一合作)。所解决的学习问题本质上是与几乎所有HPCC Grand Challenges问题相交的一类建模/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.