II-NEW: Research Software Infrastructure for Tensor Computations

II-新：张量计算的研究软件基础设施

• 批准号: 1059417
• 负责人: Gerald Baumgartner
• 金额: $ 30万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1059417&HistoricalAwards=false
• 关键词: II; NEW; Research; Software; Infrastructure

The goal of this project is to build a new software infrastructure forcontinuing research on compiler optimizations for tensor computations.Motivated by the successes of the model-driven search-basedoptimization approach of the Tensor Contraction Engine (TCE) and thepolyhedral model-based transformations, an optimization infrastructureis being developed that combines the key aspects of the TCE and thepolyhedral models and provides the flexibility to continue research onoptimizing and automatically parallelizing tensor computations forparallel and/or distributed computations for any machine architecture,including multi-cores and GPUs.This novel optimization framework has potential for high payoffs inenabling future research and eventually in generating high-performancecode for an important class of quantum chemistry codes and othertensor computations. After further research, it will become avaluable tool for reducing the time to develop high-performanceapplications in several areas of science and engineering.

这个项目的目标是建立一个新的软件基础设施，用于继续研究张量计算的编译器优化。受到张量收缩引擎（TCE）和基于多面体模型的转换的模型驱动的基于搜索的优化方法的成功的激励，目前正在开发一个优化基础设施，它将战术协同效应和多面体模型的关键方面结合起来，并为继续研究提供灵活性优化和自动并行化张量计算，用于任何机器架构（包括多核和GPU）的并行和/或分布式计算。这种新颖的优化框架具有高回报的潜力，可以实现未来的研究，并最终为一类重要的量子化学代码和其他张量计算生成高性能代码。 经过进一步的研究，它将成为一个宝贵的工具，以减少时间来开发高性能的应用程序在几个领域的科学和工程。