XPS: FULL: FP: Collaborative Research:Advancing autovectorization

XPS：完整：FP：协作研究：推进自动矢量化

• 批准号: 1533912
• 负责人: David Padua
• 金额: $ 50.7万
• 依托单位: University of Illinois at Urbana-Champaign
• 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 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533912&HistoricalAwards=false
• 关键词: XPS; FULL; FP; Collaborative; Research

Title: XPS:FULL:FP:Collaborative Research:Advancing autovectorizationThe goal of this project is to advance the state of the art in autovectorization. This is a technique applied by compilers to automatically transform computer programs so that they can take advantage of the vector devices found in most processors. Today, most compilers have autovectorization capabilities, but their effectiveness is limited. The intellectual merit of this project lies in its potential to advance an important and beautiful core area of computer science, compiler technology, by creating new techniques and extending our understanding of programming patterns, program analysis, and transformation techniques. Beyond computer science, the project's broader significance and importance is that its results aim at increasing the fraction of code segments that, without human intervention, make use of vector devices. The effect of this increase is the acceleration of computer programs and the reduction of the energy that they consume. Faster programs are of great importance in all application areas, but are particularly important in science and engineering where computing speed is an enabler of discoveries and better designs. The research strategy is to develop and evaluate a prototype autovectorizer based on the exploration of the space of equivalent versions of a program guided by an intelligent search engine. The space of equivalent versions is obtained with a source-to-source restructurer. A repository of codelets is planned in order to train the search engine so that it becomes capable of guiding the selection in the space of possibilities in order to identify a highly efficient version of the code.

标题：XPS：FULL：FP：协作研究：推进自动矢量化该项目的目标是推进自动矢量化的最新技术。这是编译器用来自动转换计算机程序的技术，以便它们可以利用大多数处理器中的矢量设备。如今，大多数编译器都具有自动向量化功能，但其有效性有限。该项目的智力价值在于它有潜力通过创造新技术和扩展我们对编程模式、程序分析和转换技术的理解来推进计算机科学、编译器技术这一重要而美丽的核心领域的发展。除了计算机科学之外，该项目更广泛的意义和重要性在于，其结果旨在增加无需人工干预即可使用矢量设备的代码段的比例。这种增加的效果是计算机程序的加速和它们消耗的能量的减少。 更快的程序在所有应用领域都非常重要，但在科学和工程中尤其重要，因为计算速度是发现和更好设计的推动力。 研究策略是基于对智能搜索引擎引导的程序等效版本空间的探索，开发和评估原型自动矢量化器。等效版本的空间是通过源到源重组器获得的。规划了一个小代码存储库，以便训练搜索引擎，使其能够在可能性空间中指导选择，从而识别代码的高效版本。