EAGER: Rapid, Efficient Implementation of Irregular Applications on SIMD Many-Core Platforms

EAGER：在 SIMD 众核平台上快速、高效地实施不规则应用

• 批准号: 1500173
• 负责人: Jeremy Buhler
• 金额: $ 25万
• 依托单位: Washington 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 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1500173&HistoricalAwards=false
• 关键词: EAGER; Rapid; Efficient; Implementation; Irregular

General-purpose computation with efficient Single Instruction, Multiple Data (SIMD) oriented many-core devices, such as graphical processing units (GPUs), can deliver high performance in a variety of application domains. Data-parallel applications that perform well on SIMD many-cores typically exhibit regularity in patterns of computation and data movement, acting identically on each of an ensemble of many equal-sized inputs. However, many important applications exhibit irregular behavior making them difficult to implement efficiently on these platforms. Thus, efficient SIMD implementation of applications with irregular behavior is an important ongoing research problem. This project's focus is the investigation and validation of novel interface designs, algorithmic techniques, and implementation strategies to address the problem of efficient SIMD implementation uniformly for applications from a variety of domains. The work includes generating alternate module designs to support efficient developer-driven searches over large design spaces to tune performance. Another key area of the research will validate these technologies on bio-sequence analysis applications resulting in innovative, efficient new GPU designs for computational tasks. More broadly and with a particular focus on new high-performance application designs for data-intensive computations critical to bioinformatics, the project will enable faster development of more efficient, more maintainable GPU software, even for applications with SIMD-unfriendly irregular behaviors.

具有高效的单指令、多数据(SIMD)面向多核设备的通用计算，例如图形处理单元(GPU)，可以在各种应用领域提供高性能。在SIMD多核上表现良好的数据并行应用程序通常在计算和数据移动模式上表现出规律性，在许多相同大小的输入的集合中的每一个上都相同地作用。然而，许多重要的应用程序表现出不规则的行为，使得它们很难在这些平台上高效地实现。因此，高效地实现具有不规则行为的应用的SIMD是一个重要的正在进行的研究问题。该项目的重点是调查和验证新颖的接口设计、算法技术和实现策略，以解决针对不同领域的应用程序统一高效地实施SIMD的问题。这项工作包括生成备用模块设计，以支持在大型设计空间上进行高效的开发人员驱动的搜索，从而优化性能。这项研究的另一个关键领域将在生物序列分析应用上验证这些技术，从而为计算任务带来创新、高效的新图形处理器设计。更广泛地说，该项目将特别关注对生物信息学至关重要的数据密集型计算的新的高性能应用程序设计，该项目将使更快地开发更高效、更可维护的GPU软件，即使是对于具有SIMD不友好的不规则行为的应用程序。