SHF: Small: Co-Processors for High-Performance Genome Analysis

SHF：小型：用于高性能基因组分析的协处理器

• 批准号: 0915608
• 负责人: Jason Bakos
• 金额: $ 15.5万
• 依托单位: University South Carolina Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915608&HistoricalAwards=false
• 关键词: SHF; Small; Co; Processors; Performance

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This research develops novel techniques for applying the heterogeneous execution model, where a general-purpose processor is accelerated by a special-purpose co-processor, to optimization-based scientific computations. The result of this research is a library of computational building blocks that perform fundamental operations used in genome analysis, as well as a new design tool that uses this library to systematically synthesize complete co-processor architectures that are optimized for the characteristics of the input dataset of interest.Traditional development methodologies for heterogeneous computing have focused on computations that are based on data-parallelized O(n) algorithms. This project demonstrates the use of heterogeneous computing for non-O(n) algorithms, which have complex behavior, internal state, temporal locality, and a high ratio of computation versus communication. Adapting this class of computation to heterogeneous platforms provides high-performance computing without the need for maintenance-intensive and power-inefficient traditional shared-memory and cluster-based supercomputers.This project targets optimization-based phylogeny reconstruction as a application case study. This application uses combinatorial optimization for its search for optimal phylogenetic (evolutionary) trees, as well as for its procedure for scoring candidate trees.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。 这项研究开发了新的技术，应用异构执行模型，其中通用处理器加速专用协处理器，优化为基础的科学计算。 这项研究的结果是一个计算构建模块库，这些模块执行基因组分析中使用的基本操作，以及一个新的设计工具，它使用这个库系统地综合完整的协处理器架构，这些架构针对感兴趣的输入数据集的特征进行了优化。算法 这个项目演示了非O（n）算法的异构计算的使用，这些算法具有复杂的行为，内部状态，时间局部性，以及计算与通信的高比率。 将这类计算应用于异构平台可以提供高性能计算，而无需使用维护密集型和功耗低的传统共享内存和基于集群的超级计算机。本项目将基于优化的异构重建作为应用案例研究。 该应用程序使用组合优化搜索最佳系统发育（进化）树，以及其评分候选树的程序。