基于异构平台的高复杂度生物序列分析算法并行化研究

项目名称： 基于异构平台的高复杂度生物序列分析算法并行化研究

项目编号： No.61202127

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 计算机科学学科

项目作者： 夏飞

作者单位： 中国人民解放军海军工程大学

项目金额： 24万元

中文摘要： 生物信息学属于多学科前沿交叉科学，应用广泛，意义重大。生物序列分析是生物信息学乃至现代生命科学领域重要的基础性研究工作，该领域的应用具有程序特征多样化、数据相关多维度、访存行为不规则等特点。通用结构计算机虽然能够提供很强的峰值计算能力，但是不能完全适应该领域复杂计算特性的特殊要求，计算效率不高。 课题以序列分析领域中的复杂结构预测和分析算法对高性能计算的需求为背景，基于通用微处理器结合硬件算法加速器（FPGA和GPU）的异构体系结构，从提取典型方法的动态计算特征入手，研究对复杂数据相关和不规则存储访问的优化方法，对典型算法实现细粒度并行，达到高效加速计算的目的；并在此基础上提取普适的并行优化方法，为特定领域的算法群提供一种基本的硬件结构模板和并行程序设计框架，为有效降低算法加速器设计复杂度、实现加速器快速生成奠定基础，为提高对生物信息的处理能力提供技术参考。

中文关键词： 可重构计算；细粒度并行；算法加速器；异构体系结构；高性能计算

英文摘要： Bioinformatics, as a new coming interdiscipline based on the development of several branches of science, has very important intension of theory and practical meanings, which has widely applied in many scientific fields. Biological sequence analysis has become a fundamental task of bioinformatics and modern life science. High performance computer systems based on general-purpose multi-core processors are widely used to accelerate sequence analysis algorithms. However, parallel efficiency is greatly limited by the diversification of program features: bit-wised parallelism, complicated and multi-dimensional data dependency, and tight synchronization resulting from irregular computing and storage features. Although general-purpose architecture computers have powerful peak performance, efficiently executing the task of bio-sequence analysis on a general-purpose parallel computer becomes very awkward. Recently, the use of FPGA and GPU coprocessors has become a promising approach for accelerating bioinformatics applications. In this project, we are going to research fine-grained parallelized algorithms and structures for accelerating complex bio-sequence structure prediction and analysis algorithms based on FPGA and GPU computing platforms. We analysis the dynamic computing and storage features of classic bioinformatic

英文关键词： Reconfigurable Computing；Fine-grained Parallelism；Algorithm Accelerator；Heterogeneous Architecture；High Performance Computing