XPS: FULL: DSD: End-to-end Acceleration of Genomic Workflows on Emerging Heterogeneous Supercomputers

XPS：完整：DSD：新兴异构超级计算机上基因组工作流程的端到端加速

• 批准号: 1439057
• 负责人: Kamesh Madduri
• 金额: $ 85万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1439057&HistoricalAwards=false
• 关键词: XPS; FULL; DSD; End; end

The proposed research harnesses parallelism to accelerate thepervasive bioinformatics workflow of detecting genetic variations.This workflow determines the genetic variants present in anindividual, given DNA sequencing data. The variant detection workflowis an integral part of current genomic data analysis, and severalstudies have linked genetic variants to diseases. Typical instancesof this workflow currently take several hours to multiple days tocomplete with state-of-the-art software, and current algorithms andsoftware are unable to exploit and benefit from even modest levels ofhardware parallelism. Most prior approaches to parallelization andperformance tuning of genomic data analysis pipelines have targetedcomputation, I/O, or network data transfer bottlenecks in isolation,and consequently, are limited in the overall performance improvementthey can achieve. This project targets end-to-end accelerationmethodologies and uses emerging heterogeneous supercomputers toreduce workflow time-to-completion.The project focuses on holistic methodologies to accelerate multiplecomponents within the genetic variant detection workflow. It exploreslightweight data reorganizations at multiple granularities to enhancelocality, investigates compute-, communication-, and I/O taskcotuning, locality-aware load-balancing, and coordinated resourcepartitioning to exploit high-performance computing platforms. A keygoal of the proposed research is to design domain-specificoptimizations targeting the massive parallelism and scalabilitypotential of current heterogeneous supercomputers, so that thedeveloped techniques can be easily transferred and applied to dedicated academic cluster and commercial computational environments.Outreach efforts target undergraduate students through recruitingworkshops and attract them to interdisciplinary graduate programs.Curriculum development activities emphasize cross-layer parallelism.For further information, see project web site at http://sites.psu.edu/XPSGenomics

这项拟议的研究利用并行性来加速普遍存在的检测遗传变异的生物信息学工作流。这个工作流确定在给定DNA测序数据的个体中存在的遗传变异。变异检测工作流程是当前基因组数据分析的一个组成部分，一些研究已经将遗传变异与疾病联系起来。这种工作流程的典型实例目前需要几个小时到几天的时间才能使用最先进的软件完成，而当前的算法和软件甚至无法利用和受益于适度的硬件并行性。大多数已有的基因组数据分析流水线并行化和性能调优方法都孤立地针对计算、I/O或网络数据传输瓶颈，因此，它们所能实现的整体性能改进是有限的。该项目的目标是端到端加速方法，并使用新兴的异质超级计算机来缩短工作流完成时间。该项目专注于整体方法来加速遗传变异检测工作流中的多个组件。它探索了多个粒度的轻量级数据重组以增强局部性，研究了计算、通信和I/O任务协调、位置感知负载平衡和协调资源分区以利用高性能计算平台。这项研究的一个主要目标是针对当前异类超级计算机的大规模并行性和可伸缩性设计特定领域的优化，以便开发的技术可以很容易地转移到专门的学术集群和商业计算环境中。推广努力通过招聘研讨会面向本科生，并吸引他们参加跨学科研究生课程。课程开发活动强调跨层并行。有关更多信息，请参见项目网站http://sites.psu.edu/XPSGenomics