Genomics, GPUs, and Next Generation Computational Statistics

基因组学、GPU 和下一代计算统计

• 批准号: 8888381
• 负责人: Eric Sobel
• 金额: $ 38.3万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-26 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8888381
• 关键词: Admixture; Algorithm Design; Algorithms; Area; Attention; Big Data; Biomedical Research; Climate; Code; Communities; Computer software; Computers; Computing Methodologies; Data; Data Set; Development; Dimensions; Discipline; Documentation; Dose; Environment; Evaluation; Evolution; Faculty; Funding; Future; Genes; Genetic; Genomics; Genotype; Goals; Grant; Health Sciences; Human Genome Project; Human Resources; Infectious Disease Epidemiology; Lead; Libraries; Link; Maps; Medical Research; Medicine; Mentors; Methods; Mission; Modeling; Performance; Phenotype; Philosophy; Procedures; Process; Production; Protein Isoforms; Research; Research Personnel; Running; Science; Scientist; Solutions; Statistical Algorithm; Suggestion; Techniques; Testing; Training; Variant; Work; Writing; base; design; experience; genetic information; genetic pedigree; human disease; improved; laptop; mathematical sciences; next generation; open source; parallel computer; parallel processing; pathogen; physical science; programs; public health relevance; software development; statistics; theories; trait; transcriptome sequencing; user friendly software

 DESCRIPTION (provided by applicant): With the size of genetic data sets and their computational demands growing exponentially, concerns are rising whether traditional statistical approaches and standard CPUs can deliver the needed analytical and computing power. Parallel computing has been touted for several years, but massively parallel CPU computers are enormously expensive and limited to a few national centers. Graphics processing unit (GPU) and many integrated core (MIC) coprocessors offer a far cheaper and more distributed solution. Each GPU or MIC card can run hundreds of computational threads simultaneously, and several cards ¿t inside a desktop computer. Today, almost all new laptop and desktop computers are equipped with multiple CPU cores and some GPU coprocessor. Thus, cheap hardware currently exists that promises a hundred-fold speedup of many basic computational procedures. Appropriate algorithm design and software development is the main hurdle hindering the exploitation of GPUs and MICs. This proposal targets this weak link in the chain of modern computing. By demonstrating the advantages of massively parallel processing on a few genetic problems, and by distributing general low-level software libraries for these and many other problems, we hope to catalyze the use of GPUs and MICs in genetics. The specific projects include: use of RNA-seq data for the discovery and analysis of isoforms, pedigree-informed genotype imputation, and analysis of pathogens' phenotype evolution. High-dimensional optimization is a common thread enabling these applications. We will pursue a promising new technique for optimization that is particularly well adapted to high dimensions and parallelization, the proximal distance algorithms. This procedure avoids major pitfalls of current state of the art methods, especially shrinkage, which distorts parameter estimates and model selection. Implementation of our demonstration projects on GPUs and MICs will require the production of subroutines of considerable general value in computational statistics. We intend to release our toolbox libraries to the open source community, including C/C++, Fortran, and R software wrappers. This may lead to a multiplier effect that will improve the computing climate in many disciplines through- out the health and physical sciences. All other application programs produced under this proposal will be freely distributed to the scientific community. Our record of producing and distributing usable parallel software with superior documentation shows our commitment to this philosophy.

 描述（由申请人提供）：随着遗传数据集的大小及其计算需求呈指数级增长，人们越来越担心传统的统计方法和标准CPU是否能够提供所需的分析和计算能力。并行计算已经被吹捧了好几年，但是大规模并行CPU计算机非常昂贵，并且仅限于少数几个国家中心。图形处理单元（GPU）和许多集成核心（MIC）协处理器提供了一个更便宜，更分布式的解决方案。每个GPU或MIC卡可以同时运行数百个计算线程，并且桌面计算机中没有几个卡。今天，几乎所有新的笔记本电脑和台式电脑都配备了多个CPU内核和一些GPU协处理器。因此，廉价的硬件目前存在的承诺，许多基本的计算过程的百倍加速。适当的算法设计和软件开发是阻碍GPU和MIC开发的主要障碍。本提案针对现代计算链中的这一薄弱环节。通过展示大规模并行处理在一些遗传学问题上的优势，并通过为这些问题和许多其他问题分发通用低级软件库，我们希望促进GPU和MIC在遗传学中的使用。具体项目包括：使用RNA-seq数据发现和分析亚型、家系信息基因型插补以及分析病原体的表型进化。高维优化是实现这些应用的一个共同思路。我们将追求一个有前途的新技术，特别是适合于高维度和并行化，最近距离算法的优化。这一过程避免了当前最先进方法的主要缺陷，特别是收缩，它扭曲了参数估计和模型选择。在GPU和MIC上实施我们的演示项目将需要在计算统计中产生相当大的一般价值的子程序。我们打算向开源社区发布我们的工具箱库，包括C/C++、Fortran和R软件包装器。这可能会导致乘数效应，将通过健康和物理科学改善许多学科的计算环境。根据本提案制作的所有其他应用程序将免费分发给科学界。我们生产和分发具有上级文档的可用并行软件的记录表明了我们对这一理念的承诺。