Genomics, GPUs, and Next Generation Computational Statistics

基因组学、GPU 和下一代计算统计

• 批准号: 9100873
• 负责人: Eric Sobel
• 金额: $ 37.8万
• 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/9100873
• 关键词: Admixture; Algorithm Design; Algorithms; Area; Attention; Big Data; Biomedical Research; Climate; Code; Communities; Computer software; Computers; Computing Methodologies; Data; Data Discovery; Data Set; Development; Dimensions; Discipline; Documentation; Dose; Environment; Evaluation; Evolution; Faculty; Funding; Future; Genes; Genetic; Genomic medicine; Genomics; Genotype; Goals; Grant; Health; Health Sciences; Human Genome Project; Human Resources; Infectious Disease Epidemiology; Lead; Libraries; Link; Maps; Medical Research; Mentors; Methods; Mission; Modeling; Performance; Phenotype; Philosophy; Procedures; Process; Production; Protein Isoforms; Research; Research Personnel; Running; Science; Scientist; Statistical Algorithm; Statistical Data Interpretation; Suggestion; Techniques; Testing; Training; Variant; Work; Writing; base; big biomedical data; design; experience; genetic information; genetic pedigree; genomic data; human disease; improved; laptop; mathematical sciences; next generation; open source; parallel computer; parallel processing; parallelization; pathogen; physical science; programs; 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（由适用提供）：随着遗传数据集的规模及其计算需求呈指数增长，担心是否会引起传统的统计方法和标准CPU是否可以提供所需的分析和计算能力。并行计算已经吹捧了几年，但是大规模平行的CPU计算机非常昂贵，并且仅限于几个国家中心。图形处理单元（GPU）和许多集成的核心（MIC）协处理器提供了更便宜，更分布的解决方案。每张GPU或MIC卡可以简单地运行数百个计算线程，而在台式计算机中可以运行几张卡。如今，几乎所有新的笔记本电脑和台式计算机都配备了多个CPU内核和一些GPU协处理器。这是当前存在的廉价硬件，有望在许多基本计算过程中加快一百倍的速度。适当的算法设计和软件开发是阻碍GPU和MIC的开发的主要障碍。该建议针对现代计算链中的这种薄弱环节。通过证明在一些遗传问题上进行大规模并行处理的优势，并通过为这些和许多其他问题分发一般的低级软件库，我们希望能够催化GPU和MIC在遗传学中的使用。特定项目包括：使用RNA-Seq数据在发现和分析同工型，谱系基因型插补以及病原体表型演化的分析中。高维优化是实现这些应用程序的通用线程。我们将追求一种有希望的新技术进行优化，该技术特别适合于高维和并行化，即近端距离算法。此过程避免了当前最新方法的主要陷阱，尤其是收缩，这会扭曲参数估计和模型选择。我们对GPU和MIC的演示项目的实施将需要在计算统计中生产考虑一般价值的子例程。我们打算将工具箱库发布到开源社区，包括C/C ++，Fortran和R软件包装纸。这可能会导致乘数效应，该效应将改善许多学科的计算氛围，从而贯穿健康和物理科学。根据本提案制定的所有其他申请计划将免费分发给科学界。我们使用出色文档生产和分发可用并行软件的记录显示了我们对这种理念的承诺。