Genomics GPUs and next generation computational statistics

基因组学 GPU 和下一代计算统计

• 批准号: 8539067
• 负责人: Eric Sobel
• 金额: $ 34.2万
• 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 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8539067
• 关键词: Admixture; Algorithms; Applications Grants; Area; Attention; Bioinformatics; Climate; Code; Communities; Computer software; Computers; Computing Methodologies; Data; Development; Devices; Discipline; Documentation; Dose; Environment; Faculty; Fruit; Gene Proteins; Genes; Genetic; Genomics; Genotype; Grant; Haplotypes; Health Sciences; Human Genome Project; Human Resources; Lead; Libraries; Link; Maps; Methods; Modeling; North Carolina; Philosophy; Process; Production; Protein Isoforms; Quantitative Trait Loci; Research; Research Personnel; Science; Series; Solutions; Suggestion; Technology; Testing; Universities; Variant; Vendor; Work; base; data mining; design; experience; gene discovery; human disease; improved; member; next generation; open source; parallel computing; physical science; programs; software development; statistics; trait; transcriptome sequencing

DESCRIPTION (provided by applicant): With computational demands in genetics growing exponentially, concerns are rising whether traditional CPUs can deliver the needed 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 units (GPUs) offer a far cheaper and more distributed solution. Hundreds of these units are fabricated on a single card, and several cards fit inside a desktop computer. Thus, cheap hardware currently exists that promises a hundred-fold speedup of many basic algorithms. Projections from the vendors of GPUs suggest that these devices will grow rapidly in computational power and versatility over the next decade. Thus, software development is the main hurdle hindering the exploitation of GPUs. This proposal targets this weak link in the chain of modern computing. Through a series of demonstration projects and the production of low-level software libraries, we hope to catalyze the spread of GPUs in genetics. The specific projects include: 1) eQTL mapping, 2) variance component models for QTL mapping, 3) genotype and haplotype construction, 4) estimation of ethnic admixture, 5) isoform discovery through RNA-Seq technology, 6) computation of genetic landscapes and clines, 7) construction of gene networks from random multigraphs, and 8) design of new parallel algorithms for data mining. High-dimensional optimization is a common thread enabling all of these applications. Our previous research on optimization has demonstrated the efficacy of four fundamental ideas, namely, penalized estimation, coordinate descent, the MM (majorization-minimization) principle, and separation of parameters. These ideas also propel parallel computing. Implementation of our demonstration projects on GPUs 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 throughout 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 software with superior documentation shows our commitment to this philosophy.

描述(申请人提供)：随着遗传学上的计算需求呈指数级增长，人们越来越担心传统的CPU能否提供所需的计算能力。并行计算已经被吹捧了几年，但大规模并行CPU计算机非常昂贵，而且仅限于少数几个国家中心。图形处理器(GPU)提供了一种更便宜、更分布式的解决方案。数百个这样的单元被制造在一张卡上，几张卡可以放在一台台式计算机中。因此，目前存在的廉价硬件有望将许多基本算法的加速速度提高100倍。来自图形处理器供应商的预测表明，这些设备在未来十年将在计算能力和多功能性方面迅速增长。因此，软件开发是阻碍GPU开发的主要障碍。这项提议针对的是现代计算链条中的这一薄弱环节。我们希望通过一系列示范项目和低水平软件库的生产，催化GPU在遗传学方面的传播。具体项目包括：1)eQTL定位，2)QTL定位的方差分量模型，3)基因和单倍型构建，4)种族混杂的估计，5)通过RNA-Seq技术发现异构体，6)遗传景观和倾斜度的计算，7)从随机多重图构建基因网络，8)设计新的并行数据挖掘算法。高维优化是支持所有这些应用程序的共同主线。我们以前的优化研究已经证明了四个基本思想的有效性，即惩罚估计、坐标下降、MM(优化-最小化)原则和参数分离。这些想法也推动了并行计算的发展。在图形处理器上实施我们的示范项目将需要产生在计算统计中具有相当普遍价值的子例程。我们打算向开放源码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，从而改善整个健康和物理科学中许多学科的计算环境。根据这项提议制作的所有其他应用程序将免费分发给科学界。我们用卓越的文档制作和分发可用的软件的记录表明了我们对这一理念的承诺。