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（由申请人提供）：随着遗传学的计算需求呈指数增长，担心传统CPU是否可以提供所需的计算能力。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）提供了更便宜，更分布的解决方案。这些单元中的数百个单位是在一张卡片上制造的，几张卡片可容纳台式计算机。因此，目前存在的廉价硬件有望在许多基本算法中速度一百倍。 GPU供应商的预测表明，这些设备将在未来十年内迅速增长的计算能力和多功能性。因此，软件开发是阻碍GPU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。通过一系列的示范项目和低级软件库的生产，我们希望催化GPU在遗传学中的传播。具体项目包括：1）EQTL映射，2）QTL映射的方差成分模型，3）基因型和单倍型结构，4）种族混合的估计，5）通过RNA-Seq技术发现的同工型发现，6）6）遗传环境和克林斯的遗传景观和克林斯，7）从遗传网络的构建，7）构建元素的元素，以及8）新设计的新设计。高维优化是启用所有这些应用程序的通用线程。我们先前对优化的研究表明，四个基本思想的功效，即受到惩罚估计，坐标下降，MM（大型最小化）原理和参数的分离。这些想法也推动并行计算。我们在GPU上的演示项目的实施将需要生产计算统计中相当一般价值的子例程。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，从而改善整个健康和物理科学的许多学科的计算环境。根据本提案制定的所有其他申请计划将自由分发给科学界。我们使用出色文档生产和分发可用软件的记录显示了我们对这种理念的承诺。