XPS: FULL: DSD: Collaborative Research: Parallelizing and Accelerating Metagenomic Applications

XPS：完整：DSD：协作研究：并行化和加速宏基因组应用

• 批准号: 1533933
• 负责人: Yuan Xie
• 金额: $ 54.8万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533933&HistoricalAwards=false
• 关键词: XPS; FULL; DSD; Collaborative; Research

The importance of metagenomics arises from the fact that over 99% ofthe species yet to be discovered are resistant to cultivation. Unlikesingle genome sequencing, assembly of a metagenome is intractable andis in large part, an unsolved mystery. Moreover, the advent of highthroughput sequencing is fueling rapid generation of enormousmetagenomic datasets. There is no available sequenced genome for amajority of the species. There is a need to determine the number of species ina metagenomic dataset as well as the abundance of each of thesespecies. The key steps (Assembly and Clustering) in the metagenomicsanalysis algorithms are compute-intensive, while the sheer amount ofdata the algorithms operate on is staggering. The most promising wayto tackle the computational challenges is to build special purposehardware, dedicated solely to suitable algorithms.The main objective of this project is to develop a range of flexible,affordable, parallel, fast hardware-accelerated bioinformaticssolutions, using GPGPU, FPGA, and ASIC, for metagenomic analytics to provide an alternative toexpensive computer clusters. Specifically, hardware solutions formetagenomic clustering and assembly will be developed. Severalacceleration methodologies, including parallel software mapping andspecial hardware design, are proposed to explore the parallelisminside the applications and to improve the data access bandwidth inthe hardware running bioinformatics applications. The ideas proposedin this work will be evaluated in a multi-pronged manner using acombination of simulation, emulation and prototyping efforts. Further,the PIs will use a combination of commercial tools, collaboratorresources and existing internal tools. The research will be conducted in collaboration with industrial partners. Through closecollaboration with several industry partners, direct transfer of manyideas to industry is enabled. The outcome of this research will,therefore, have a direct impact on future bioinformatics applicationsolutions. This project will involve graduate and undergraduatestudents in all aspects of the research. The PIs will activelyintegrate the research results from this project into the graduate andundergraduate curricula, and develop new interdisciplinary courses onbioinformatics and computer architecture to train the next generationwork-force. Finally, the tools and techniques developed in thisresearch will be made available through web-sites for use by othereducators, researchers, and industry practitioners.

宏基因组学的重要性源于以下事实：尚未发现的物种中有99％对培养具有抵抗力。不可能的基因组测序，元素组的组装在很大程度上是棘手的，在很大程度上是一个未解决的谜团。 此外，高通量测序的出现正在加剧巨大的元素数据集的快速生成。该物种的交流基因组没有可用的测序基因组。有必要确定INA元基因组数据集的物种数量以及每个元素的丰度。 元基因组分析算法中的关键步骤（组装和聚类）是计算密集型的，而算法运行的庞大数量令人震惊。 应对计算挑战的最有前途的方法是建立专门用于合适的算法的特殊用途智能软件。该项目的主要目标是使用GPGPGA，FPGA，FPGA，for Metagenomic clsitics clticativative for Metagent to Expics，开发了一系列灵活，可负担，平行，快速加速的生物信息固定。具体而言，将开发硬件解决方案甲元素聚类和组装。提出了一些方法，包括并行软件映射和专业硬件设计，以探索ParallelismInside应用程序的应用程序，并改善运行生物信息信息技术应用程序的硬件中的数据访问带宽。提出的想法将通过模拟，仿真和原型制作的依据来评估这项工作。此外，PI将使用商业工具，协作术和现有内部工具的组合。该研究将与工业伙伴合作进行。通过与几个行业合作伙伴的关闭定位，可以直接转移到行业。因此，这项研究的结果将直接影响未来的生物信息学应用程序。该项目将涉及研究的各个方面的研究生和本科生。 PI将将该项目的研究结果积极地融合到研究生和教学课程中，并开发新的跨学科课程OnBioDormitics和计算机架构来培训下一代工作。最后，将在本研究中开发的工具和技术通过网站提供，以供其他教育者，研究人员和行业从业人员使用。

项目成果

MEDAL: Scalable DIMM based Near Data Processing Accelerator for DNA Seeding Algorithm

• DOI: 10.1145/3352460.3358329
• 发表时间: 2019-10
• 期刊: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture
• 作者: Wenqin Huangfu;Xueqi Li;Shuangchen Li;Xing Hu;P. Gu;Yuan Xie