多区域异质系统基因组变异检测的并行加速

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Yaning Yang;Xiaoqi Wang;Chengqing Li;Shaoliang Peng
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引用次数: 0

摘要

基因组变异对了解疾病的遗传基础至关重要。Pindel是一种广泛使用的结构变异调用器,利用短读测序数据在单碱基分辨率下检测变异;然而,其热点模块施加了大量的计算需求,限制了大规模全基因组分析的效率。异构体系结构提供了一个很有前途的解决方案,但是硬件设计和编程模型的差异阻碍了原始算法的直接移植。为了解决这个问题,我们引入了MTPindel,这是一种为MT-3000处理器量身定制的新型异构并行优化框架。我们专注于Pindel最计算密集型的模块,设计了多核和任务级并行算法,利用MT-3000的加速器域来平衡和加速工作负载分配。在配备mt -3000的128个天河下一代超级计算机节点上,MTPindel实现了122.549倍的加速和95.74%的并行效率,相对于原始实现只有0.74%的误差。这项工作代表了异质并行化变异检测的开创性努力,为研究和临床环境中的快速、大规模基因组分析铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel Acceleration of Genome Variation Detection on Multi-Zone Heterogeneous System
Genomic variation is critical for understanding the genetic basis of disease. Pindel, a widely used structural variant caller, leverages short-read sequencing data to detect variation at single-base resolution; however, its hotspot module imposes substantial computational demands, limiting efficiency in large-scale whole-genome analyses. Heterogeneous architectures offer a promising solution, yet disparities in hardware design and programming models preclude direct porting of the original algorithm. To address this, we introduce MTPindel, a novel heterogeneous parallel optimization framework tailored to the MT-3000 processor. Focusing on Pindel’s most compute-intensive modules, we design multi-core and task-level parallel algorithms that exploit the MT-3000’s accelerator domains to balance and accelerate workload distribution. On 128 MT-3000–equipped nodes of the Tianhe next-generation supercomputer, MTPindel achieves an impressive 122.549 times of speedup and 95.74% parallel efficiency, with only a 0.74% error margin relative to the original implementation. This work represents a pioneering effort in heterogeneous parallelization for variant detection, paving the way for rapid, large-scale genomic analyses in research and clinical settings.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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