Scaling bioinformatics applications on HPC

BMC Bioinformatics

Research

Shraddha Thakkar¹  Zhichao Liu¹  Weida Tong¹  Nicholas Petrick²  Mike Mikailov²  Fu-Jyh Luo²  Lohit Valleru²  Stephen Whitney²  Stuart Barkley² 

[1] Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, 72079, Jefferson, AR, USA;Office of Science and Engineering Labs, Center for Devices and Radiological Health, US Food and Drug Administration, 10903 New Hampshire Ave., 20993, Silver Spring, MD, USA;

关键词: HPC;    Blast;    Parallelization;    MPI;    Multi-threading;    Bioinformatics;    Array jobs;    Next generation sequencing;    Grid engine;    Cluster;   

DOI  :  10.1186/s12859-017-1902-7

来源: Springer

【 摘 要 】

BackgroundRecent breakthroughs in molecular biology and next generation sequencing technologies have led to the expenential growh of the sequence databases. Researchrs use BLAST for processing these sequences. However traditional software parallelization techniques (threads, message passing interface) applied in newer versios of BLAST are not adequate for processing these sequences in timely manner.MethodsA new method for array job parallelization has been developed which offers O(T) theoretical speed-up in comparison to multi-threading and MPI techniques. Here T is the number of array job tasks. (The number of CPUs that will be used to complete the job equals the product of T multiplied by the number of CPUs used by a single task.) The approach is based on segmentation of both input datasets to the BLAST process, combining partial solutions published earlier (Dhanker and Gupta, Int J Comput Sci Inf Technol_5:4818-4820, 2014), (Grant et al., Bioinformatics_18:765-766, 2002), (Mathog, Bioinformatics_19:1865-1866, 2003). It is accordingly referred to as a “dual segmentation” method. In order to implement the new method, the BLAST source code was modified to allow the researcher to pass to the program the number of records (effective number of sequences) in the original database. The team also developed methods to manage and consolidate the large number of partial results that get produced. Dual segmentation allows for massive parallelization, which lifts the scaling ceiling in exciting ways.ResultsBLAST jobs that hitherto failed or slogged inefficiently to completion now finish with speeds that characteristically reduce wallclock time from 27 days on 40 CPUs to a single day using 4104 tasks, each task utilizing eight CPUs and taking less than 7 minutes to complete.ConclusionsThe massive increase in the number of tasks when running an analysis job with dual segmentation reduces the size, scope and execution time of each task. Besides significant speed of completion, additional benefits include fine-grained checkpointing and increased flexibility of job submission. “Trickling in” a swarm of individual small tasks tempers competition for CPU time in the shared HPC environment, and jobs submitted during quiet periods can complete in extraordinarily short time frames. The smaller task size also allows the use of older and less powerful hardware. The CDRH workhorse cluster was commissioned in 2010, yet its eight-core CPUs with only 24GB RAM work well in 2017 for these dual segmentation jobs. Finally, these techniques are excitingly friendly to budget conscious scientific research organizations where probabilistic algorithms such as BLAST might discourage attempts at greater certainty because single runs represent a major resource drain. If a job that used to take 24 days can now be completed in less than an hour or on a space available basis (which is the case at CDRH), repeated runs for more exhaustive analyses can be usefully contemplated.

【 授权许可】

CC BY   
© The Author(s). 2017