Performance Analysis of a Parallel, Multi-node Pipeline for DNA Sequencing
Post-sequencing DNA analysis typically consists of read mapping followed by variant calling and is very time-consuming, even on a multi-core machine. Recently, we proposed Halvade, a parallel, multi-node implementation of a DNA sequencing pipeline according to the GATK Best Practices recommendations. The MapReduce programming model is used to distribute the workload among different workers. In this paper, we study the impact of different hardware configurations on the performance of Halvade. Benchmarks indicate that especially the lack of good multithreading capabilities in the existing tools (BWA, SAMtools, Picard, GATK) cause suboptimal scaling behavior. We demonstrate that it is possible to circumvent this bottleneck by using multiprocessing on high-memory machines rather than using multithreading. Using a 15-node cluster with 360 CPU cores in total, this results in a runtime of 1 h 31 min. Compared to a single-threaded runtime of \(\sim \)12 days, this corresponds to an overall parallel efficiency of 53 %.
KeywordsDNA sequencing MapReduce Hadoop Cloudera Distributed file systems
This work is funded by Intel, Janssen Pharmaceutica and by the Institute of the Promotion of Innovation through Science and Technology in Flanders (IWT). The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, the Hercules Foundation and the Flemish Government department EWI. Special thanks goes to Stijn De Weirdt for his assistance with the Java wrappers to improve NUMA locality. Benchmarks on Lustre were run at the Intel Big Data Lab, Swindon, UK. We acknowledge the support of Ghent University (Multidisciplinary Research Partnership Bioinformatics: From Nucleotides to Networks).
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