Profiling the BLAST bioinformatics application for load balancing on high-performance computing clusters

Trinity Cheng; Pei-Ju Chin; Kenny Cha; Nicholas Petrick; Mike Mikailov

doi:10.1186/s12859-022-05029-7

Profiling the BLAST bioinformatics application for load balancing on high-performance computing clusters

BMC Bioinformatics. 2022 Dec 16;23(1):544. doi: 10.1186/s12859-022-05029-7.

Authors

Trinity Cheng^{1

2}, Pei-Ju Chin³, Kenny Cha¹, Nicholas Petrick¹, Mike Mikailov⁴

Affiliations

¹ Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.
² Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
³ Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.
⁴ Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA. Mike.Mikailov@fda.hhs.gov.

Abstract

Background: The Basic Local Alignment Search Tool (BLAST) is a suite of commonly used algorithms for identifying matches between biological sequences. The user supplies a database file and query file of sequences for BLAST to find identical sequences between the two. The typical millions of database and query sequences make BLAST computationally challenging but also well suited for parallelization on high-performance computing clusters. The efficacy of parallelization depends on the data partitioning, where the optimal data partitioning relies on an accurate performance model. In previous studies, a BLAST job was sped up by 27 times by partitioning the database and query among thousands of processor nodes. However, the optimality of the partitioning method was not studied. Unlike BLAST performance models proposed in the literature that usually have problem size and hardware configuration as the only variables, the execution time of a BLAST job is a function of database size, query size, and hardware capability. In this work, the nucleotide BLAST application BLASTN was profiled using three methods: shell-level profiling with the Unix "time" command, code-level profiling with the built-in "profiler" module, and system-level profiling with the Unix "gprof" program. The runtimes were measured for six node types, using six different database files and 15 query files, on a heterogeneous HPC cluster with 500+ nodes. The empirical measurement data were fitted with quadratic functions to develop performance models that were used to guide the data parallelization for BLASTN jobs.

Results: Profiling results showed that BLASTN contains more than 34,500 different functions, but a single function, RunMTBySplitDB, takes 99.12% of the total runtime. Among its 53 child functions, five core functions were identified to make up 92.12% of the overall BLASTN runtime. Based on the performance models, static load balancing algorithms can be applied to the BLASTN input data to minimize the runtime of the longest job on an HPC cluster. Four test cases being run on homogeneous and heterogeneous clusters were tested. Experiment results showed that the runtime can be reduced by 81% on a homogeneous cluster and by 20% on a heterogeneous cluster by re-distributing the workload.

Discussion: Optimal data partitioning can improve BLASTN's overall runtime 5.4-fold in comparison with dividing the database and query into the same number of fragments. The proposed methodology can be used in the other applications in the BLAST+ suite or any other application as long as source code is available.

Keywords: BLAST; High performance computing (HPC); Load balancing; Parallelization; Performance model; Profiling.

MeSH terms

Algorithms
Computational Biology / methods
Computing Methodologies*
Sequence Alignment
Software*