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

Trinity Cheng^1,2, Pei-Ju Chin³, Kenny Cha¹

¹Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.

BMC Bioinformatics

|December 16, 2022

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Optimizing data partitioning for the Basic Local Alignment Search Tool (BLAST) significantly reduces runtime. This study developed performance models to guide parallelization, achieving substantial speedups for BLASTN jobs on high-performance computing clusters.

Area of Science:

Computational Biology
Bioinformatics
High-Performance Computing

Background:

The Basic Local Alignment Search Tool (BLAST) is crucial for biological sequence matching.
BLAST's performance on large datasets is computationally intensive, necessitating efficient parallelization.
Accurate performance models are key to optimizing data partitioning for parallel BLAST jobs.

Purpose of the Study:

To develop accurate performance models for the nucleotide BLAST application (BLASTN).
To guide data partitioning strategies for minimizing BLASTN runtime on high-performance computing (HPC) clusters.
To investigate the impact of different data partitioning methods on BLASTN execution time.

Main Methods:

Profiling BLASTN using shell, code, and system-level tools.

Keywords:

BLAST High performance computing (HPC)Load balancing Parallelization Performance model Profiling

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Measuring runtimes across diverse node types, database sizes, and query files on a heterogeneous HPC cluster.

Fitting empirical data with quadratic functions to create predictive performance models.

Main Results:

A single function, RunMTBySplitDB, accounts for 99.12% of BLASTN's total runtime.
Five core functions contribute 92.12% to the overall execution time.
Static load balancing based on performance models reduced runtime by 81% on homogeneous and 20% on heterogeneous clusters.

Conclusions:

Optimal data partitioning can improve BLASTN runtime by up to 5.4-fold compared to standard fragmentation.
The developed methodology for performance modeling and data partitioning is applicable to other BLAST+ applications and similar software.