Evidence map›Paper›PMID 36526957›Full record

ArticleBMC bioinformatics2022

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

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

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 47% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Trinity ChengCenter for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.
Pei-Ju ChinCenter for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.
Kenny ChaCenter for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.
Nicholas PetrickCenter for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.
Mike MikailovCenter for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA. Mike.Mikailov@fda.hhs.gov.
Center for Devices and Radiological Health · USCenter for Biologics Evaluation and Research · USUnited States Food and Drug Administration · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

resultsProfiling 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.

Indexed as

Computing MethodologiesSoftwareAlgorithmsComputational BiologySequence AlignmentBLASTHigh performance computing (HPC)Load balancingParallelizationPerformance modelProfiling

Identifiers

PMID36526957
PMCPMC9758941
OpenAlexW4311999292

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.