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https://doi.org/10.1145/370803...
Article . 2025 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2025
License: CC BY
Data sources: Datacite
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Applying Large-Scale Distributed Computing to Structural Bioinformatics – Bridging Legacy HPC Clusters with Big Data Technologies using kafka-slurm-agent

Authors: Pawel Rubach;

Applying Large-Scale Distributed Computing to Structural Bioinformatics – Bridging Legacy HPC Clusters with Big Data Technologies using kafka-slurm-agent

Abstract

This paper presents the Kafka Slurm Agent (KSA), an open source (Apache 2.0 license) distributed computing and stream processing engine designed to help researchers distribute Python-based computational tasks across multiple Slurm-managed HPC clusters and workstations. Written entirely in Python, this extensible framework utilizes an Apache Kafka broker for asynchronous communication between its components. It is intended for non-expert users and does not require administrative privileges or additional libraries to run on Slurm. The framework's development was driven by the introduction of the AlphaFold protein structure prediction model, specifically, it was first created to facilitate the detection of knots in protein chains within structures predicted by AlphaFold. KSA has since been applied to several structural bioinformatics research projects, among others, leading to the discovery of new knotted proteins with previously unknown knot types. These knotted structures are now part of the AlphaKnot 2.0 web server and database, where KSA is applied to manage the knot detection process for user-uploaded structures.

Accepted for the PEARC 2025 conference, 12 pages

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Keywords

FOS: Computer and information sciences, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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