Speakers
Description
PanDA - Production and Distributed Analysis Workload Management System has been developed to address ATLAS experiment at LHC data processing and analysis challenges. Recently PanDA has been extended to run HEP scientific applications on Leadership Class Facilities and supercomputers. The success of the projects to use PanDA beyond HEP and Grid has drawn attention from other compute intensive sciences such as bioinformatics.
Modern biology uses complex algorithms and sophisticated software, which is impossible to run without access to significant computing resources. Recent advances of Next Generation Genome Sequencing (NGS) technology led to increasing streams of sequencing data that need to be processed, analysed and made available for bioinformaticians worldwide. Analysis of ancient genomes sequencing data using popular software pipeline PALEOMIX can take a month even running it on the powerful computer resource. PALEOMIX include typical set of software used to process NGS data including adapter trimming, read filtering, sequence alignment, genotyping and phylogenetic or metagenomic analysis. Sophisticated computing software WMS and efficient usage of the supercomputers can greatly enhance this process.
In this paper we will describe the adaptation the PALEOMIX pipeline to run it on a distributed computing environment powered by PanDA. We used PanDA to manage computational tasks on a multi-node parallel supercomputer. To run pipeline we split input files into chunks which are run separately on different nodes as separate inputs for PALEOMIX and finally merge output file, it is very similar to what it done by ATLAS to process and to simulate data. We dramatically decreased the total walltime because of jobs (re)submission automation and brokering within PanDA, what was earlier demonstrated for the ATLAS applications on the Grid. Using software tools developed initially for HEP and Grid can reduce payload execution time for Mammoths DNA samples from weeks to days.
| Primary Keyword (Mandatory) | Data processing workflows and frameworks/pipelines |
|---|---|
| Secondary Keyword (Optional) | Experience/plans from outside experimental HEP/NP |
| Tertiary Keyword (Optional) | High performance computing |
