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Scaling Of Biological Data Work Ows To Large Hpc Systems - A Case Study In Marine Genomics -
Scaling Of Biological Data Work Ows To Large Hpc Systems - A Case Study In Marine Genomics -
Sequencing projects, like the Aqua Genome project, generate vast amounts of data which is processed through dif- ferent work ows composed of several steps linked together. Currently, such workflows are often run manually on large servers. With the increasing amount of raw data that approach is no longer feasible. The successful imple- mentation of the project's goals requires 2-3 orders of magnitude scaling of computing, while achieving high reli- ability on and supporting ease-of-use of super computing resources at the same time. We describe two example use cases, the implementation challenges and constraints, the actual application enabling and report our ndings.
- University of Oslo Norway
workflows, magnitude scaling
workflows, magnitude scaling
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1. The Abel Computing Cluster. http://www.uio.no/english/services/it/research/hpc/abel/, [Accessed May 6th, 2014]
2. Centre for Integrative Genetics (CIGENE). http://www.cigene.no, [Accessed May 6th, 2014]
3. The Curie supercomputer. http://www-hpc.cea.fr/en/complexe/tgcc-curie.htm, [Accessed May 26th, 2014]
4. The Galaxy Project. http://galaxyproject.org, [Accessed May 6th, 2014]
5. The Genome Analysis Toolkit (GATK). https://www.broadinstitute.org/gatk/, [Accessed May 6th, 2014]
6. Lifeportal { University of Oslo. http://lifeportal.uio.no/, [Accessed May 6th, 2014]
7. mapDamage. http://ginolhac.github.io/mapDamage/, [Accessed May 6th, 2014]
8. No ma. http://www.nofima.no/en, [Accessed May 6th, 2014]
9. sysstat. https://github.com/sysstat, [Accessed May 6th, 2014]
10. Simple Linux Utility for Resource Management (SLURM). https://computing.llnl.gov/linux/ slurm/, [Accessed May 6th, 2014]
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citations 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).0 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.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations 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).0 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.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!- University of Oslo Norway
Sequencing projects, like the Aqua Genome project, generate vast amounts of data which is processed through dif- ferent work ows composed of several steps linked together. Currently, such workflows are often run manually on large servers. With the increasing amount of raw data that approach is no longer feasible. The successful imple- mentation of the project's goals requires 2-3 orders of magnitude scaling of computing, while achieving high reli- ability on and supporting ease-of-use of super computing resources at the same time. We describe two example use cases, the implementation challenges and constraints, the actual application enabling and report our ndings.