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dc.contributor.authorCadenelli, Nicola
dc.contributor.authorPolo Bardés, Jordà
dc.contributor.authorCarrera, David
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2018-07-10T13:51:58Z
dc.date.available2018-07-10T13:51:58Z
dc.date.issued2018-02-15
dc.identifier.citationCadenelli, N.; Polo, J.; Carrera, D. Accelerating K-mer Frequency Counting with GPU and Non-Volatile Memory. A: "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)". IEEE, 2018, p. 434-441.
dc.identifier.isbn978-1-5386-2588-0
dc.identifier.urihttp://hdl.handle.net/2117/119211
dc.description.abstractThe emergence of Next Generation Sequencing (NGS) platforms has increased the throughput of genomic sequencing and in turn the amount of data that needs to be processed, requiring highly efficient computation for its analysis. In this context, modern architectures including accelerators and non-volatile memory are essential to enable the mass exploitation of these bioinformatics workloads. This paper presents a redesign of the main component of a state-of-the-art reference-free method for variant calling, SMUFIN, which has been adapted to make the most of GPUs and NVM devices. SMUFIN relies on counting the frequency of k-mers (substrings of length k) in DNA sequences, which also constitutes a well-known problem for many bioinformatics workloads, such as genome assembly. We propose techniques to improve the efficiency of k-mer counting and to scale-up workloads like SMUFIN that used to require 16 nodes of Marenostrum 3 to a single machine with a GPU and NVM drives. Results show that although the single machine is not able to improve the time to solution of 16 nodes, its CPU time is 7.5x shorter than the aggregate CPU time of the 16 nodes, with a reduction in energy consumption of 5.5x.
dc.description.sponsorshipThis project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493). We are also grateful to SandDisk for lending the FusionIO cards and to Nvidia who donated the Tesla K40c.
dc.format.extent8 p.
dc.language.isoeng
dc.publisherIEEE
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshGenome
dc.subject.lcshGPU
dc.subject.otherBioinformatics
dc.subject.otherGenomics
dc.subject.otherGraphics processing units
dc.subject.otherNonvolatile memory
dc.subject.otherInstruction sets
dc.subject.otherAcceleration
dc.subject.otherDNA
dc.titleAccelerating K-mer Frequency Counting with GPU and Non-Volatile Memory
dc.typeConference lecture
dc.subject.lemacGenomes
dc.subject.lemacSupercomputadors
dc.identifier.doi10.1109/HPCC-SmartCity-DSS.2017.57
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8291960/
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
dc.relation.projectideu-repo/grantAgreement/MINECO/PE2013-2016/TIN2015-65316-P
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/639595/EU/Holistic Integration of Emerging Supercomputing Technologies/Hi-EST
local.citation.publicationName2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
local.citation.startingPage434
local.citation.endingPage441


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