GBTL+Metall – Adding Persistence to GraphBLAS

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dc.contributor.authorVelusamy, Kaushik
dc.contributor.authorMcMillan, Scott
dc.contributor.authorIwabuchi, Keita
dc.contributor.authorPearce, Roger
dc.date.accessioned2021-03-26T18:08:00Z
dc.date.available2021-03-26T18:08:00Z
dc.descriptionNVMW 2021en
dc.description.abstractIt is well known that software-hardware co-design is required for attaining high-performance implementations. System software libraries help us in achieving this goal. Metall persistent memory allocator is one such library. Metall enables large scale data analytics by leveraging emerging memory technologies. Metall is a persistent memory allocator designed to provide developers with rich C++ interfaces to allocate custom C++ data structures in persistent memory, not just from block storage and byte addressable persistent memories (NVMe, Optane) but also in DRAM TempFS. Having a large capacity of persistent memory changes the way we solve problems and leads to algorithmic innovation. In this work, we present GraphBLAS as a real application use case to demonstrate Metall persistent memory allocator benefits. We show an example of how storing and reattaching graph containers using Metall, eliminates the need for graph reconstruction at a one-time cost of re-attaching to Metall datastore.en
dc.description.sponsorshipThis material is based upon work funded and supported by the Department of Defense under Contract No. FA8702-15- D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center [DM21-0180] . This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Lab- oratory under Contract DE-AC52-07NA27344 [LLNL-ABS-819988]. Experiments were performed at the Livermore Computing facility.en
dc.description.urihttp://nvmw.ucsd.edu/nvmw2021-program/nvmw2021-data/nvmw2021-paper61-final_version_your_extended_abstract.pdfen
dc.format.extent2 pagesen
dc.genreconference papers and proceedingsen
dc.identifierdoi:10.13016/m2i5er-r2xo
dc.identifier.citationKaushik Velusamy, Scott McMillan Keita Iwabuchi and Roger Pearce, GBTL+Metall – Adding Persistence to GraphBLAS, http://nvmw.ucsd.edu/nvmw2021-program/nvmw2021-data/nvmw2021-paper61-final_version_your_extended_abstract.pdfen
dc.identifier.urihttp://hdl.handle.net/11603/21230
dc.language.isoenen
dc.publisherNVMWen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsPublic Domain Mark 1.0*
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.titleGBTL+Metall – Adding Persistence to GraphBLASen
dc.typeTexten

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