GBTL+Metall – Adding Persistence to GraphBLAS
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Kaushik 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.pdf
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This 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
Public Domain Mark 1.0
This 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
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Abstract
It 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.