GBTL+Metall – Adding Persistence to GraphBLAS

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nvmw2021-paper61-final_version_your_extended_abstract.pdf (196.03 KB)

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http://hdl.handle.net/11603/21230

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conference papers and proceedings
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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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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.