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- W4315881412 abstract "The new non-volatile memory technology relies on data recoverability to achieve the promise of byte-addressable persistence in computer applications. The durable transaction (e.g. logging) is one of the major persistency programming models to provide recoverable data structures. To achieve performant failure-atomic transactional updates to PM, multi-data-path architectures that separate the data paths for persists are recently explored for CPUs. Considering the importance of GPU as a key computing platform for many application domains, we investigate the multi-data-path architecture for durable transactions to PM in GPU. Our solution, AGPM, exploits an adaptative data-path-selection strategy for the log updates to PM. AGPM reduces the GPU kernels' execution time by at least 24.37% (at most 66.44%) compared to the state-of-the-art designs." @default.
- W4315881412 created "2023-01-13" @default.
- W4315881412 creator A5070716642 @default.
- W4315881412 date "2023-01-11" @default.
- W4315881412 modified "2023-09-26" @default.
- W4315881412 title "Adaptive Data Path Selection for Durable Transaction in GPU Persistent Memory" @default.
- W4315881412 doi "https://doi.org/10.48550/arxiv.2301.04392" @default.
- W4315881412 hasPublicationYear "2023" @default.
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