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The new non-volatile memory technology relies on data recoverability toachieve the promise of byte-addressable persistence in computer applications.The durable transaction (e.g. logging) is one of the major persistencyprogramming models to provide recoverable data structures. To achieveperformant failure-atomic transactional updates to PM, multi-data-patharchitectures that separate the data paths for persists are recently exploredfor CPUs. Considering the importance of GPU as a key computing platform formany application domains, we investigate the multi-data-path architecture fordurable transactions to PM in GPU. Our solution, AGPM, exploits an adaptativedata-path-selection strategy for the log updates to PM. AGPM reduces the GPUkernels' execution time by at least 24.37% (at most 66.44%) compared to thestate-of-the-art designs.

Adaptive Data Path Selection for Durable Transaction in GPU Persistent Memory