  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.
