Adaptive Data Path Selection for Durable Transaction in GPU Persistent Memory
Xinjian Long

TL;DR
This paper proposes AGPM, an adaptive data-path selection strategy for durable transactions in GPU persistent memory, significantly improving execution time over existing designs.
Contribution
It introduces a novel adaptive data-path selection approach for GPU persistent memory, enhancing performance of durable transactions.
Findings
Reduces GPU kernel execution time by at least 24.37%.
Achieves up to 66.44% performance improvement.
Demonstrates effectiveness of adaptive data-path selection in GPU persistent memory.
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.
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Taxonomy
TopicsAdvanced Data Storage Technologies · Cloud Computing and Resource Management · Ferroelectric and Negative Capacitance Devices
