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Memory Transfer Decomposition: Exploring Smart Data Movement through Architecture-Aware Strategies

SessionTenth Workshop on Accelerator Programming and Directives (WACCPD 2023)

DescriptionWe provide an automated framework that utilizes complex hardware links while preserving the simplified abstraction level for the user. Through the decomposition of user-issued memory operations into architecture-aware sub-tasks, we automatically exploit generally underused connections of the system. The operations we support include moving, distribution, and consolidation of memory across the node. For each of them, our Auto-Strategyzer framework proposes a task graph that transparently improves performance, in terms of latency or bandwidth, compared to naive strategies. For our evaluation, we integrated the Auto-Strategyzer as a C++ library into the LLVM-OpenMP runtime infrastructure. We demonstrate that some memory operations can be improved by a factor of 5x compared to naive versions. Integrated into LLVM/OpenMP, our Auto-Strategyzer accelerates cross-device memory movement by a factor of 1.9x, for large transfers, resulting in approx 6% end-to-end execution time decrease for a scientific proxy application.

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