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Hybrid transactional/analytical processing (HTAP) workloads on graph data can significantly benefit from GPU accelerators. However, to exploit the full potential of GPU processing, dedicated graph representations are necessary, which mostly… Click to show full abstract
Hybrid transactional/analytical processing (HTAP) workloads on graph data can significantly benefit from GPU accelerators. However, to exploit the full potential of GPU processing, dedicated graph representations are necessary, which mostly make in-place updates difficult. In this paper, we discuss an adaptive update handling approach in a graph database system for HTAP workloads. We discuss and evaluate strategies for propagating transactional updates from an update-friendly table storage to a GPU-optimized sparse matrix format for analytics.
Journal Title: Distributed and Parallel Databases
Year Published: 2022
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