Presentation
A Locality-Aware Temporal Motif Mining Accelerator with Chunk-Based Search Tree Expansion
DescriptionMining temporal motifs in temporal graphs is essential for many critical applications. Despite several software/hardware temporal motif mining solutions have been proposed, they still suffer from substantial redundant and irregular off-chip communications due to misaligned search tree expansions across different motif matching tasks. In this work, we observe that different tasks traverse the same temporal graph edges in strict chronological order, exhibiting strong data locality among these tasks. Motivated by this insight, we propose LTMiner, a locality-aware hardware accelerator designed to efficiently handle temporal motif mining. Specifically, LTMiner proposes a novel chunk-based search tree expansion mechanism into the accelerator design to align the graph traversals of different tasks at the granularity of data chunks, substantially boosting the data locality among these tasks for lower data access cost. The results show that LTMiner gains 1.1×–652.6×, 1.8×–70.3× speedups and 3.9×–2050.9×, 1.2×–17.3× energy savings compared to the cutting-edge software and hardware solutions, respectively.
Event Type
Research Manuscript
TimeTuesday, July 281:55pm - 2:08pm PDT
LocationMtg Room 202C
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