Presentation
Dypamear: Efficient and Scalable Dynamic Graph Pattern Mining on Practical Processing-in-Memory Architecture
DescriptionDynamic Graph Pattern Mining (DGPM) has been widely applied in various domains. However, existing solutions still suffer from severe memory access bottlenecks due to the irregular and data-intensive nature of DGPM workloads. In this paper, we propose DyPamear, the first full-stack hardware-software co-designed system for accelerating DGPM on practical Processing-in-Memory (PIM) hardware. DyPamear is built atop UPMEM, an emerging commercially available PIM platform. To fully exploit UPMEM's bandwidth and parallelism, DyPamear introduces a cross-layer design that integrates load-aware task distribution, data-driven asynchronous execution, and a degree-adaptive set intersection kernel to balance load and alleviate architectural constraints. Evaluations on real UPMEM hardware show that DyPamear achieves average speedups of 267.38x, 82.52x, and 8.78x over Cheetah, PimPam, and PSMiner, respectively, and scales nearly linearly to 20,480 DPUs. The source codes are available at https://github.com/DyPamear-AE/DyPamear-AE.
Event Type
Research Manuscript
TimeWednesday, July 2911:30am - 11:45am PDT
LocationMtg Room 203AB