SuperCut
Chenfeng Zhao, Roger D. Chamberlain, Xuan Zhang · 2023
The parallel execution of many graph algorithms is frequently dominated by data communication overheads between compute nodes. This bottleneck becomes even more pronounced in Near-Memory Processing (NMP) architectures with multiple memory cubes as local memory accesses are less expensive. Existing near-memory architectures typically use graph partitioning methods with a fixed vertex assignment, which limits their potential to improve performance and reduce energy consumption. Here, we argue that an NMP-based graph processing system should also consider the distribution of vertices onto memory cubes. We propose SuperCut, a framework for near-memory architectures to effectively reduce communication overheads while maintaining computational balance. We evaluate SuperCut via architectural simulation with 6 real-world datasets and 4 representative applications. The results show that it provides up to 1.8x total energy reduction and 2.6x speedup relative to current state-of-the-art approaches.