SilverChunk: An Efficient In-Memory Parallel Graph Processing System

Abstract

One of the main constructs of graph processing is the two-level nested loop structure. Parallelizing nested loops is notoriously unfriendly to both CPU and memory access when dealing with real graph data due to its skewed distribution. To address this problem, we present SilverChunk, a high performance graph processing system. SilverChunk builds edge chunks of equal size from original graphs and unfolds nested loops statically in pull-based executions (VR-Chunk) and dynamically in push-based executions (D-Chunk). VR-Chunk slices the entire graph into several chunks. A virtual vertex is generated pointing to the first half of each sliced edge list so that no edge list lives in more than one chunk. D-Chunk builds its chunk list via binary searching over the prefix degree sum array of the active vertices. Each chunk has a local buffer for conflict-free maintenance of the next frontier. By changing the units of scheduling from edges to chunks, SilverChunk achieves better CPU and memory utilization. SilverChunk provides a high level programming interface combined with multiple optimization techniques to help developing efficient graph processing applications. Our evaluation results reveal that SilverChunk outperforms state-of-the-art shared-memory graph processing systems by up to 4 ×, including Gemini, Grazelle, etc. Moreover, it has lower memory overheads and nearly zero pre-processing time.