NodeFetch: High Performance Graph Processing Using Processing in Memory

Background and Objectives: Graph processing is increasingly gaining attention during era of big data. However graph processing applications are highly memory intensive due to nature of graphs. Processing-in-memory (PIM) is an old idea which revisited recently with the advent of technology specifically the ability to manufacture 3D stacked chipsets. PIM puts forward to enrich memory units with computational capabilities to reduce the cost of data movement between processor and memory system. This approach seems to be a way of dealing with large-scale graph processing, considering recent advances in the field. Methods: This paper explores real-world PIM technology to improve graph processing efficiency by reducing irregular access patterns and improving temporal locality using HMC. We propose NodeFetch, a new method to access nodes and their neighbors while processing a graph by adding a new command to HMC system. Results: Results of our simulation on a set of real-world graphs point out that the proposed idea can achieve 3.3x speed up in average and 69% reduction of energy consumption over the baseline PIM architecture which is HMC. Conclusion: Most of the techniques in the field of processing-in-memory, hire methods to reduce movement of data between processor and memory. This paper proposes a method to reduce graph processing execution time and energy consumption by reducing cache misses while processing a graph