High Performance Big Data Graph Analytics Leveraging Near Memory System

Abstract

Big data graph analytics is the future of high performance computing and key to many current and future applications. There is a growing demand for high performance graph computing for real-world social network graphs. Real-world graph algorithms are memory-intensive and generate a high percentage of accesses to the memory subsystem due to low cache locality. Near memory or 3D die-stacked memory, known for its low latency, high bandwidth communication has the potential to improve the performance of big data graph analytics.In this paper, we analyse, evaluate and compare the performance of a near memory system for big data graph analytics. Real-world graphs associated with social networks and the web are processed with graph analytics algorithms in a simulated near memory system. The performance advantage of near memory with a large number of simple in-order processor cores for graph analysis is presented.The proposed system provides a performance per Watt improvement of $3.55 - 8.55 \times$ for Breadth-First Search algorithm for big data graphs over computing systems with fat cores and traditional Double Data Rate (DDR) memory. The proposed near memory computing system provides a considerable improvement in computational performance of graph analytics algorithms with an average improvement in Instructions Per Cycle (IPC) of $5 \times$ and in performance per Watt of $7 \times$.