GAS A Heterogeneous Memory Architecture for Graph Processing

Graph processing has become important for various applications in today's big data era. However, most graph processing applications suffer from large memory overhead due to random memory accesses. Such random memory access pattern provides little temporal and spatial locality which cannot be accelerated by the conventional hierarchical memory system. In this work, we propose GAS, a heterogeneous memory architecture, to accelerate graph applications implemented in message-based vertex program model, which is widely used in various graph processing systems. GAS utilizes the specialized content-addressable memory (CAM) to store random data, and determine exact access patterns by a series of associative search. Thus, GAS not only removes the inefficiency of random accesses but also reduces the memory access latency by accurate prefetching. We test the efficiency of GAS with three important graph processing kernels on five well-known graphs. Our experimental results show that GAS can significantly reduce cache miss rate and improve the bandwidth utilization as compared to a conventional system with a state-of-the-art graph-specific prefetching mechanism. These enhancements result in 34% and 27% reduction in energy consumption and execution time, respectively.