GRAM: Graph Processing in a ReRAM-based Computational Memory

The performance of graph processing for real-world graphs is limited by inefficient memory behaviours in traditional systems because of random memory access patterns. Offloading computations to the memory is a promising strategy to overcome such challenges. In this paper, we exploit the resistive memory (ReRAM) based processing-in-memory (PIM) technology to accelerate graph applications. The proposed solution, GRAM, can efficiently executes vertex-centric model, which is widely used in large-scale parallel graph processing programs, in the computational memory. The hardware-software co-design used in GRAM maximizes the computation parallelism while minimizing the number of data movements. Based on our experiments with three important graph kernels on seven real-world graphs, GRAM provides 122.5x and 11.1x speedup compared with an in-memory graph system and optimized multi-threading algorithms running on a multi-core CPU. Compared to a GPU-based graph acceleration library and a recently proposed PIM accelerator, GRAM improves the performance by 7.1x and 3.8x respectively.