GRAM: Graph Processing in a ReRAM-based Computational Memory

The performance of graph processing for real-world graphs is limited by inefficient memory behaviors in traditional systems because of random memory access patterns. Offloading computations to the memory is a promising strategy to overcome such challenges. In this work, we exploit the resistive memory (ReRAM) based processing-in-memory (PIM) technology to accelerate graph applications. The proposed solution, GRAM, can efficiently executes the 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.