A novel ReRAM-based processing-in-memory architecture for graph computing

Graph algorithms such as breadth-first search (BFS) have been gaining ever-increasing importance in the era of Big Data. However, the memory bandwidth remains the key performance bottleneck for graph processing. To address this problem, we utilize processing-in-memory (PIM), combined with non-volatile metal-oxide resistive random access memory (ReRAM), to improve the performance of both computation and I/O. The idea is to integrate the computation logic into the memory in which the data accesses are located. We propose and implement a new ReRAM-based processing-in-memory architecture called RPBFS, in which graphs can be processed and persistently stored. We also design an efficient graph traversal scheme. Benefited from low data movement overhead and bank-level parallel computation, RPBFS shows a significant performance improvement compared with both the CPU-based and GPU-based BFS implementations. On a suite of real world graphs, our architecture yields up to 33.8× speedup.