Exploiting NVM in Large-scale Graph Analytics

Data center applications like graph analytics require servers with ever larger memory capacities. DRAM scaling, how-ever, is not able to match the increasing demands for ca-pacity. Emerging byte-addressable, non-volatile memory technologies (NVM) offer a more scalable alternative, with memory that is directly addressable to software, but at a higher latency and lower bandwidth. Using an NVM hardware emulator, we study the suitabil-ity of NVM in meeting the memory demands of four state of the art graph analytics frameworks, namely Graphlab, Galois, X-Stream and Graphmat. We evaluate their perfor-mance with popular algorithms (Pagerank, BFS, Triangle Counting and Collaborative filtering) by allocating mem-ory exclusive from DRAM (DRAM-only) or emulated NV