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 NVM (NVM-only). While all of these applications are sensitive to higher latency or lower bandwidth of NVM, resulting in perfor- mance degradation of up to 4X with NVM-only (compared to DRAM-only), we show that the performance impact is somewhat mitigated in the frameworks that exploit CPU memory-level parallelism and hardware prefetchers. Further, we demonstrate that, in a hybrid memory system with NVM and DRAM, intelligent placement of application data based on their relative importance may help offset the overheads of the NVM-only solution in a cost-effective man- ner (i.e., using only a small amount of DRAM). Specifically, we show that, depending on the algorithm, Graphmat can achieve close to DRAM-only performance (within 1.2X) by placing only 6.7% to 31.5% of its total memory footprint in DRAM