Exploiting NVM in large-scale graph analytics

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Abstract
Data center applications like graph analytics require servers with ever larger memory capacities. DRAM scaling, however, is not able to match the increasing demands for capacity. 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 suitability 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 performance with popular algorithms (Pagerank, BFS, Triangle Counting and Collaborative filtering) by allocating memory 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 performance 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 manner (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.

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