Abstract
Large-capacity memory system allows big data applications to load as much data as possible for in-memory processing, which improves application performance. However, DRAM faces both scalability and energy challenges due to its inherent charging mechanism. Thus, DRAM-based memory system incurs excessive cost to meet both capacity and energy requirements for the emerging big data workloads. Fortunately, non-volatile memories(NVMs) are emerging with the advanced features of better scalability and lower power leakage. Integrating NVMs into main memory is non-trivial as NVMs have a few weakness, such as asymmetric read and write latency and power. Designing memory system comprising both DRAM and NVMs requires to understand the memory access behaviors of big data applications. In this paper, we first investigate the memory access patterns of both typical big data workloads and traditional parallel workloads. By doing so, we show the read/write intensity as well as temporal/spatial locality of big data workloads. We then replay memory access traces of big data applications to DRAM simulator and PCM simulator, respectively. We explore design implications of hybrid memory comprising DRAM and PCM.
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Wei, W., Jiang, D., Xiong, J., Chen, M. (2014). Exploring Opportunities for Non-volatile Memories in Big Data Applications . In: Zhan, J., Han, R., Weng, C. (eds) Big Data Benchmarks, Performance Optimization, and Emerging Hardware. BPOE 2014. Lecture Notes in Computer Science(), vol 8807. Springer, Cham. https://doi.org/10.1007/978-3-319-13021-7_16
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DOI: https://doi.org/10.1007/978-3-319-13021-7_16
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