Mon 24 Jun 2019 16:40 - 17:00 at 229AB - Memory Management Chair(s): Erez Petrank

Modern data-parallel systems such as Spark rely increasingly on in-memory computing that can significantly improve the efficiency of iterative algorithms. To process real-world datasets, modern data-parallel systems often require extremely large amounts of memory, which are both costly and energy-inefficient. Emerging non-volatile memory (NVM) technologies offers high capacity compared to DRAM and low energy compared to SSDs. Hence, NVMs have the potential to fundamentally change the dichotomy between DRAM and durable storage in Big Data processing. However, most Big Data applications are written in managed languages (e.g., Scala and Java) and executed on top of a managed runtime (e.g., the Java Virtual Machine) that already performs various dimensions of memory management. Supporting hybrid physical memories adds in a new dimension, creating unique challenges in data replacement and migration.

This paper proposes Panthera, a semantics-aware, fully automated memory management technique for Big Data processing over hybrid memories. Panthera analyzes user programs on a Big Data system to infer their coarse-grained access patterns, which are then passed down to the Panthera runtime for efficient data placement and migration. For Big Data applications, the coarse-grained data division is accurate enough to guide GC for data layout, which hardly incurs data monitoring and moving overhead. We have implemented Panthera in OpenJDK and Apache Spark. An extensive evaluation with various datasets and applications demonstrates that Panthera reduces energy by 37 – 52% at only a 1 – 4% execution time overhead.

Mon 24 Jun
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16:00 - 17:00: PLDI Research Papers - Memory Management at 229AB
Chair(s): Erez PetrankTechnion
pldi-2019-papers16:00 - 16:20
Thomas ShullUniversity of Illinois at Urbana-Champaign, Jian HuangUniversity of Illinois at Urbana-Champaign, Josep TorrellasUniversity of Illinois at Urbana-Champaign
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pldi-2019-papers16:20 - 16:40
Bobby PowersUniversity of Massachusetts, Amherst, David TenchUniversity of Massachusetts at Amherst, USA, Emery BergerUniversity of Massachusetts, Amherst, Andrew McGregor
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pldi-2019-papers16:40 - 17:00
Chenxi WangUCLA, Huimin CuiInstitute of Computing Technology, Chinese Academy of Sciences, Ting CaoMicrosoft Research, John ZigmanUniversity of Sydney, Australia, Haris Volos, Onur MutluETH Zurich, Fang LvInstitute of Computing Technology, Chinese Academy of Sciences, Xiaobing FengICT CAS, Guoqing Harry XuUCLA
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