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    NVMalloc: Exposing an Aggregate SSD Store as a Memory Partition in Extreme-Scale Machines
    Update time: 2012-05-31
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    Time: 10:00-12:00 am , June. 5th,2012 (Tuesday)

    Place:Room 446, ICT. CAS

    Speaker: Xiaosong Ma


    DRAM is a precious resource in extreme-scale machines and is increasingly becoming scarce, mainly due to the growing number of cores per node. On future multi-petaflop and exaflop machines, the memory pressure is likely to be so severe that we need to rethink our memory usage models. Fortunately, the advent of non-volatile memory (NVM) offers a unique opportunity in this space. Current NVM offerings possess several desirable properties, such as low cost and power efficiency, but suffer from high latency and lifetime issues. We need rich techniques to be able to use them alongside DRAM.

    In this talk, we present a novel approach for exploiting NVM as a secondary memory partition so that applications can explicitly allocate and manipulate memory regions therein. More specifically, we propose an NVMalloc library with a suite of services that enables applications to access a distributed NVM storage system. We have devised ways within NVMalloc so that the storage system, built from compute node-local NVM devices, can be accessed in a byte-addressable fashion using the memory mapped I/O interface. Our approach has the potential to re-energize out-of-core computations on largescale machines by having applications allocate certain variables through NVMalloc, thereby increasing the overall memory capacity available. Our evaluation on a 128-core cluster shows that NVMalloc enables applications to compute problem sizes larger than the physical memory in a cost-effective manner. It can bring more performance/efficiency gain with increased computation time between NVM memory accesses or increased data access locality. In addition, our results suggest that while NVMalloc enables transparent access to NVM-resident variables, the explicit control it provides is crucial to optimize application performance.


    Xiaosong Ma is currently an Associate Professor in the Department of Computer Science at North Carolina State University. She is also a Joint Faculty in the Computer Science and Mathematics Division at Oak Ridge National Laboratory. Her research interests are in the areas of storage systems, parallel I/O, high-performance parallel applications, cloud computing, and self-configurable performance optimization. She received the DOE Early Career Principal Investigator Award in 2005, the NSF CAREER Award in 2006, an IBM Faculty Award in 2009, and a NetApp Faculty Fellowship in 2012. Prior to joining NCSU, Xiaosong received her Ph.D. in computer science from the University of Illinois at Urbana-Champaign in 2003, and her B.S. in computer science from Peking University, China in 1997.


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