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Accelerating Big Data Processing and Management on Modern HPC Clusters
时间:2017-04-09 09:32    点击:   所属单位:通信工程学院
讲座名称 Accelerating Big Data Processing and Management on Modern HPC Clusters
讲座时间 2017-04-10 09:30:00
讲座地点 北校区新科技楼 609会议室
讲座人 Dr. Xiaoyi Lu
讲座人介绍 Dr. Xiaoyi Lu is a Research Scientist in the Department of Computer Science and Engineering at the Ohio State University, USA. His current research interests include high-performance interconnects and protocols, Big Data technologies and ecosystems, Parallel Computing Models (MPI/PGAS), Virtualization and Cloud Computing. He has published over 80 papers in International journals, conferences, and book chapters related to these research areas. He has been actively involved in various professional activities in academic journals and conferences. Recently, Dr. Lu is leading the research and development of RDMA-based accelerations for Apache Hadoop, Spark, HBase, and Memcached, and OSU HiBD micro-benchmarks, which are publicly available from the HiBD project website (http://hibd.cse.ohio-state.edu). These libraries are currently being used by more than 215 organizations from 29 countries. More than 21,100 downloads of these libraries have taken place from the project site. He is also leading the research and development of the MVAPICH2-Virt (high-performance and scalable MPI for hypervisor and container based HPC cloud) project. More details about Dr. Lu are available at http://web.cse.ohio-state.edu/~luxi/.
讲座内容 Modern HPC systems and the associated middleware (such as MPI and parallel file systems) have been exploiting the advances in HPC technologies (multi/many-core architecture, RDMA-enabled networking, and SSD) during the last decade. However, Big Data processing and management middleware (such as Hadoop, Spark, and Memcached) have not embraced such technologies. These disparities are taking HPC and Big Data processing into 'divergent trajectories'. This talk will provide an overview of challenges in accelerating Big Data processing and management middleware on modern clusters through HPC technologies. An overview of advanced designs based on RDMA and heterogeneous storage architecture for multiple components of Hadoop (HDFS, MapReduce, RPC and HBase), Spark, and Memcached will be presented.  Benefits of these designs on various cluster configurations will be shown. The talk will also address the need for designing benchmarks using a multi-layered approach, which can be used to evaluate the performance of these middleware.  All these work aim to bring HPC and Big Data processing into a 'convergent trajectory'.
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