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Designing Convergent HPC and Big Data Software Stacks: An Overview of the HiBD Project

DK Panda, Ohio State University

In this video from the 2019 Stanford HPC Conference, DK Panda from Ohio State University presents: Designing Convergent HPC and Big Data Software Stacks: An Overview of the HiBD Project.

This talk will provide an overview of challenges in designing convergent HPC and BigData software stacks on modern HPC clusters. An overview of RDMA-based designs for Hadoop (HDFS, MapReduce, RPC and HBase), Spark, Memcached, Swift, and Kafka using native RDMA support for InfiniBand and RoCE will be presented. Enhanced designs for these components to exploit HPC scheduler (SLURM), parallel file systems (Lustre) and NVM-based in-memory technology will also be presented. Benefits of these designs on various cluster configurations using the publicly available RDMA-enabled packages from the OSU HiBD project will be shown.

DK Panda is a Professor and University Distinguished Scholar of Computer Science and Engineering at the Ohio State University. He has published over 450 papers in the area of high-end computing and networking. The MVAPICH2 (High Performance MPI and PGAS over InfiniBand, Omni-Path, iWARP and RoCE) libraries, designed and developed by his research group (http://mvapich.cse.ohio-state.edu), are currently being used by more than 2,950 organizations worldwide (in 85 countries). More than 518,000 downloads of this software have taken place from the project’s site. This software is empowering several InfiniBand clusters (including the 3rd, 14th, 17th, and 27th ranked ones) in the TOP500 list. The RDMA packages for Apache Spark, Apache Hadoop and Memcached together with OSU HiBD benchmarks from his group (http://hibd.cse.ohio-state.edu) are also publicly available. These libraries are currently being used by more than 300 organizations in 35 countries. More than 28,900 downloads of these libraries have taken place. High-performance and scalable versions of the Caffe and TensorFlow framework are available from https://hidl.cse.ohio-state.edu.
Prof. Panda is an IEEE Fellow.

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