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Designing HPC, Big Data, & Deep Learning Middleware for Exascale

DK Panda, Ohio State University

In this video from HPC Advisory Council Spain Conference, DK Panda from Ohio State University presents: Designing HPC, Big Data & Deep Learning Middleware for Exascale.

“This talk will focus on challenges in designing HPC, Big Data, and Deep Learning middleware for Exascale systems with millions of processors and accelerators. For the HPC domain, we will discuss about the challenges in designing runtime environments for MPI+X (PGAS OpenSHMEM/UPC/CAF/UPC++, OpenMP, and CUDA) programming models. Features and sample performance numbers from MVAPICH2 libraries will be presented. For the Big Data domain, we will focus on high-performance and scalable designs of Spark, Hadoop (including HDFS, MapReduce, RPC, and HBase), and Memcached using native RDMA support for InfiniBand and RoCE. For the Deep Learning domain, we will focus on popular Deep Learning frameworks (Caffe, CNTK, and TensorFlow) to extract performance and scalability with MVAPICH2-GDR MPI library and RDMA-Enabled Big Data stacks.”

Dr. Dhabaleswar K. (DK) Panda is a Professor and Distinguished Scholar of Computer Science at the Ohio State University. He obtained his Ph.D. in computer engineering from the University of Southern California. His research interests include parallel computer architecture, high performance networking, InfiniBand, network-based computing, exascale computing, programming models, GPUs and accelerators, high performance file systems and storage, virtualization and cloud computing and BigData (Hadoop, HDFS, MapReduce, HBase, and Memcached). He has published over 400 papers in major journals and international conferences related to these research areas.

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