June 20, 2019, Thursday

  • 14:00 – 14:05 Introduction (Hartwig Anzt): [ PDF ]

  • 14:05 – 14:55 Keynote: Dhabaleswar K. (DK) Panda (The Ohio State University)

    Title: Scalable and Distributed DNN Training on Modern HPC Systems: Challenges and Solutions [ PDF ]

    Abstract: This talk will start with an overview of challenges being faced by the AI community to achieve scalable and distributed DNN training on Modern HPC systems. Next, an overview of the emerging HPC technologies will be provided. Next, we will focus on a range of solutions to bring together HPC and Deep Learning together to address the challenges in scalable and distributed DNN training. Solutions along the following directions will be presented: 1) MPI-driven Deep Learning for CPU- based and GPU-based clusters, 2) Co-designing Deep Learning Stacks with High-Performance MPI, 3) Out-of-core DNN training, 4) Accelerating TensorFlow over gRPC on HPC Systems, and 5) Efficient Deep Learning over Big Data Stacks like Spark and Hadoop.

    Speaker short Bio: 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 (, are currently being used by more than 3,000 organizations worldwide (in 88 countries). More than 540,000 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 ( are also publicly available. These libraries are currently being used by more than 310 organizations in 35 countries. More than 30,000 downloads of these libraries have taken place. High-performance and scalable versions of the Caffe and TensorFlow framework are available from Prof. Panda is an IEEE Fellow. More details about Prof. Panda are available at

  • 14:55 – 15:00 Question/Answers session

  • 15:00 – 15:30 Paper 1: Kushal Datta, Training Multiscale-CNN for Large Microscopy Image Classification in One Hour [ PDF ]

  • 15:30 – 16:00 Paper 2: Valentin Kozlov, Benchmarking Deep Learning Infrastructures by the Means of Tensorflow and Containers [ PDF ]

  • 16:00 – 16:30 Coffee break

  • 16:30 – 17:20 Invited Talk: Kai Krajsek (Juelich Supercomputing Centre, Forschungszentrum Jülich GmbH)

    Title: The Helmholtz Analytics Toolkit (HeAT) - A HPC Library for Scientific Big Data Analytics

    Abstract: This talk presents the Helmholtz Analytics Toolkit (HeAT), a HPC data analytics library for scientific applications. HeAT builds on top of PyTorch which provides many required features such as automatic differentiation, CPU and GPU support, linear algebra operations and basic MPI functionalities. However, distributed computations must be designed by hand for each basic communication and furthermore PyTorch implements only a subset of MPI functionalities. HeAT starts at this point providing a distributed tensor data object on which operations can be performed. The tensor data objects reside either on the CPU or on the GPU and, if desired, are distributed over various nodes. Operations on tensor objects are transparent to the user, i.e. they remain the same irrespective of whether the HeAT data object resides on a single node or if it is distributed over several nodes. On the basis of this core structure, HeAT implements typical data analytics methods motivated from various scientific use cases.

    After motivating the framework and specifying its scope, the talk describes its concept and its realization in detail. The presentation demonstrates the usage of HeAT by means of several typical examples from data analytics. The presentation closes with a discussion on the downsides, further developments and future challenges of HeAT.

  • 17:20 – 17:50 Paper 3: Stanimire Tomov, MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing [ PDF ]

  • 17:50 – 18:00 Closing remarks (Hartwig Anzt)

SDASC 2019 Workshop concludes