Designing HPC, Deep Learning, and Cloud Middleware for Exascale Systems - insideHPC

In this video from the Stanford HPC Conference, DK Panda from Ohio State University presents: Designing HPC, Deep Learning, and Cloud Middleware for Exascale Systems. "This talk will focus on challenges in designing HPC, Deep Learning, and HPC Cloud middleware for Exascale systems with millions of processors and accelerators. For the HPC domain, we will discuss the challenges in designing runtime environments for MPI X (PGAS-OpenSHMEM/UPC/CAF/UPC, OpenMP and Cuda) programming models by taking into account support for multi-core systems (KNL and OpenPower), high networks, GPGPUs (including GPUDirect RDMA) and energy awareness. Features and sample performance numbers from MVAPICH2 libraries will be presented. For the Deep Learning domain, we will focus on popular Deep Learning framewords (Caffe, CNTK, and TensorFlow) to extract performance and scalability with MVAPICH2-GDR MPI library and RDMA-enabled Big Data stacks. Finally, we will outline the challenges in moving these middleware to the Cloud environments."