Article 4ST2F Designing Scalable HPC, Deep Learning, Big Data, and Cloud Middleware for Exascale Systems

Designing Scalable HPC, Deep Learning, Big Data, and Cloud Middleware for Exascale Systems

by
Rich Brueckner
from High-Performance Computing News Analysis | insideHPC on (#4ST2F)
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DK Panda from Ohio State University gave this talk at the UK HPC Conference. "This talk will focus on challenges in designing HPC, Deep Learning, Big Data and HPC Cloud 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 by taking into account support for multi-core systems (Xeon, ARM and OpenPower), high-performance networks, and GPGPUs (including GPUDirect RDMA)."

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