A new whitepaper from NEC X delves into the world of unstructured data and explores how vector processors and their optimization software can help solve the challenges of wrangling the ever-growing volumes of data generated globally. “In short, vector processing with SX-Aurora TSUBASA will play a key role in changing the way big data is handled while stripping away the barriers to achieving even higher performance in the future.”
How to Design Scalable HPC, Deep Learning and Cloud Middleware for Exascale Systems
March 4, 2019 by
DK Panda from Ohio State University gave this talk at the Stanford HPC Conference. “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 about the challenges in designing runtime environments for MPI+X (PGAS – OpenSHMEM/UPC/CAF/UPC++, OpenMP, and CUDA) programming models taking into account support for multi-core systems (Xeon, OpenPower, and ARM), high-performance networks, GPGPUs (including GPUDirect RDMA), and energy-awareness.”
Exploring the Possibilities of Deep Learning Software
April 30, 2018 by
This is the second post in a five-part series from a report that explores the potential of unified deep learning with CPU, GPU and FGPA technologies. This post explores the possibilities and functions of software for deep learning.
Designing HPC & Deep Learning Middleware for Exascale Systems
February 13, 2017 by
DK Panda from Ohio State University presented this deck at the 2017 HPC Advisory Council Stanford Conference. “This talk will focus on challenges in designing runtime environments for exascale systems with millions of processors and accelerators to support various programming models. We will focus on MPI, PGAS (OpenSHMEM, CAF, UPC and UPC++) and Hybrid MPI+PGAS programming models by taking into account support for multi-core, high-performance networks, accelerators (GPGPUs and Intel MIC), virtualization technologies (KVM, Docker, and Singularity), and energy-awareness. Features and sample performance numbers from the MVAPICH2 libraries will be presented.”
Not Created Equal: Techila Publishes Cloud HPC Benchmark Report
May 28, 2014 by
Techila Technologies has published an interesting Cloud HPC Benchmark Report. In comparing Amazon, Google, and Microsoft Azure, they come to the conclusion that a successful move to Cloud HPC is all about effective middleware.
