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Designing HPC & Deep Learning Middleware for Exascale Systems

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.”

Global HPC Centers Form Accelerated Computing Institute

Leaders in hybrid accelerated HPC in the United States, Japan, and Switzerland have signed a memorandum of understanding establishing an international institute dedicated to common goals, the sharing of HPC expertise, and forward-thinking evaluation of computing architecture. “Forecasting the future of leadership-class computing and managing the risk of architectural change is a shared interest among ORNL, Tokyo Tech, and ETH Zurich,” said Jeff Nichols, associate laboratory director of computing and computational sciences at ORNL. “What unites our three organizations is a willingness to embrace change, actively partner with HPC vendors, and devise solutions that advance the work of our scientific users. ADAC provides a framework for member organizations to pursue mutual interests such as accelerated node architectures as computing moves toward the exascale era and beyond.”

NVIDIA Rolls Out New Quadro Pascal GPUs

“Professional workflows are now infused with artificial intelligence, virtual reality and photorealism, creating new challenges for our most demanding users,” said Bob Pette, vice president of Professional Visualization at NVIDIA. “Our new Quadro lineup provides the graphics and compute performance required to address these challenges. And, by unifying compute and design, the Quadro GP100 transforms the average desktop workstation with the power of a supercomputer.”

Radio Free HPC Looks at How Bosch Helped Volkswagen Cheat Emissions Tests

In this podcast, the Radio Free HPC team looks at recent developments in the Volkswagen Dieselgate scandal. According to reports, auto parts supplier Bosch wrote the software for VW that enabled the company’s diesel vehicles to cheat on emissions tests. “We know because Bosch asked VW for indemnity back in 2006.”

Deep Learning in HPC – Using Data to Go Far Beyond Automation in Pathology

Dr. Amit Seti from IIT-Gauwhati presented this talk at GTCx in India. “This talk will cover how medical imaging data can be used to train computer vision systems that automate diagnostic analysis in current clinical practice. Not only that, with more creative use of data, we can go even beyond that to predict outcome of specific treatment for individual patients. We will cover results from prostate and breast cancers to show that a future is not too far where algorithms will become a necessary set of tools in a pathologist’s toolbox.”

IBM Adds TensorFlow Support for PowerAI Deep Learning

Today IBM announced that its PowerAI distribution for popular open source Machine Learning and Deep Learning frameworks on the POWER8 architecture now supports the TensorFlow 0.12 framework that was originally created by Google. TensorFlow support through IBM PowerAI provides enterprises with another option for fast, flexible, and production-ready tools and support for developing advanced machine learning products and systems.

CUDA Made Easy: An Introduction

“CUDA C++ is just one of the ways you can create massively parallel applications with CUDA. It lets you use the powerful C++ programming language to develop high performance algorithms accelerated by thousands of parallel threads running on GPUs. Many developers have accelerated their computation- and bandwidth-hungry applications this way, including the libraries and frameworks that underpin the ongoing revolution in artificial intelligence known as Deep Learning.”

Video: AI – The Next HPC Workload

“From new cloud offerings on AWS and Azure, to Summit and Sierra, the 150+ PF supercomputers being built by the US in 2017, new AI workloads are driving the rapid growth of GPU accelerated HPC systems. For years, HPC simulations have generated ever increasing amounts of big data, a trend further accelerated by GPU computing. With GPU Deep Learning and other AI approaches, a larger amount of big data than ever can now be used to advance scientific discovery.”

Podcast: How Deep Learning Will Reshape Our Cities

In this AI Podcast, Lynn Richards, president and CEO of the Congress for New Urbanism and Charles Marohn, president and co-founder of Strong Towns, describe how AI will reshape our cities. “AI will do much more than automate driving. It promises to help create more liveable cities. And help put expensive infrastructure where we need it most.”

Upgraded Bridges Supercomputer Now in Production

“Bridges’ new nodes add large-memory and GPU resources that enable researchers who have never used high-performance computing to easily scale their applications to tackle much larger analyses,” says Nick Nystrom, principal investigator in the Bridges project and Senior Director of Research at PSC. “Our goal with Bridges is to transform researchers’ thinking from ‘What can I do within my local computing environment?’ to ‘What problems do I really want to solve?’”