HPC TOP 5 STORIESWeekly Insights into the World of High Performance Computing

HPC AND AI HAVE PAVED THE WAY FOR GROUNDBREAKING DISCOVERIES IN SCIENCE, MEDICINE, AND OTHER FIELDS…

PROVING THAT AI IS THE FUTURE OF SUPERCOMPUTING…

HERE ARE THE “TOP FIVE’ STORIES HIGHLIGHTING WHAT’S HOT IN HPC AND AI

TOP 5

TOP 5

1. Scaling Deep Learning on an 18,000 GPU Supercomputer

2. UK to Launch Six Major HPC Centers

3. Deep Learning Driving Up Data Center Power Density

4. 3D Map of Earth’s Interior

5. Video: Computational Fluid Dynamics for Surgical Planning

SCALING DEEP LEARNING ON AN 18,000 GPU SUPERCOMPUTER

It is one thing to scale a neural network on a single GPU or even a single system with four or eight GPUs. But it is another thing entirely to push it across thousands of nodes. Most centers doing deep learning have relatively small GPU clusters for training and certainly nothing on the order of the Titan supercomputer at Oak Ridge National Laboratory.

The emphasis on machine learning scalability has often been focused on node counts in the past for single-model runs. This is useful for some applications, but as neural networks become more integrated into existing workflows, including those in HPC, there is another way to consider scalability. 

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UK TO LAUNCH SIX MAJOR HPC CENTERS2

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Six high performance computing centers will be formally launched in the U.K. later this week intended to provide wider access to HPC resources to U.K. industry and academics. This expansion of HPC resources and access to them is being funded with £20 million from the Engineering and Physical Sciences Research Council. The EPSRC plays a somewhat similar role in the U.K. to the National Science Foundation role in the U.S.

The centers are located at the universities of Cambridge, Edinburgh, Exeter, and Oxford, Loughborough University, and University College London. According to today’s pre-launch announcement, some of the centers will be available free of charge to any EPSRC-supported researcher, and some will give access to UK industry. Some of the infrastructure is in place and has been in use for a while.

DEEP LEARNING DRIVING UP DATA CENTER POWER DENSITY

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Few people on the planet know more about building computers for Artificial Intelligence than Rob Ober. As the top technology exec at NVIDIA’s Accelerated Computing Group, he’s the chief platform architect behind Tesla, the most powerful GPU on the market for Machine Learning, which is the most widespread type of AI today.

GPUs, or Graphics Processing Units, take their name from their original purpose, but their applications today stretch far beyond that. Supercomputer designers have found them ideal for offloading huge chunks of workloads from CPUs in the systems they build; they’ve also proven to be super-efficient processors for a Machine Learning approach called Deep Learning. EXPLORE

3D MAP OF EARTH’S INTERIOR4

A team of researchers led by Jeroen Tromp at Princeton University used a GPU-accelerated supercomputer to create a detailed 3D picture of Earth’s interior.

“This is the first global seismic model where no approximations — other than the chosen numerical method — were used to simulate how seismic waves travel through the Earth and how they sense heterogeneities,” said Ebru Bozdag, a coprincipal investigator of the project and an assistant professor of geophysics at the University of Nice Sophia Antipolis. “That’s a milestone for the seismology community. For the first time, we showed people the value and feasibility of running these kinds of tools for global seismic imaging.”

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COMPUTATIONAL FLUID DYNAMICS FOR SURGICAL PLANNING

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In this video, Todd Raeker, Research Technology Consultant at the University of Michigan shares how a group of 50 researchers at University of Michigan are using GPUs and OpenACC to accelerate the codes for their data-driven physics simulations.

“The current versions of the codes use MPI and depend on finer and finer meshes for higher accuracy which are computationally demanding. To overcome the demands, the team has gained access to their state-of-the-art cluster equipped with POWER CPUs and Tesla P100 GPUs — and turning to OpenACC and machine learning to accelerate their science. This has allowed them to spend the least resources on programming, and effectively utilize available compute resources.”READ MORE

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