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HPC Top 5 Stories: April 26, 2018

Catch up on the latest weekly insights into the world of HPC and AI with this HPC Top 5 Stories.

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HPC Top 5 Stories: April 26, 2018

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  1. HPC TOP 5 STORIES Weekly Insights into the World of High Performance Computing

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

  3. PROVING THAT AI IS THE FUTURE OF SUPERCOMPUTING…

  4. TOP 5 HERE ARE THE “TOP FIVE’ STORIES HIGHLIGHTING WHAT’S HOT IN HPC AND AI

  5. TOP 5 1. Why UIUC Built HPC Application Containers for NVIDIA GPU Cloud 2. Fujitsu Boosts RIKEN AI Supercomputer to 54 PetaFLOPS 3. More Power, Less Tower: AI May Make Aircraft Control Towers Obsolete 4. The Buck Stops – And Starts- Here for GPU Compute 5. Boston University Researchers use AI to Detect Kidney Disease

  6. 1 WHY UIUC BUILT HPC APPLICATION CONTAINERS FOR NVIDIA GPU CLOUD “Containers are a way of packaging up an application and all of its dependencies in such a way that you can install them collectively on a cloud instance or a workstation or a compute node. And it doesn’t require the typical amount of system administration skills and involvement to put one of these containers on a machine. And within the container image in a manner that’s roughly similar to what you have in a virtual machine, the user can change anything they want. So you have an entire sort of operating system snapshot is what it looks like on the inside. So you can customize the layout of the file system and do all kinds of other things that would otherwise involve getting a lot of permission and cooperation, particularly in large computing installations.” VIDEO ARTICLE

  7. 2 FUJITSU BOOSTS RIKEN AI SUPERCOMPUTER TO 54 PETAFLOPS Fujitsu has performed a massive upgrade to RIKEN’s RAIDEN supercomputer using NVIDIA DGX-1 servers outfitted with the latest V100 Tesla GPUs. RAIDEN was originally deployed in 2017 using 24 of NVIDIA’s first-generation DGX-1 servers, each of which were powered by eight P100 GPUs. Together, set of servers delivered four half-precision petaflops for deep learning applications. With the upgrade, those original servers were replaced with 54 DGX-1 boxes, using the newest V100 GPUs. Since the V100 has the special Tensor Core circuitry specifically designed for neural network processing (125 teraflops of mixed precision floating point operations per device), the upgraded system will offer a whopping 54 petaflops of deep learning performance. ARTICLE

  8. 3 MORE POWER, LESS TOWER: AI MAY MAKE AIRCRAFT CONTROL TOWERS OBSOLETE Airport control towers are an emblem of the aviation industry. A Canadian company wants to use its technology to make them a relic of the past. Airport buffs may mourn the change. But Ontario-based Searidge Technologies believes its reasoning is, um, well-grounded. It believes AI-powered video systems can better watch runways, taxiways and gate areas. By “seeing” airport operations through as many as 200 cameras, there’s no need for the sightline towers give air traffic controllers. BLOG

  9. 4 THE BUCK STOPS – AND STARTS – HERE FOR GPU COMPUTE We have done surveys of the HPC codes, and about 70 percent of the processing cycles of the HPC centers is dominated by 15 different applications. This small number of applications dominate compute time. We have been focused on accelerating those applications first as well as making them run well. There are hundreds of accelerated applications, but some of them are just moving to GPUs and they are going to take a while. You do have to know your workloads, and at every HPC center, all that we ask is that they look at their applications and first of all make sure they are on the most recent versions of the code, many of which have been accelerated by GPUs. On average, if you compare a server with four Volta GPUs against a best-in-class server with two “Skylake” Xeon SP processors, the average speed up across a basket of HPC applications is 20X. - Ian Buck ARTICLE

  10. 5 BOSTON UNIVERSITY RESEARCHERS USE AI TO DETECT KIDNEY DISEASE Researchers at Boston University developed a deep learning algorithm that can assess kidney disease with better accuracy than trained pathologists. Detecting kidney damage is of great importance, and unlike many other diseases, symptoms often don’t appear until the disease is very advanced. Getting this diagnosis wrong can lead to a series of life- threatening conditions. “This rapid, scalable method can be deployable in the form of software at the point of care, and holds the potential for substantial clinical impact, including augmenting clinical decision making for nephrologists,” the team wrote in their research paper. BLOG

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