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Why UIUC Built HPC Application Containers for NVIDIA GPU Cloud

In this video from the GPU Technology Conference, John Stone from the University of Illinois describes how container technology in the NVIDIA GPU Cloud help the University distribute accelerated applications for science and engineering. “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.”

Iceland’s Verne Global Steps up to run HPC & AI Workloads in the Cloud

In this video from the GPU Technology Conference, Bob Fletcher from Verne Global discusses why more and more HPC & AI workloads are moving to the company’s datacenters in Iceland. “Today’s computational environments are changing rapidly as more companies are looking to utilize HPC and intensive applications across an increasingly wide variety of industries. At Verne Global we have fully optimized our campus to meet the specific requirements of the international HPC community.”

How ZIFF Powers AI with Dell EMC Technologies

In this video from the GPU Technology Conference, David Gonzolez from Ziff describes how Dell EMC powers AI solutions at his company. “ZIFF is unique in its approach to Ai. By focusing on empowering product visionaries and software engineers, ZIFF can help organizations fully unlock the insights and automation trapped within their unstructured data.” To accelerate it’s unstructured database technology, Ziff uses the Dell PowerEdge C4140, which allows them meet the demands of cognitive computing workloads with a dense, accelerator-optimized 1U server supporting 4 GPUs and superior thermal efficiency.

Thierry Pellegrino on the Move to AI with HPC at Dell EMC

In this video from the GPU Technology Conference, Thierry Pellegrino describes how Dell EMC customers are applying HPC technologies to AI workloads. “I’ll just mention one of our customers, AeroFarms, who use a lot of our technology in order to bring the value of IoT into an environment where you can do machine learning, deep learning, artificial intelligence, and automatically grow crops in an environment that you would never think it would be possible.”

Video: Liqid Teams with Inspur at GTC for Composable Infrastructure

In this video from GTC 2018, Dolly Wu from Inspur and Marius Tudor from Liquid describe how the two companies are collaborating on Composable Infrastructure for AI and Deep Learning workloads. “AI and deep learning applications will determine the direction of next-generation infrastructure design, and we believe dynamically composing GPUs will be central to these emerging platforms,” said Dolly Wu, GM and VP Inspur Systems.

Inside the new NVIDIA DGX-2 Supercomputer with NVSwitch

In this video from the GPU Technology Conference, Marc Hamilton from NVIDIA describes the new DGX-2 supercomputer with the NVSwitch interconnect. “NVIDIA NVSwitch is the first on-node switch architecture to support 16 fully-connected GPUs in a single server node and drive simultaneous communication between all eight GPU pairs at an incredible 300 GB/s each. These 16 GPUs can be used as a single large-scale accelerator with 0.5 Terabytes of unified memory space and 2 petaFLOPS of deep learning compute power. With NVSwitch, we have 2.4 terabytes a second bisection bandwidth, 24 times what you would have with two DGX-1s.”

RAID No More: GPUs Power NSULATE for Extreme HPC Data Protection

In this video from GTC 2018, Alexander St . John from Nyriad demonstrates how the company’s NSULATE software running on Advanced HPC gear provides extreme data protection for HPC data. As we watch, he removes a dozen SSDs from a live filesystem — and it keeps on running!

Why the World’s Largest Telescope Relies on GPUs

Over at the NVIDIA blog, Jamie Beckett writes that the new European-Extremely Large Telescope, or E-ELT, will capture images 15 times sharper than the dazzling shots the Hubble telescope has beamed to Earth for the past three decades. “are running GPU-powered simulations to predict how different configurations of E-ELT will affect image quality. Changes to the angle of the telescope’s mirrors, different numbers of cameras and other factors could improve image quality.”

Video: NVIDIA Unveils DGX-2 Supercomputer

In this video, NVIDIA CEO Jensen Huang unveils the DGX-2 supercomputer. Combined with a fully optimized, updated suite of NVIDIA deep learning software, DGX-2 is purpose-built for data scientists pushing the outer limits of deep learning research and computing. “Watch to learn how we’ve created the first 2 petaFLOPS deep learning system, using NVIDIA NVSwitch to combine the power of 16 V100 GPUs for 10X the deep learning performance.”

DDN feeds NVIDIA DGX Servers 33GB/s for Machine Learning

Today DDN announced that its EXAScaler DGX solution accelerated client has been fully integrated with the NVIDIA DGX Architecture. “By supplying this groundbreaking level of performance, DDN enables customers to greatly accelerate their Machine Learning initiatives, reducing load wait times of large datasets to mere seconds for faster training turnaround.”