ANN ARBOR, Mich.—August 16, 2022 — Verge.io, with a mission to offer a simpler way to virtualize data centers, has added new features to its Verge-OS software designed to give users the performance of GPUs as virtualized, shared resources. The intent is to create a cost-effective, simple and flexible way to perform GPU-based machine learning, remote […]
Bing Uses NVIDIA GPUs, Tech to Enhance Object Detection and Image Search
June 28, 2018 by
Object detection and image search is seen as the next big thing when it comes to the search sector. And it’s easy to see why. A new white paper from NVIDIA explores how Bing deployed NVIDIA GPUs and technology to speed up object detection and visual search capabilities.
Minsky at Murex: GPUs for Risk Management
June 8, 2018 by
Pierre Spatz from Murex gave this talk at the NVIDIA GPU Technology Conference. “Murex has been an early adopters of GPU for pricing and risk management of complex financial options. GPU adoption has generated performance boost of its software while reducing its usage cost. Each new generation of GPU has also shown the importance of the necessary reshaping of the architecture of the software using its GPU accelerated analytics. Minsky featuring far better GPU memory bandwidth and GPU-CPU interconnect rase the bar even further. Murex will show how it has handled this new challenge for its business.”
University of North Texas turns to Bright Computing for Efficient Cluster Management
November 7, 2017 by
Today Bright Computing, a global leader in cluster and cloud infrastructure automation software, is proud to announce that the University of North Texas (UNT) has integrated Bright Cluster Manager into its HPC environment for research support. The University uses this 350-node cluster to optimize efficiency for researchers working on groundbreaking discoveries in a variety of domains material science, engineering, chemistry, and physics. Bright Cluster Manager helps UNT’s University Information Technology department successfully provision, monitor and manage heterogeneous clusters.
JAIST in Japan installs Cray XC40 Supercomputer
September 19, 2017 by
Today Cray announced the Japan Advanced Institute for Science and Technology (JAIST) has put a Cray XC40 supercomputer into production. The Cray XC40 supercomputers incorporate the Aries high performance network interconnect for low latency and scalable global bandwidth, as well as the latest Intel Xeon processors, Intel Xeon Phi processors, and NVIDIA Tesla GPU accelerators. “Our new Cray XC40 supercomputer will support our mission of becoming a premier center of excellence in education and research.”
xDCI Infrastructure Manages 3D Brain Microscopy Images at RENCI
August 17, 2017 by
Researchers at RENCI are using xDCI Data CyberInfrastructure to manage brain microscopy images that were overwhelming the storage capacity at individual workstations. “BRAIN-I is a computational infrastructure for handling these huge images combined with a discovery environment where scientists can run applications and do their analysis,” explained Mike Conway, a senior data science researcher at RENCI. “BRAIN-I deals with big data and computation in a user-friendly way so scientists can concentrate on their science.”
E4 to Showcase GPU-Accelerated OpenPOWER Servers at ISC 2016
June 17, 2016 by
Today Italy’s E4 Computer Engineering announced plans to showcase of new NVIDIA GPU-accelerated OpenPOWER servers at ISC 2016 in Frankfurt. “For this edition of ISC16, we wanted to reinforce the message that E4 is a company that actively engages and pursues new technologies’ paths with the aim to deliver leading-edge solutions for a number of demanding environments,” said Piero Altoè, Marketing and BDM Manager, E4 Computer Engineering. “Our priority is to collaborate with organizations such as OpenPOWER Foundation and true visionaries like NVIDIA in order to obtain powerful, scalable and affordable solutions for a number of complex applications and contribute to the development of technologies that have a huge impact on many aspects of our lives.”
The GPUltima for Graphics-Intensive VDI Environments
June 14, 2016 by
For Universities and Colleges that have a traditional infrastructure, adding new programs and applications is a huge endeavor. The IT staff needs to determine if all of the hardware meets the installation requirements and how to deploy these new programs on different models of desktops and notebooks. With a VDI environment that utilizes simple boot-up devices that connect to virtual desktops on the school’s server, the IT staff doesn’t have to worry about the age and capability of each individual PC when installing new software.
Bright Cluster Manager Comes to GPUltima from One Stop Systems
June 7, 2016 by
Today One Stop Systems announced that its the GPUltima product line now employs Bright Computing’s HPC Cluster Manager software. Bright Computing is a provider of comprehensive software solutions for provisioning and managing HPC clusters. Where conventional computer cluster systems use CPUs as the primary data processor, the GPUltima employs numbers of GPU cards, providing 10 times the performance by adding thousands more cores,” said Steve Cooper, CEO of One Stop Systems. “The GPUltima is completely ‘application-ready’, configured and tested to the customer’s specifications, so that the customer can begin processing immediately. The unique cluster management and monitoring software and the service and support packages that accompany the GPUltima make this a user-friendly system that allows the customer to begin his work without having to configure the cluster.”
Video: Using GPUs for Electromagnetic Simulations of Human Interface Technology
June 5, 2016 by
Chris Mason from Acceleware presented this talk at GTC 2016. “This session will focus on real life examples including an RF powered contact lens, a wireless capsule endoscopy, and a smart watch. The session will also outline the basics of the subgridding algorithm along with the GPU implementation and the development challenges. Performance results will illustrate the significant reduction in computation times when using a localized subgridded mesh running on an NVIDIA Tesla GPU.”
