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Video: How OpenACC Enables Scientists to port their codes to GPUs and Beyond

In this video SC18, Jack Wells from ORNL describes how OpenACC enables scientists to port their codes to GPUs and other HPC platforms. “OpenACC, a directive-based high-level parallel programming model, has gained rapid momentum among scientific application users – the key drivers of the specification. The user-friendly programming model has facilitated acceleration of over 130 applications including CAM, ANSYS Fluent, Gaussian, VASP, Synopsys on multiple platforms and is also seen as an entry-level programming model for the top supercomputers (Top500 list) such as Summit, Sunway Taihulight, and Piz Daint. As in previous years, this BoF invites scientists, programmers, and researchers to discuss their experiences in adopting OpenACC for scientific applications, learn about the roadmaps from implementers and the latest developments in the specification.”

NVIDIA Unveils TITAN RTX GPU for Accelerated Ai

Today NVIDIA introduced the TITAN RTX as what the company calls “the world’s most powerful desktop GPU” for AI research, data science and creative applications. “Driven by the new NVIDIA Turing architecture, TITAN RTX — dubbed T-Rex — delivers 130 teraflops of deep learning performance and 11 GigaRays of ray-tracing performance. Turing is NVIDIA’s biggest advance in a decade – fusing shaders, ray tracing, and deep learning to reinvent the GPU,” said Jensen Huang, founder and CEO of NVIDIA. “The introduction of T-Rex puts Turing within reach of millions of the most demanding PC users — developers, scientists and content creators.”

One Stop Systems Steps up GPU Servers for Ai and World’s First PCIe Gen 4 Cable Adapter

In this video from SC18, Jaan Mannik from One Stop Systems describes how the company’s high performance GPU system power HPC and Ai applications. At the show, the company also introduced HIB616-x16, the world’s first PCIe Gen 4 cable adapter. “The OSS booth will also feature a partner pavilion where several OSS partners will be represented, including NVIDIA, SkyScale, Western Digital, Liqid, One Convergence, Intel and Lenovo. OSS and its partners will showcase new products, services and solutions for high-performance computing, including GPU and flash storage expansion, composable infrastructure solutions, the latest EOS server, cloud computing, and the company’s recently introduced Thunderbolt eGPU product.”

Singularity Containers Power NVIDIA Full Galaxy Simulation

In this video from the NVIDIA Showcase event at SC18, Jensen Huang hosts a demo of Singularity containers running a full galaxy simulation. “This has to be the best Container demo ever,” said Huang. “As part of that effort, Huang announced new multi-node HPC and visualization containers for the NGC container registry, which allow supercomputing users to run GPU-accelerated applications on large-scale clusters. NVIDIA also announced a new NGC-Ready program, including workstations and servers from top vendors.”

Overclocked NVIDIA DGX-2H Cluster Lands at #61 on the TOP500

In this video from SC18 in Dallas, Marc Hamilton from NVIDIA describe the all new overclocked DGX-2H supercomputer. Built by NVIDIA, a cluster of 36 DGX-2H devices with 3 Petaflops of LINPACK performance was just ranked #62 on the TOP500 list of the world’s fastest supercomputers.

NVIDIA’s New Turing T4 GPU is going gangbusters in the Cloud Space

Two months after its introduction, the NVIDIA T4 GPU is featured in 57 separate server designs from the world’s leading computer makers. It is also available in the cloud, with the first availability of the T4 for Google Cloud Platform customers. “Just 60 days after the T4’s launch, it’s now available in the cloud and is supported by a worldwide network of server makers. The T4 gives today’s public and private clouds the performance and efficiency needed for compute-intensive workloads at scale.”

NVIDIA Powers New Performance Records on TOP500 List

Today NVIDIA showcased its HPC leadership in the TOP500 list of the world’s fastest supercomputers. The closely watched list shows a 48 percent jump in one year in the number of systems using NVIDIA GPU accelerators. The total climbed to 127 from 86 a year ago, and is three times greater than five years ago. “With the end of Moore’s Law, a new HPC market has emerged, fueled by new AI and machine learning workloads. These rely as never before on our high performance, highly efficient GPU platform to provide the power required to address the most challenging problems in science and society.”

vScaler Cloud Adopts RAPIDS Open Source Software for Accelerated Data Science

vScaler has incorporated NVIDIA’s new RAPIDS open source software into its cloud platform for on-premise, hybrid, and multi-cloud environments. Deployable via its own Docker container in the vScaler Cloud management portal, the RAPIDS suite of software libraries gives users the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. “The new RAPIDS library offers Python interfaces which will leverage the NVIDIA CUDA platform for acceleration across one or multiple GPUs. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.”

Video: Unified Memory on Summit (Power9 + V100)

Jeff Larkin from NVIDIA gave this talk at the Summit Application Readiness Workshop. The event had the primary objective of providing the detailed technical information and hands-on help required for select application teams to meet the scalability and performance metrics required for Early Science proposals. Technical representatives from the IBM/NVIDIA Center of Excellence will be delivering a few plenary presentations, but most of the time will be set aside for the extended application teams to carry out hands-on technical work on Summit.”

Podcast: Bill Daly on How NVIDIA is Accelerating Ai

In this AI Podcast, Bill Dally from NVIDIA describes how the company is accelerating Ai with GPUs. “NVIDIA researchers are gearing up to present 19 accepted papers and posters, seven of them during speaking sessions, at the annual Computer Vision and Pattern Recognition conference next week in Salt Lake City, Utah. Joining us to discuss some of what’s being presented at CVPR, and to share his perspective on the world of deep learning and AI in general is one of the pillars of the computer science world, Bill Dally, chief scientist at NVIDIA.”