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  1. Stretching GPU Database Performance With Flash Arrays

    For the past decade, flash has been used as a kind of storage accelerator, sprinkled into systems here and crammed into bigger chunks there, often with hierarchical tiering software to make it all work as a go-between that sits between slower storage (or sometimes no other tier of storage) and either CPU DRAM or GPU HBM or GDDR memory.

    With chunks of HBM2 memory stacked up right next to the “Pascal” and “Volta” Tesla GPU accelerators and plenty of DRAM memory in the Xeon processors, you might not think that an all-flash array based on NVM-Express interconnects would do much to accelerate the performance of GPU accelerated databases, but as it turns out, in many cases it does help, particularly when datasets bust out beyond that GPU memory and transactions have a lot of I/O contention.
  2. NVIDIA and Luxembourg To Build Joint AI Lab in Digital Luxembourg Push

    European city state Luxembourg has signed an agreement with NVIDIA that will see the two build a national artificial intelligence (AI) lab in what the Santa Clara-based hardware specialist described as its “first national AI collaboration”.

    Under the terms of the deal, NVIDIA will furnish state-of-the-art hardware and software supplied for the lab, which will be overseen by a joint advisory boardtasked with guiding research across a range of AI projects.

    The lab will investigate potential AI implementation in multiple national industries such as finance, healthcare and security.

    NVIDA will provide its expertise and tools, but the project involves a host of researchers from an array of Luxembourgian institutions, including the University of Luxembourg’s High Performance Computing team, the Luxembourg Centre for Systems Biomedicine, and its Interdisciplinary Centre for Security.
  3. Colovore Selected As NVIDIA DGX Colocation Data Center Partner

    “The GPU revolution is here, and artificial intelligence and machine learning servers require proper power densities and cooling capacities to operate efficiently,” stated Sean Holzknecht, President and Co-Founder of Colovore. “NVIDIA‘s DGX-1 and DGX-2 platforms are leading the way in solving complex AI challenges and we are proud to partner with NVIDIA and their customers to provide the most cost-effective, flexible, and scalable data center home for these servers. With close to 1,000 DGX platforms already deployed and operating at Colovore, we have tremendous experience providing the optimal footprint for DGX and HPC infrastructure success.”
  4. NVIDIA may have unwittingly leaked Unity ray-tracing support

    Unity has its own history with ray tracing, having experimented with it back in 2015. It also teamed with District 9 director Neil Blomkamp in 2017, creating an animated series called Adam. It used effects like area lights and volumetric fog, and most impressively, rendered everything in real time, showing the potential for more realism in gaming.

    Unreal recently announced ray tracing support for NVIDIA's RTX in its UE 4.22 engine, with studios able to implement basic RTX shaders and effects via DirectX 12 support. Unity has stayed mum on the issue, so while it's possible that Huang misspoke, it seems unlikely. "Probably one of the biggest stories that came out just last week is Unreal engine and Unity, both of the game engines, are going to incorporate RTX and ray tracing technology in the engine itself," he said in an earnings call. Either way, we should find out soon.
  5. NVIDIA steps up with Nsight Systems Performance Analysis Tool - insideHPC

    Nsight Systems is part of a larger family of Nsight tools. A developer can start with Nsight Systems to see the big picture and avoid picking less efficient optimizations based on assumptions and false-positive indicators. If the system-wide view of CPU-GPU interactions indicates large GPU workloads are a bottleneck, then Nsight Graphics and Nsight Compute can further assist in deeper analysis.

    "We noticed that our new Quadro P6000 server was ‘starved’ during training and we needed experts for supporting us,” said Felix Goldberg, Chief AI Scientist at Tracxpoint. “NVIDIA Nsight Systems helped us to achieve over 90 percent GPU utilization. A deep learning model that previously took 600 minutes to train, now takes only 90.”
  6. NVIDIA CEO Jensen Huang to Keynote World’s Premier AI Conference

    GLOBE NEWSWIRE -- NVIDIA founder and CEO Jensen Huang will deliver the opening keynote address at the 10th annual GPU Technology Conference, being held March 17-21, in San Jose, Calif.

    Huang will highlight the company’s latest innovations in AI, autonomous vehicles and robotics in his keynote on Monday, March 18, at 2 pm. As many as 10,000 developers, data scientists and industry executives are expected to register for this year’s conference, being held at the San Jose McEnery Convention Center.
  7. NVIDIA-Based Computer Vision Camera Built for Smart City Applications - Security Sales & Integration

    Entropix, a provider of AI-powered computational imaging software and Boulder AI, a manufacturer of intelligent camera solutions, have teamed up to produce and distribute what it claims is “the world’s most powerful computer vision data collection system.”

    The camera will utilize NVIDIA’s latest generation Tegra and Xavier processing architectures and will be built upon the DNNCam platform, fully integrated with the Entropix Resolution Engine and Enterprise Computer Vision Management System (ECVMS).
  8. NVIDIA's TITAN RTX Helps Researchers More Quickly Detect Osteoporosis

    A team of researchers at Dartmouth College are reporting promising results after swapping their Titan Xp GPU for the TITAN RTX. Running their existing code on the new GPU, the team achieved an 80% performance increase when training a pair of neural networks to detect osteoporotic vertebral fractures.

    To evaluate the new GPUs performance, the team compared a TITAN Xp with the new generation TITAN RTX GPU.

    “We trained our proposed deep neural network (CNN + LSTM) on a dataset of CT images for automatic detection of osteoporotic vertebral fractures as it was presented in our 2018 paper,” the team said.

    Trained for 300 epochs on dataset that included over 1400 CT scans, comprised of 10,546 two-dimensional 2D images, the training process was 80% faster with the new TITAN RTX GPU, the researchers said.
  9. France Merges Cascade Lake Xeons With Volta Tesla GPUs For AI Supercomputer

    In terms of hardware, Jean Zay is based on the HPE SGI 8600 platform and will be comprised of 1,528 CPU-only nodes, which are outfitted with two “Cascade Lake” Xeon SP 6248 processors (20 cores at 2.5 GHz), and an additional 261 GPU-accelerated nodes, each equipped with two of those same Cascade Lake processors plus four Nvidia Tesla “Volta” V100 GPU accelerators. Each of the nodes will sport 192 GB of main memory, with each of the GPU outfitted with 32 GB of its own local memory. The system will be hooked together using Intel’s Omni-Path fabric using a quad-rail interconnect that delivers 400 Gb/sec of aggregate bandwidth. It will be France’s second most powerful supercomputer in the country, trailing only the 23 petaflops Tera 1000 BullSequana system.
  10. GPU Applications Get The Container Treatment

    Containerized high performance computing is fast becoming one of the more popular ways of running HPC workloads. And perhaps nowhere is this more apparent than in the realm of GPU computing, where Nvidia and its partners have devoted considerable attention to making this method as seamless as possible.

    This is all the more appreciated in HPC set-ups, where GPUs add another layer of software complexity, which, in turn, has provided an extra bit of motivation for Nvidia to embrace the container model. The company’s efforts in this area has centered on the Nvidia GPU Cloud (NGC) which encompasses its own registry of GPU-based containers. The only significant drawback is that compatibility is limited to Nvidia GPUs of fairly recent vintage, specifically the Pascal-generation and Volta-generation processors.
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