{"id":4015,"date":"2025-05-29T20:40:31","date_gmt":"2025-05-29T20:40:31","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2025\/05\/29\/the-supercomputer-designed-to-accelerate-nobel-worthy-science\/"},"modified":"2025-05-29T20:40:31","modified_gmt":"2025-05-29T20:40:31","slug":"the-supercomputer-designed-to-accelerate-nobel-worthy-science","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2025\/05\/29\/the-supercomputer-designed-to-accelerate-nobel-worthy-science\/","title":{"rendered":"The Supercomputer Designed to Accelerate Nobel-Worthy Science"},"content":{"rendered":"
\n\t\t<\/span> <\/span> <\/span><\/span><\/p>\n

Ready for a front-row seat to the next scientific revolution?<\/p>\n

That\u2019s the idea behind Doudna \u2014 a groundbreaking supercomputer being built at Lawrence Berkeley National Laboratory. The system represents a major national investment in advancing U.S. high-performance computing leadership, ensuring U.S. researchers have access to cutting-edge tools to address global challenges.<\/p>\n

Also known as NERSC-10, Doudna is named for Nobel laureate and CRISPR pioneer Jennifer Doudna. The next-generation system announced today at Lawrence Berkeley National Laboratory is designed not just for speed, but for impact.<\/p>\n

\u201cThe Doudna system represents DOE\u2019s commitment to advancing American leadership in science, AI, and high-performance computing,\u201d said\u00a0U.S. Secretary of Energy Chris Wright said in a statement.<\/p>\n

Powered by Dell infrastructure with the NVIDIA Vera Rubin architecture, and set to launch in 2026, Doudna is tailored for real-time discovery across the U.S. Department of Energy\u2019s most urgent scientific missions. It\u2019s poised to catapult American researchers to the forefront of critical scientific breakthroughs, fostering innovation and securing the nation\u2019s competitive edge in key technological fields.<\/p>\n

\u201cDoudna is a time machine for science \u2014 compressing years of discovery into days,\u201d said Jensen Huang, founder and CEO of NVIDIA in a statement.\u00a0\u201cBuilt together with DOE and powered by NVIDIA\u2019s Vera Rubin platform, it will let scientists delve deeper and think bigger to seek the fundamental truths of the universe.\u201d<\/p>\n

Designed to Accelerate Breakthroughs\u00a0<\/b><\/p>\n

Unlike traditional systems that operate in silos, Doudna merges simulation, data and AI into a single seamless platform.<\/p>\n

\"\"
The Mayall 4-Meter Telescope, which will be home to the Dark Energy Spectroscopic Instrument (DESI), seen at night at Kitt Peak National Observatory. \u00a9 The Regents of the University of California, Lawrence Berkeley National Laboratory<\/figcaption><\/figure>\n

It\u2019s engineered to empower over 11,000 researchers with almost instantaneous responsiveness and integrated workflows, helping scientists explore bigger questions and reach answers faster than ever.<\/p>\n

\u201cWe\u2019re not just building a faster computer,\u201d said Nick Wright, advanced technologies group lead and Doudna chief architect at NERSC. \u201cWe\u2019re building a system that helps researchers think bigger, and discover sooner.\u201d<\/p>\n

Here\u2019s what Wright expects Doudna to advance:<\/p>\n

    \n
  • Fusion energy: <\/b>Breakthroughs in simulation that unlocks clean fusion energy.<\/li>\n
  • Materials science: <\/b>AI models that design new classes of superconducting materials.<\/li>\n
  • Drug discovery acceleration:<\/b> Ultrarapid workflow that helps biologists fold proteins fast enough to outpace a pandemic.<\/li>\n
  • Astronomy: <\/b>Real-time processing of data from the Dark Energy Spectroscopic Instrument at Kitt Peak to help scientists map the universe.<\/li>\n<\/ul>\n

    Doudna is expected to outperform its predecessor, Perlmutter, by more than 10x in scientific output, all while using just 2-3x the power.<\/p>\n

    This translates to a 3-5x increase in performance per watt, a result of innovations in chip design, dynamic load balancing and system-level efficiencies.<\/p>\n

    AI-Powered Discovery, at Scale<\/b>
    <\/b>
    Doudna will power AI-driven breakthroughs across high-impact scientific fields nationwide.<\/p>\n

    Highlights include:<\/p>\n

      \n
    • AI for protein design: <\/b>David Baker, a 2024 Nobel laureate, used NERSC systems to support his work using AI to predict novel protein structures, addressing challenges across scientific disciplines.<\/li>\n
    • AI for fundamental physics:<\/b> Researchers like Benjamin Nachman are using AI to \u201cunfold\u201d detector distortions in particle physics data and analyze proton data from electron-proton colliders.<\/li>\n
    • AI for materials science: <\/b>A collaboration including Berkeley Lab and Meta created \u201cOpen Molecules 2025,\u201d a massive dataset for using AI to accurately model complex molecular chemical reactions. Researchers involved also use NERSC for their AI models.<\/li>\n<\/ul>\n

      Real-Time Science, Real-World Impact\u00a0<\/b><\/p>\n

      \"\"
      The new system is named for Nobel laureate and CRISPR pioneer Jennifer Doudna. \u00a9 The Regents of the University of California, Lawrence Berkeley National Laboratory<\/figcaption><\/figure>\n

      Doudna isn\u2019t a standalone system. It\u2019s an integral part of scientific workflows. DOE\u2019s ESnet will stream data from telescopes, detectors and genome sequencers directly into the machine with low-latency, high-throughput NVIDIA Quantum-X800 InfiniBand networking.<\/p>\n

      This critical data flow is prioritized by intelligent QoS mechanisms, ensuring it stays fast and uninterrupted, from input to insight.<\/p>\n

      This will make the system incredibly responsive. At the DIII-D national fusion ignition facility, for example, data will stream control-room events directly into Doudna for rapid-response plasma modeling, so scientists can make adjustments in real time.<\/p>\n

      \u201cWe used to think of the supercomputer as a passive participant in the corner,\u201d Wright said. \u201cNow it\u2019s part of the entire workflow, connected to experiments, telescopes, detectors.\u201d<\/p>\n

      The Platform for What\u2019s Next: Unlocking Quantum and HPC Workflows<\/b><\/p>\n

      Doudna supports traditional HPC, cutting-edge AI, real-time streaming and even quantum workflows.<\/p>\n

      This includes support for scalable quantum algorithm development and the co-design of future integrated quantum-HPC systems, using platforms like NVIDIA CUDA-Q.<\/p>\n

      All of these workflows will run on the next-generation NVIDIA Vera Rubin platform, which will blend high-performance CPUs with coherent GPUs, meaning all processors can access and share data directly to support the most demanding scientific workloads.<\/p>\n

      Researchers are already porting full pipelines using frameworks like PyTorch, the NVIDIA Holoscan software development kit, NVIDIA TensorFlow, NVIDIA cuDNN and NVIDIA CUDA-Q, all optimized for the system\u2019s Rubin GPUs and NVIDIA NVLink architecture.<\/p>\n

      Over 20 research teams are already porting full workflows to Doudna through the NERSC Science Acceleration Program, tackling everything from climate models to particle physics. This isn\u2019t just about raw compute, it\u2019s about discovery, integrated from idea to insight.<\/p>\n

      Designed for Urgency\u00a0<\/b><\/p>\n

      In 2024, AI-assisted science earned two Nobel Prizes. From climate research to pandemic response, the next breakthroughs won\u2019t wait for better infrastructure.<\/p>\n

      With deployment slated for 2026, Doudna is positioned to lead a new era of accelerated science. DOE facilities across the country, from Fermilab to the Joint Genome Institute, will rely on its capabilities to turn today\u2019s questions into tomorrow\u2019s breakthroughs.<\/p>\n

      \u201cThis isn\u2019t a system for one field,\u201d Wright said. \u201cIt\u2019s for discovery \u2014 across chemistry, physics and fields we haven\u2019t imagined yet.\u201d<\/p>\n

      As NVIDIA founder and CEO Jensen Huang put it, Doudna is \u201ca time machine for science.\u201d It compresses years of discovery into days, and gives the world\u2019s toughest problems the power they\u2019ve been waiting for.<\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"

      https:\/\/blogs.nvidia.com\/blog\/dell-nvidia-berkeley-doudna\/<\/p>\n","protected":false},"author":0,"featured_media":4016,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4015"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=4015"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4015\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/4016"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=4015"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=4015"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=4015"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}