How cutting-edge computer chips are speeding up the AI revolution<\/p>\n <\/a><\/p>\n But speed and size are secondary to Frontier\u2019s main purpose \u2014 to push the bounds of human knowledge. Frontier excels at creating simulations that capture large-scale patterns with small-scale details, such as how tiny cloud droplets can affect the pace at which Earth\u2019s climate warms<\/a>. Researchers are using the supercomputer to create cutting-edge models of everything from subatomic particles to galaxies. Some projects are simulating proteins to help develop new drugs, modelling turbulence to improve aeroplane engine design and creating open-source large language models<\/a> (LLMs) to compete with the artificial intelligence (AI) tools from Google and OpenAI.<\/p>\n Researchers log on to Frontier from all over the world. In 2023, the supercomputer had 1,744 users in 18 countries. And, in 2024, Oak Ridge anticipates that Frontier users will publish at least 500 papers based on computations performed on the machine.<\/p>\n \u201cFrontier is not unlike the James Webb Space Telescope,\u201d says biophysicist Dilip Asthagiri of Oak Ridge National Laboratory. \u201cWe should see it as a scientific instrument.\u201d<\/p>\n Inside the machine<\/b><\/p>\n The brains of Frontier reside in a warehouse-sized room filled with a steady electronic hum that is gentle enough to talk over. In the room are 74 identical glossy black racks that hold a total of 9,408 nodes. These are the workhorses of a supercomputer. Each node consists of four graphics processing units (GPUs) and one computer processing unit (CPU).<\/p>\n A team of engineers continuously monitors the machine for signs of trouble, says Corey Edmonds, a technician at Hewlett Packard Enterprise, the company that built the supercomputer. Edmonds, who is based at Oak Ridge, is doing maintenance surgery on Frontier on this day. After fixing a broken connector on one of the nodes, he squeezes grey thermal grease from a syringe on to a silvery rectangle \u2014 one of the node\u2019s four GPUs. This helps the GPU to dissipate heat quickly and stay cool.<\/p>\n Frontier owes its speed mainly to its extensive use of GPUs. These chips, first developed to render realistic graphics for computer gamers, are now powering advances in AI through machine-learning applications<\/a>.<\/p>\n \u201cThey can run really fast,\u201d says Messer. \u201cThey\u2019re also abysmally stupid.\u201d GPUs excel at crunching many numbers at once \u2014 and not much else. \u201cThey can do one thing over and over and over and over again,\u201d he says, which makes them useful for speedy work in supercomputer calculations.<\/p>\n Researchers have to customize their code to take advantage of Frontier\u2019s GPUs. Messer likens a scientist using Frontier for the first time to a suburban driver commandeering a race car. \u201cIt\u2019s got a steering wheel, gas pedal and a brake,\u201d he says. \u201cBut try to get a regular driver into a Formula One car and actually have them go from here to there.\u201d<\/p>\n Big science<\/b><\/p>\n It\u2019s not easy for researchers to get a chance to use Frontier. Messer and three colleagues are gathering on this April Monday to evaluate research proposals for the machine. On average, they approve around one in four proposals, and last year awarded time to 131 projects. In particular, applicants need to make the case that their project can take advantage of the supercomputer\u2019s entire system.<\/p>\n The most common allocations they offer are around 500,000 node hours, equivalent to running the entire machine for three days continuously. Their largest allocation is four times bigger. Researchers who are granted time on Frontier get about ten times more computing resources than they can procure anywhere else, says Messer.<\/p>\n Today, his team is doling out smaller awards of around 20,000 node hours, which it does on a weekly basis. Many projects take advantage of Frontier\u2019s ability to simultaneously model a wide range of spatial and time scales. In total, Frontier has about 65 million node-hours available each year.<\/p>\n Technicians work on Frontier, which has more than 50,000 processors and is cooled by water.Credit: Nick McGinn for Nature<\/p>\n Scientists want to use Frontier, for example, to simulate atomically accurate biological processes, such as proteins or nucleic acids in solution interacting with other parts of cells.<\/p>\n This May, Asthagiri and Nick Hagerty, a high-performance-computing engineer at Oak Ridge, used Frontier to simulate a cube-shaped drop of liquid water containing more than 155 billion water molecules. \u201cIt was to push the machine,\u201d says Asthagiri. The simulated cube is about one-tenth the width of a human hair, and the model is among the largest atomic-level simulations ever made, says Asthagiri, who has not yet published the work in a peer-reviewed journal.<\/p>\n These initial simulations are building towards more ambitious goals to model entire cells from the atoms up. In the near term, researchers would like to simulate a cellular organelle and use these to inform laboratory experiments. They are also working to combine Frontier\u2019s high-resolution simulations of biological materials with ultra-fast imaging using X-ray free-electron lasers<\/a> to accelerate discoveries.<\/p>\n With Frontier, climate models have become more precise, too. In 2023, Oak Ridge climate scientist Matt Norman and other researchers used the supercomputer to run a global climate model with 3.25-kilometre resolution. Frontier\u2019s computing capability was necessary for them to create a decades-long forecast at this resolution1<\/a>. The model also incorporated the effects of the complex motion of clouds, which occurs on an even finer resolution. \u201cIt took all of Frontier to do it,\u201d says Norman.<\/p>\n Models would run significantly more slowly on other computers to achieve the same resolution while including the effects from clouds, he says. This limitation is a major hurdle for climate scientists seeking to forecast conditions, because cloud behaviour influences the movement of energy around the globe.<\/p>\n Supercomputing poised for a massive speed boost<\/p>\n <\/a><\/p>\n For a model to be practical for weather and climate forecasts, it needs to run at least one simulated year per day. Frontier could run through 1.26 simulated years per day for this model1<\/a>, a rate that will allow the researchers to create more-accurate 50-year forecasts than before.<\/p>\n Frontier also brings higher resolution to cosmological scales. Astrophysicist Evan Schneider at the University of Pittsburgh in Pennsylvania is using the supercomputer to study how Milky Way-sized galaxies evolve as they age. Frontier\u2019s galaxy models span four orders of magnitude, up to large-scale galactic structures about 100,000 light years (30,660 parsecs) in size. Before Frontier, the largest structures she could model at comparable resolution were dwarf galaxies, which are about one-fiftieth the mass.<\/p>\n Schneider simulates how supernovae cause gas to leak out of these galaxies2<\/a>. Over time, thousands to millions of supernova explosions collectively release a significant amount of gas that ultimately exits the galaxy3<\/a>. Because that gas is the raw material from which new stars are born, star formation slows down as the galaxies age. Frontier allows Schneider to include the effects of hotter gas than is practical with other computers. Her simulations suggest that current cosmological models downplay the role of this hot gas in the evolution of galaxies.<\/p>\n AI researchers are also clamouring for time on Frontier\u2019s GPUs, known for their role in training neural-network-based architectures such as the transformer model underpinning ChatGPT. With its nearly 38,000 GPUs, Frontier occupies a unique public-sector role in the field of AI research, which is otherwise dominated by industry.<\/p>\n Nur Ahmed, an economics researcher now at the University of Arkansas in Fayetteville, and his colleagues highlighted the gap between AI in academia and industry in a commentary last year4<\/a>. In 2021, 96% of the largest AI models came from industry. On average, industry models were nearly 30 times the size of academic models. The discrepancy is evident in monetary investment, too. Non-defence US agencies provided US$1.5 billion to support AI research in 2021. In that same year, industry spent more than $340 billion globally.<\/p>\n Mind the gap<\/b><\/p>\n The gap has only widened since the release of commercial large language models, says Ahmed. The computational resources to train OpenAI\u2019s GPT-4, for example, cost an estimated $78 million, whereas Google spent $191 million to train Gemini Ultra (see go.nature.com\/44ihnhx<\/a>). This gulf in investment leads to a stark asymmetry in the computing resources available to researchers in industry versus academia.<\/p>\n Industry is pushing the boundaries of basic AI research, and this could pose a problem for the field, write Ahmed and his co-authors. Industry dominance could lead to a lack of basic research that is not immediately profitable and result, for example, in the development of AI technologies that neglect the needs of lower-income communities, say the researchers. In an unpublished study, Ahmed has analysed 6 million peer-reviewed articles and 32 million patent citations and found that \u201con average, industry tends to ignore some of the concerns of marginalized populations in the global south\u201d.<\/p>\n![\"\"](\"https:\/\/www.europesays.com\/ie\/wp-content\/uploads\/2025\/08\/d41586-024-02832-5_27292892.jpg\"\/)
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