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Politico
14-05-2025
- Business
- Politico
America tries to skirt a supercomputer gap
Presented by Amid all the hype about artificial intelligence, quantum computers and advanced chipmaking — to say nothing of the mega-billion-dollar investments— is it possible that the United States still isn't doing enough to maximize computers' potential? As Congress scrambles to put together a budget deal, some tech experts are worried about the ability of modern hardware to keep up with the demands of powerful AI tools — and arguing that government has a bigger role to play in keeping American computing globally competitive. 'Other countries are moving quickly, and without a national strategy, the U.S. risks falling behind,' wrote veteran computer scientist Jack Dongarra of the University of Tennessee in an essay published today by The Conversation. Citing the success of efforts like Europe's EuroHPC program and Japan's Fugaku supercomputer, Dongarra argues that 'a U.S. national strategy should include funding new machines and training for people to use them,' as well as 'partnerships with universities, national labs and private companies.' This might seem almost deliberately contrarian in an age of radical research cuts, but President Donald Trump's proposed budget actually maintains current spending levels for support of artificial intelligence, quantum computing and high-performance computing. Historically, at that, the U.S. has shown a willingness to make significant investments in what's broadly known as 'high-performance computing,' or supercomputers that often use millions of processors in concert to execute operations at lightning speed. The Exascale Computing Project, which spanned the Obama, Trump 1.0 and Biden eras, came to a conclusion last year with nearly $2 billion spent on a massive supercomputing effort that led to the El Capitan exascale computer at the Lawrence Livermore National Laboratory coming online in February. The supercomputer race isn't purely about technology, and America's ecosystem gives it a built-in advantage. 'China may have faster machines, but America's supercomputers have proven vastly superior,' said Stephen Ezell, vice president of global innovation policy at the Information Technology and Innovation Foundation — because they have more efficient architecture. For this, he credits the 'symbiosis' among American computing skills, hardware and software development. 'It's critical the United States both invest in skills and also in programs to help small businesses leverage these technologies,' he said. Now what? Despite the (comparative) budget support by the White House, the complex, interdependent research system that powers computer science innovations could still be threatened by the Trump administration's efforts to roll back recent policy. In his essay, Dongarra cites the National Science Foundation's Directorate of Technology, Innovation and Partnerships office as an example of pro-compute policy created by the 2022 CHIPS and Science Act, but the NSF is currently facing radical budget cuts. In his joint address to Congress this year Trump asked Speaker of the House Mike Johnson to 'get rid' of CHIPS and Science altogether. ITIF's Ezell called for the Trump administration to continue CHIPS and Science funding for high-performance computing — and called out the administration for proposed NSF budget cuts that threaten the construction of a supercomputer at the University of Texas. One major focus of White House policy under President Joe Biden was subsidizing research on and production of 'chiplets,' or small chips that can be packaged and rearranged in a modular fashion to make large-scale computing more efficient. While the European Union is investing hundreds of millions of euros in chiplet projects as part of EuroHPC, the future of similar efforts by the U.S. government remains unclear after the Trump administration brought CHIPS and Science negotiations under the auspices of the United States Investment Accelerator at the Department of Commerce in April. Quantum, another field poised to make big contributions to supercomputing, seems largely off the chopping block when it comes to government spending. Speaking at a Holland and Knight event in April, Rep. Jay Obernolte (R-Calif.) said that Congress is 'unified in our belief' that it's necessary to reauthorize the National Quantum Initiative Act, a bill signed into law during the first Trump administration that authorized more than $1 billion in spending on quantum initiatives. House Committee on Space, Science and Technology Chair Brian Babin (R-Tex.) also said he looked forward to renewing the bill. ITIF's Ezell pointed to a list of 10 policy proposals his organization made on quantum spending and support, and argued that 'America needs to graduate more computer scientists and electrical engineering students and bolster America's STEM pipeline' to support large computing projects. Given the fundamental importance of simply having the most powerful computers to fields like defense, energy, and innovation — especially amid global competition with China, one of Trump's top priorities — continuing to back high-performance computing efforts seems like a political no-brainer. But with unpredictability the only predictable thing about the second Trump administration thus far, the research and tech communities have their guard up for any threat to America's longstanding support for the sector. ai moratorium pushback An open letter from state lawmakers and AI researcher Gary Marcus argues that the proposal in the House Energy and Commerce Committee's budget reconciliation bill to block any state and local AI laws for 10 years is a 'major step backwards.' POLITICO's Alfred Ng reported for Pro subscribers on the letter, which says the moratorium would conflict with the Tenth Amendment separating powers between federal and state governments. 'The federal government should not get to control literally every aspect of how states regulate AI — particularly when they themselves have fallen down on the job — and the Constitution makes pretty clear that the bill as written is far, far too broad,' the letter said. The committee narrowly approved the moratorium this morning despite Democratic opposition. potential treasury conflicts The DOGE officials installed at the Treasury Department reported owning stock in a plethora of banks and companies doing business with the government. POLITICO's Michael Stratford reported in Morning Money today that Tom Krause, the lead official for Treasury's DOGE team, reported hundreds of thousands of dollars' worth of shares in financial companies like JPMorgan Chase, Bank of America and PNC – including some that provide services for his unit. It's not clear whether he or other DOGE members have been required to divest from financial stocks, and a Treasury spokesperson said in a statement that 'These Treasury and IRS employees are following all ethics laws and guidelines, including policies concerning recusals.' That has not convinced ethics watchdogs. Dylan Hedtler-Gaudette, the director of government at the Project on Government Oversight, called it a 'massive, glaring red flag of a conflict of interest.' He said, 'A person at this level of [the] Treasury Department should absolutely not have direct financial ties to the industries and the companies that he or she is in part responsible for overseeing.' sell, sell, sell Some of the Trump memecoin's biggest investors are already cashing out. POLITICO's Irie Sentner reported Tuesday that of the 220 top investors in the $TRUMP memecoin in line to be invited to a May 22 dinner at the president's golf club in Virginia, at least 34 sold most of their stakes just hours after the cutoff to be considered. 'There's really no reason to own it after May 12, because you're already getting the value of it if you were buying it specifically for the [dinner],' said Jeff Dorman, chief investment officer at crypto firm Arca. It's unclear who the top investors in the coin actually are, but Bloomberg reported last week that a majority of them are likely foreign, stoking concerns that the coin might open up foreign donations to Trump that would otherwise be illegal or improper. In a statement, White House press secretary Karoline Leavitt said, 'President Trump is compliant with all conflict-of-interest rules, and only acts in the best interests of the American public.' post of the day THE FUTURE IN 5 LINKS Stay in touch with the whole team: Mohar Chatterjee (mchatterjee@ Steve Heuser (sheuser@ Nate Robson (nrobson@ and Daniella Cheslow (dcheslow@
Business Wire
22-04-2025
- Health
- Business Wire
Argonne Leverages AI and Supercomputing to Revolutionize Cancer Research
LEMONT, Ill.--(BUSINESS WIRE)--Discovering new drugs to treat cancer and predicting how tumors will respond to them remain key challenges in the fight against the disease. At the U.S. Department of Energy's (DOE) Argonne National Laboratory, researchers are using the power of artificial intelligence (AI) and high performance computing (HPC) to pioneer new methods that speed up drug discovery and enhance drug response prediction. Over the past decade, their efforts have evolved from creating AI tools for cancer research to evaluating the growing number of AI models, ultimately leading to their latest work aimed at drug-resistant cancer targets. Using the new Aurora exascale system, researchers led an early science project that focused on AI-driven drug discovery for cancer. Their work demonstrated how the system's immense processing power can help accelerate the discovery of promising new drug molecules. A decade of AI-driven innovation The origins of Argonne's AI-driven cancer research date back to 2016, when DOE forged a partnership with National Cancer Institute (NCI) to employ advanced computing technologies in the fight against cancer. Argonne has been a key player in this collaboration, developing software and AI tools to accelerate progress in cancer research. A cornerstone of this effort was the CANcer Distributed Learning Environment (CANDLE) project. Led by Argonne's Rick Stevens and supported by DOE's Exascale Computing Project, CANDLE's goal was to develop a scalable deep learning software stack for the nation's exascale supercomputers. With that foundation, the lab's focus extended from building AI models to developing a rigorous method to assess the growing number of models emerging from the broader cancer research community. This shift led to the launch of the IMPROVE (Innovative Methodologies and New Data for Predictive Oncology Model Evaluation) project in 2021. Led by Argonne in collaboration with Frederick National Laboratory for Cancer Research, the IMPROVE team set out to develop a standardized way to analyze and compare the performance of various drug response prediction models. While the team's efforts are laying the groundwork for more reliable and effective AI models, the project continues to evolve to meet new challenges as they emerge. The next frontier: 'undruggable' targets Expanding on the DOE-NCI efforts, researchers are now setting their sights on a longstanding challenge in cancer research: 'undruggable' targets (proteins that are known to resist chemical treatments). With a focus on proteins that play a key role in cancer progression, the team's work begins with a list of targets identified through lab experiments. The researchers then retrieve the protein sequences from public databases. If a protein's 3D structure is unknown, they work with scientists at the Advanced Photon Source (APS), a DOE Office of Science user facility, to determine it. The APS was recently upgraded to deliver significantly brighter X-ray beams, giving scientists a powerful tool to advance research across different fields. After determining the protein's structure, the team turns to Aurora to simulate the behavior and interactions of the protein at the atomic level. The simulations combined with experimental data help identify areas where small molecules might bind to inhibit the protein's activity. The computational results are then relayed to experimental collaborators to validate the findings. Adding to the challenge is the team's focus on undruggable targets. Inhibiting these proteins has eluded researchers for decades, earning them a reputation as one of the most difficult problems in cancer biology.
