Following the launch of its 2020 Call for CodeGlobal Challenge, IBM today announced that it will help coordinate an effort to provide over 3 to scientistsresearching COVID-19, the coronavirus that’s sickened over 300,000 people. Thecompany anticipates that the capacity will be used to develop algorithms thatassess how COVID-19 is progressing, and to model potential therapies in pursuitof a possible vaccine.
“These high-performance computing systems allow researchers torun very large numbers of calculations in epidemiology, bioinformatics, andmolecular modeling. These experiments would take years to complete if worked byhand, or months if handled on slower, traditional computing platforms,” wroteIBM Research director Dario Gil in a blog post. “Since the start of theCOVID-19 pandemic we have been working closely with governments in the U.S. andworldwide to find all available options to put our technology and expertise towork to help organizations be resilient and adapt to the consequences of thepandemic.”
As part of a newly launched consortium — the COVID-19 HighPerformance Computing (HPC) Consortium — that includes the White House Officeof Science and Technology Policy, the U.S. Department of Energy, MIT,Rensselaer Polytechnic Institute, Lawrence Livermore National Lab, ArgonneNational Lab, Oak Ridge National Laboratory, Sandia National Laboratory, LosAlamos National Laboratory, NASA, and the National Science Foundation,Microsoft, Google, Hewlett Packard Enterprise, and Amazon, IBM says it willassist in evaluating proposals from institutions and provide access to computefor projects that can “make the most immediate impact.” Teams will have attheir disposal 16 systems with a combined 775,000 processor cores and 34,000
GPUs, which can perform around 330 thousand million million floating-pointoperations per second (330 petaflops).
We want to make sure researchers working to combat COVID-19have access to the tools they need,” “Through our AI for Health program, we’ve seenfirst-hand the impact of empowering talented researchers with powerfultechnology. We hope that by expanding access to the Azure cloud and highperformance computing capabilities, and by creating more opportunities tocollaborate with our own data scientists, we can help accelerate this importantwork.
- John Kahan, Global Head, Microsoft AI for Health program
Researchers will be able to apply through a website beginning later today. They must describe whether support from staff at the national labs or other facilities will be essential, helpful, or unnecessary for their project and whether any restrictions might apply such as proprietary data sets or HIPAA restrictions.
Amazon, whose Amazon Web Services (AWS) division recentlylaunched said it’s offeringresearch institutions and companies technical support and promotional creditsfor the use of AWS programs to ,treatment, and vaccine studies. “We’re proud to support this critical work andstand ready with the compute power of AWS to help accelerate research anddevelopment efforts,” said AWS worldwide public sector vice president TeresaCarlson. “Working together, government, business, and academic leaders canutilize the power of the cloud to advance the pace of scientific discovery andinnovation and help combat the COVID-19 virus.”
For the Rensselaer Polytechnic Institute’s part, it will enlistits Artificial Intelligence Multiprocessing Optimized System (AiMOS) computerat the Rensselaer Centre for Computational Innovations, the 24th-most powerful supercomputer in the world, in the battle against the pandemic. Rensselaer says it is reaching out to the research community, including government entities, universities, and industry, to offer access to AiMOS in support of research related to COVID-19.
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We know that high-performance computing can reduce the time ittakes to process massive data sets and perform complex simulations from days to hours.We look forward to participating in this initiative alongsideleaders in technology, academia, and the public sector to make more resourcesavailable to COVID-19 researchers and to apply Google Cloud computingcapabilities toward the development of potential treatments and vaccines.
- Mike Daniels, Vice President, Google Cloud global public sector
The announcement follows news that scientists tapped IBM���s Summit at Oak Ridge National Laboratory, the world’s fastest supercomputer, to simulate how 8,000 different molecules would interact with COVID-19, resulting in the isolation of 77 compounds likely to render the virus unable to infect host cells. Elsewhere, the Tianhe-1 supercomputer at National Supercomputer Centre in Tianjin was recently used to process hundreds of images generated by computed tomography and give diagnoses in seconds. And the Gauss Centre for Supercomputing, an alliance of Germany’s three national supercomputing centres, said it would help those working on COVID-19 gain expedited access to computing resources.
More recently, Folding@home, one of the largest crowd-sourcedsupercomputing programs in the world kick-started an initiative to uncover the mysteries behind COVID-19’s spike protein, which the virus uses to infect cells. Since announcing in late February the new focus on the coronavirus, some 400,000 new volunteers joined the effort, according to project organizer and Washington University School of Medicine associate professor of biochemistry and molecular biophysics Greg Bowman.
Supercomputers have long been used to identify and testpotential treatments for complex and chronic diseases. Researchers tapped the Texas Advanced Computing Centre’s Lonestar5 cluster to simulate over 1,400 FDA-approved drugs to see if they could be used to treat cancer. Last June, eight supercomputing centres were selected across the E.U. to host applications in personalized medicine and drug design. And pharmaceutical company TwoXAR recently teamed up with the Asian Liver Centre at Stanford to screen 25,000 drug candidates for adult liver cancer.
The hope is that supercomputers can reduce the amount of time ittakes to bring novel drugs to market. Fewer than 12% of all drugs enteringclinical trials end up in pharmacies, and it takes at least 10 years formedicines to complete the journey from discovery to the marketplace. Clinicaltrials alone take six to seven years on average, inflating the cost of R&Dto roughly $2.6 billion, according to the Pharmaceutical Research andManufacturers of America.
“America is coming together to fight COVID-19, and that meansunleashing the full capacity of our world-class supercomputers to rapidlyadvance scientific research for treatments and a vaccine” said U.S. ChiefTechnology Officer Michael Kratsios of today’s news. “We thank the privatesector and academicleaders who are joining the federal government as part ofthe Trump Administration’s whole-of-America response.”
The White House previously partnered with Google parent companyVerily to develop screening to build a triaging tool to help people findCOVID-19 testing sites in the U.S., which is currently live for selectlocations in the San Francisco Bay Area. (Google is also working with the U.S.government to create self-screening tools for people wondering whether they should seek medical attention.) And last week, at the request of the White House Office of Science and Technology Policy, researchers and leaders from the Allen Institute for AI, Chan Zuckerberg Initiative, Microsoft, the National Library of Medicine at the National Institutes of Health, and others released a data set of over 29,000 articles about COVID-19, SARS-CoV-2, and the Coronavirus group.
This afternoon, U.S. President Trump gave Ford, GM, and Teslathe “go ahead” to make ventilators to help alleviate a shortage amid thepandemic, only days after Trump issued an executive order invoking the. COVID-19 is a respiratory disease, and ventilators are a critical piece of medical equipment used to treat hospitalized patients. The Society of Critical Care Medicine projects that 960,000coronavirus patients in the U.S. may need to be put on ventilators in thefuture, but the nation has only about 200,000 of the machines, and around halfare around older models that might not be ideal for critically ill patients.
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