Educating for Exascale
Berkeley Lab Hosts Summer School for Next Generation of Computational Chemists
August 10, 2018
Margie Wylie, firstname.lastname@example.org, +1.510.486.7421
Some 25 graduate and post-graduate students recently spent four intense days preparing for the next generation of parallel supercomputers and exascale at the Parallel Computing in Molecular Sciences (ParCompMolSci) Summer School and Workshop hosted by Berkeley Lab.
Held August 6 - 9 at the Brower Center in downtown Berkeley, the event aimed to “prepare the next generation of computational molecular scientists to use new parallel hardware platforms, such as the [DOE] exascale computer arriving in 2021,” said Berkeley Lab Senior Scientist Bert de Jong, an organizer of the summer school and one of the scientists behind the DOE Exascale Computing Project’s NWChemEx effort. NWChemEx belongs to the less talked about, but equally necessary half of building exascale systems: software.
It was, in fact, NWChemEx principal investigators behind the summer school and workshop, said de Jong. “We recognized the need for training graduate students and postdocs in our field, and we partnered with our National Science Foundation counterpart MoISSI to make it happen,” he said. MoISSI is the Molecular Sciences Software Institute.
“Workshops like this are particularly valuable for helping you find out what it is you don’t already know,” said attendee Marjory Clement, a Virginia Tech graduate student. “You won’t come away being an expert in any particular thing being taught, but you learn about new directions you may want to take in your own work,” she said. Clement, for example, had no exposure to using GPUs (graphical processing units) until the workshop. The GPU is one technology that will be deployed in future exascale systems.
Learning new ways to wring the most out of even bigger and more capable systems is especially important to fields like biochemistry and pharmaceuticals that need to simulate large drug molecules and complex proteins. “We’re never going to have enough compute power. As soon as they come out with a bigger machine, we’re going to simulate the biggest molecule we can on that and then we’re going to want something even bigger,” Clement said. This voracious appetite has made computational chemistry one of the driving applications for the nascent field of quantum computing, not coincidentally the subject of the gatherings’ final presentations.
Among the instructors were Berkeley Lab’s Sam Williams and Khaled Ibrahim who taught classes on intranode and hybrid programming models, respectively.
Other organizers of ParCompSci were Robert Harrison of the Institute of Advanced Computational Science (IACS) at Stony Brook University and Brookhaven National Laboratory, Ed Valeev of Virginia Tech, and Carlos Simmerling of the Laufer Center for Physical and Quantitative Biology at Stony Brook University. The DOE Exascale Computing Project and National Science Foundation’s MoISSI were major underwriters with additional funding from Berkeley Lab and IACS.
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The Computing Sciences Area at Lawrence Berkeley National Laboratory(Berkeley Lab) provides the computing and networking resources and expertise critical to advancing Department of Energy Office of Science (DOE-SC) research missions: developing new energy sources, improving energy efficiency, developing new materials, and increasing our understanding of ourselves, our world, and our universe. ESnet, the Energy Sciences Network, provides the high-bandwidth, reliable connections that link scientists at 40 DOE research sites to each other and to experimental facilities and supercomputing centers around the country. The National Energy Research Scientific Computing Center (NERSC) powers the discoveries of 7,000-plus scientists at national laboratories and universities. NERSC and ESnet are both Department of Energy Office of Science National User Facilities. The Computational Research Division (CRD) conducts research and development in mathematical modeling and simulation, algorithm design, data storage, management and analysis, computer system architecture and high-performance software implementation.
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