Racing the Clock, COVID Killer Sought Among a Billion Molecules


Working from home, sometimes in pajamas, Ada Sedova taps into the world’s most powerful supercomputer in the hunt for a tiny molecule that could stop the coronavirus from infecting someone with COVID-19.
“I’m getting more done than ever, and with all the anxiety around the pandemic, I’m devoting a lot of my personal time to this effort,” said Sedova, a biophysics researcher at the Oak Ridge National Laboratory.
Her efforts could bring a 10-figure payday — specifically 2 billion molecular tests executed in just 24 hours.
Sedova seeks a ligand, an organic molecule less than a few dozen atoms in size. The right ligand will attach itself to a protein from the coronavirus, preventing it from infecting healthy cells.
The problem is there are so many ligands and proteins to check, and they keep changing shapes as their atomic forces shift. It’s one heck of a tiny needle in a ginormous stack of billions of possible compounds.
It could take many years for experts in wet labs to try each of the possibilities. Even simulating them all on the 9,216 CPUs on Summit , ORNL’s supercomputer, could take four years. So Sedova and colleagues turned to Summit’s 27,648 NVIDIA GPUs to accelerate their efforts.
They found a version of AutoDock, the open source program for simulating how proteins and ligands bind, from Scripps Research. It uses OpenCL on GPUs to speed processing up to 50x compared to CPUs.
CUDA Cuts to the Chase
With help from NVIDIA, the team ported the code to CUDA so it could run on Summit, delivering an added benefit of another 2.8x speedup. Another researcher, Aaron Scheinberg of Jubilee Development , accelerated the work another 3x when he found a way to use OpenMP to speed up feeding data to the GPUs.
In another test, Sedova showed results that suggest they may be able to screen a dataset of 1.4 billion compounds against a protein with high accuracy in as little as 12 hours. That’s more than a 33x speedup compared to a program running on CPUs.
GPUs reduced by more than an order of magnitude the time required to process a database of 1.4 billion ligands. They also narrowed the wide variability in results that made the process on CPUs hard to schedule on a supercomputer. “GPUs combined with Summit’s scale and architecture provide the capability for docking billions more compounds than what was possible previously,” she said.
Another member of the team, biophysicist Josh Vermaas, gave a shout-out to NVIDIA’s Scott Le Grand, who helped port AutoDock to CUDA. “He’s been a phenomenal help in improving performance from what used to be an OpenCL-only code,” said Vermaas in a blog on the origins of the work.
Simulating 2 Billion Compounds in 24 Hours
Sedova now believes with further improvements the team could create a capability to examine as many as two billion compounds in 24 hours. It would mark the first simulation of that...

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