About 10 percent of people who are prescribed opioids experience full-blown addiction, yet there’s no way to know who is most susceptible.
Genomics can likely explain this riddle — by identifying genetic predispositions to addiction — but such research will take enormous calculations on massive datasets.
Researchers at Oak Ridge National Laboratory are using the biggest GPU-powered computing cluster ever built to perform these calculations faster than ever. It’s part of an effort that could one day lead to alternative medications for pain management and help mitigate the opioid addiction crisis.
“We’ve been dreaming about solving these sorts of problems for years,” said Dan Jacobson, chief scientist of computational systems biology at ORNL, one of several labs run by the U.S. Department of Energy and home to Summit, the world’s fastest supercomputer.
Scientists across numerous fields are hailing Summit as a huge leap in computational research capabilities. Its 27,000-plus NVIDIA V100 Tensor Core GPUs deliver exponentially more processing power than was possible just a few years ago.
In the world of biology, this translates to being able to zoom in closer on the molecular level and explore new frontiers. That, in turn, will enable researchers to learn more about how all the components of a cell interact, and to do studies on a population scale.
Among the first such projects will be an effort led by Jacobson to train a machine learning model on genomic data in the hopes of accurately predicting whether a patient is predisposed to opioid addiction.
“Tensor Cores on GPUs on Summit will give us this enormous boost in performance to solve fundamental biological problems we simply couldn’t before,” he said.
Jacobson’s team plans to tap Summit’s prodigious mathematical capabilities by running immense calculations on genetics data that will help establish correlations between that data and the likelihood of addiction.
First, the team will use Summit to look for genetic changes across an entire population, then it’ll write algorithms to search for correlations between those changes.
To do this, the team is working with a Veterans Administration dataset of clinical records for 23 million people going back two decades. It already has assembled a dataset of genomics correlations on 600,000 people. The goal is to build that to about 2 million.
Once they have a large enough set of these correlations, the team can start testing them against two groups: Those who have developed opioid addiction and a control group who have been exposed but not developed an addiction.
Which brings us to the math: Jacobson said the very first calculation would require somewhere on the order of 10 to the 16th power (or 10 quadrillion) comparisons, and that operation would be repeated thousands, possibly even hundreds of thousands, of times.
Being able to perform such calculations in manageable amounts of time will open the doors to new ways of dealing with the growing opioid addiction crisis.
“We can develop better therapies for addiction, we can develop better therapies for chronic pain, and we can predict which patients will become addicted to opioids and then not give them opioids,” said Jacobson.
More Breakthroughs to Come
The team already has been able to test some of its applications on Summit and has managed to boost performance from 1.8 exaflops to 2.36 exaflops on its algorithm. It’s the fastest science application ever reported, and earned the team a Gordon Bell Prize in 2018. (For reference, one exaflops equals 1 quintillion, or billion billion, operations per second.)
As it continues to refine performance, Jacobson’s team expects to achieve higher levels of accuracy and to get those results faster.
That’s almost hard to imagine, given that Jacobson already said that his team can, in one hour on Summit, complete tasks that would require 35 years on a competing supercomputer, or 12,000 years on a laptop.
Jacobson believes that being able to do his team’s work on opioid addiction on Summit will lead to breakthrough treatments of other conditions, such as Alzheimer’s, dementia, prostate cancer and cardiovascular disease.
“Once we understand the complex genetic architecture underlying addiction, we want to do this really for all clinical disease states that seem to have some sort of genetic underpinning,” said Jacobson. “It’s machines like Summit that give us the ability to do that at scale, so we can now start to answer scientific questions that were literally impossible earlier this year.”
Learn more about Summit and why it plays such a critical role in enabling scientific progress in the video below.