When Faster Supercomputers Aren’t Enough
"Arthur T Knackerbracket" writes:
When a million veterans shared their genetic data by enrolling in the Department of Veterans Affairs' "Million Veteran Program," they expected nothing back. This did not surprise Ravi Madduri, a senior scientist with the Data Science and Learning Division at the U.S. Department of Energy's (DOE) Argonne National Laboratory. He is abundantly clear about the terms under which these veterans donated their data: "Even after giving everything to the country, they found it in their hearts to give more without expecting anything in return."
That gift, which constituted the nation's largest biorepository and most ancestrally varied cohorts of veteran data in the world, created a debt that Madduri, who also holds a joint appointment in the Department of Human Genetics at the University of Chicago, is working to repay.
"What I feel when I look at that data is that I have an obligation to deliver to the veterans on their intentions in doing this," he says. Those intentions, as he reads it, are that others, veteran or not, can benefit from whatever the data can reveal.
"We cannot dilly dally," Madduri says.
After arriving at Argonne as a graduate student in 2001, Madduri has spent the last two decades at the intersection of supercomputing and biomedicine, much of the time building the computing that turns the veterans' data into results impacting areas from post-traumatic stress disorder to heart disease to cancer.
He explains that machines have largely held up their end, but the obstacles between precious data and delivered results are not always solved with faster machines. Understanding the challenges is the path toward better science.
Any two human genomes differ at millions of specific positions. A genome-wide association study asks a seemingly simple question across an enormous volume of data. For example, does a given genetic variant travel together with a particular medical condition? On its own, the question is simple. Multiplied across every variant worth testing and every condition recorded in a patient's chart, it becomes an enormous math problem, which is what supercomputers exist to solve. The harder question is whether the code is written to let them.
Madduri's team has run this problem on the Summit system at DOE's Oak Ridge Leadership Computing Facility and on the Aurora system at the Argonne Leadership Computing Facility. On Summit, the team found that the job would not finish on the machine's CPUs. As the cohort grew, the matrix the analysis needed for each group no longer fit in memory, and the job stalled.
"We decided to open the box and look at the code," Madduri said. They found that the issue was not merely a speed problem, but a coding one.
The analysis had been written for ordinary processors because that is what the genetics community was running on. Underneath the algorithms, though, the code spent nearly all of its time doing one thing: multiplying very large grids of numbers. This is exactly the operation that graphics processors exist to perform, and what makes them the engine of modern artificial intelligence training. As Madduri notes, the same computational architecture accelerating AI discovery "is also foundational for population genomics." So, the team, led on this effort by Argonne's Alex Rodriguez, rewrote that part of the code for GPUs, and an analysis that would have taken roughly 481,000 CPU-core hours in the original version became tractable. This shift did more than speed up a slow job. It made a virtually impossible job possible.
The workhorse they accelerated is a widely used genetics package called SAIGE, which made the rewritten version SAIGE-GPU, described in a separate paper in Bioinformatics.
"We are not good at coming up with names. Sorry," Madduri says sheepishly.
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