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Get with the program.

I'm Bob Karson with the Discovery Files, from the National Science Foundation.

How many times do you feel things get all glitchy after a software update? Often bugs are introduced. Errors that cause programs to (Sound effect: slow motion voice) slo-o-o-w d-o-w-w-n, rather than run faster. Computer scientists call it: "Performance Regression" -- "P.R."

To get to the root of the "P.R.-oblem," debuggers often laboriously check performance counters within the CPU. When the software runs, these counters track how often it accesses certain memory locations, how long it's there and when it leaves. Info that usually reveals telltale signs of aberrations -- anomalies! Uh, something whack. But with hundreds of performance counters in newer desktops and servers, it's nearly impossible to track every status manually and then look for abnormal patterns.

Researchers at Texas A&M, together with computer scientists at Intel Labs, have developed an automated way to find performance bugs super-fast, based on a form of AI called 'Deep Learning.' Their algorithm is taught what normal counter data looked like on an older, glitch-free version of the software, and then looks at the updated version. In tests it was able to pinpoint bugs in a few hours instead of days.

(Sound effect: spaghetti western riff) A slowdown showdown at the CPU Corral.

"The discovery files" covers projects funded by the government's National Science Foundation. Federally sponsored research -- brought to you, by you! Learn more at nsf.gov or on our podcast.

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