Nav: Home

Software updates slowing you down?

February 11, 2020

We've all shared the frustration -- software updates that are intended to make our applications run faster inadvertently end up doing just the opposite. These bugs, dubbed in the computer science field as performance regressions, are time-consuming to fix since locating software errors normally requires substantial human intervention.

To overcome this obstacle, researchers at Texas A&M University, in collaboration with computer scientists at Intel Labs, have now developed a complete automated way of identifying the source of errors caused by software updates. Their algorithm, based on a specialized form of machine learning called deep learning, is not only turnkey, but also quick, finding performance bugs in a matter of a few hours instead of days.

"Updating software can sometimes turn on you when errors creep in and cause slowdowns. This problem is even more exaggerated for companies that use large-scale software systems that are continuously evolving," said Dr. Abdullah Muzahid, assistant professor in the Department of Computer Science and Engineering. "We have designed a convenient tool for diagnosing performance regressions that is compatible with a whole range of software and programming languages, expanding its usefulness tremendously."

The researchers described their findings in the 32nd edition of Advances in Neural Information Processing Systems from the proceedings of the Neural Information Processing Systems conference in December.

To pinpoint the source of errors within a software, debuggers often check the status of performance counters within the central processing unit. These counters are lines of code that monitor how the program is being executed on the computer's hardware in the memory, for example. So, when the software runs, counters keep track of the number of times it accesses certain memory locations, the time it stays there and when it exits, among other things. Hence, when the software's behavior goes awry, counters are again used for diagnostics.

"Performance counters give an idea of the execution health of the program," said Muzahid. "So, if some program is not running as it is supposed to, these counters will usually have the telltale sign of anomalous behavior."

However, newer desktops and servers have hundreds of performance counters, making it virtually impossible to keep track of all of their statuses manually and then look for aberrant patterns that are indicative of a performance error. That is where Muzahid's machine learning comes in.

By using deep learning, the researchers were able to monitor data coming from a large number of the counters simultaneously by reducing the size of the data, which is similar to compressing a high-resolution image to a fraction of its original size by changing its format. In the lower dimensional data, their algorithm could then look for patterns that deviate from normal.

When their algorithm was ready, the researchers tested if it could find and diagnose a performance bug in a commercially available data management software used by companies to keep track of their numbers and figures. First, they trained their algorithm to recognize normal counter data by running an older, glitch-free version of the data management software. Next, they ran their algorithm on an updated version of the software with the performance regression. They found that their algorithm located and diagnosed the bug within a few hours. Muzahid said this type of analysis could take a considerable amount of time if done manually.

In addition to diagnosing performance regressions in software, Muzahid noted that their deep learning algorithm has potential uses in other areas of research as well, such as developing the technology needed for autonomous driving.

"The basic idea is once again the same, that is being able to detect an anomalous pattern," said Muzahid. "Self-driving cars must be able to detect whether a car or a human is in front of it and then act accordingly. So, it's again a form of anomaly detection and the good news is that is what our algorithm is already designed to do."

Other contributors to the research include Dr. Mejbah Alam, Dr. Justin Gottschlich, Dr. Nesime Tatbul, Dr. Javier Turek and Dr. Timothy Mattson from Intel Labs.
-end-


Texas A&M University

Related Algorithm Articles:

New algorithm to help process biological images
Skoltech researchers have presented a new biological image processing method that accurately picks out specific biological objects in complex images.
Skoltech scientists break Google's quantum algorithm
In the near term, Google has devised new quantum enhanced algorithms that operate in the presence of realistic noise.
The most human algorithm
A team from the research group SEES:lab of the Department of Chemical Engineering of the Universitat Rovira I Virgili and ICREA has made a breakthrough with the development of a new algorithm that makes more accurate predictions and generates mathematical models that also make it possible to understand these predictions.
Algorithm turns cancer gene discovery on its head
Prediction method could help personalize cancer treatments and reveal new drug targets.
New algorithm predicts gestational diabetes
Timely prediction may help prevent the condition using nutritional and lifestyle changes.
New algorithm could mean more efficient, accurate equipment for Army
Researchers working on an Army-funded project have developed an algorithm to simulate how electromagnetic waves interact with materials in devices to create equipment more efficiently and accurately.
Universal algorithm set to boost microscopes
EPFL scientists have developed an algorithm that can determine whether a super-resolution microscope is operating at maximum resolution based on a single image.
Algorithm designed to map universe, solve mysteries
Cornell University researchers have developed an algorithm designed to visualize models of the universe in order to solve some of physics' greatest mysteries.
Algorithm tells robots where nearby humans are headed
A new tool for predicting a person's movement trajectory may help humans and robots work together in close proximity.
Algorithm to transform investment banking with higher returns
A University of Bath researcher has created an algorithm which aims to remove the elements of chance, bias or emotion from investment banking decisions, a development which has the potential to reduce errors in financial decision making and improve financial returns in global markets.
More Algorithm News and Algorithm Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Listen Again: Reinvention
Change is hard, but it's also an opportunity to discover and reimagine what you thought you knew. From our economy, to music, to even ourselves–this hour TED speakers explore the power of reinvention. Guests include OK Go lead singer Damian Kulash Jr., former college gymnastics coach Valorie Kondos Field, Stockton Mayor Michael Tubbs, and entrepreneur Nick Hanauer.
Now Playing: Science for the People

#562 Superbug to Bedside
By now we're all good and scared about antibiotic resistance, one of the many things coming to get us all. But there's good news, sort of. News antibiotics are coming out! How do they get tested? What does that kind of a trial look like and how does it happen? Host Bethany Brookeshire talks with Matt McCarthy, author of "Superbugs: The Race to Stop an Epidemic", about the ins and outs of testing a new antibiotic in the hospital.
Now Playing: Radiolab

Dispatch 6: Strange Times
Covid has disrupted the most basic routines of our days and nights. But in the middle of a conversation about how to fight the virus, we find a place impervious to the stalled plans and frenetic demands of the outside world. It's a very different kind of front line, where urgent work means moving slow, and time is marked out in tiny pre-planned steps. Then, on a walk through the woods, we consider how the tempo of our lives affects our minds and discover how the beats of biology shape our bodies. This episode was produced with help from Molly Webster and Tracie Hunte. Support Radiolab today at Radiolab.org/donate.