Presentation
Classifying Performance Bounds Using Machine Learning
DescriptionTraditional performance analysis tools, such as the roofline model, require visual interpretation to determine performance bounds. For CPUs which have complex cache hierarchies and front-end out-of-order capabilities—that is, the CPUs we use for high performance computing—accurately identifying the true performance bound is challenging. This work is the first step towards a data-driven approach to performance modeling, leveraging machine learning techniques. We build and evaluate a number of supervised and unsupervised models using a new curated data set of performance counters collected from well-understood (i.e., easily labeled) benchmark applications. We further analyze the data set and highlight potential "performance fingerprints" obtainable using this methodology.
Event Type
Research and ACM SRC Posters
TimeTuesday, 18 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
Archive
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