Classifying Performance Bounds Using Machine Learning

Traditional 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 steps towards a datadriven approach to performance modelling, 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 analyse the data set and highlight potential “performance fingerprints” obtainable using this methodology.