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Performance Tuning of HPC and ML/AI Applications with the Roofline Model on GPUs, APUs, and CPUs

DescriptionThe Roofline performance model offers an insightful and intuitive method for extracting the key execution characteristics of HPC and ML/AI applications and comparing them against the performance bounds of modern CPUs and GPUs. Its ability to abstract the complexity of memory hierarchies and identify the most profitable optimization techniques has made Roofline-based analysis increasingly popular in the HPC and ML/AI communities. Although different flavors of the Roofline model have been developed to deal with various definitions of memory data movement, there remains a need for a systematic methodology when applying them to analyze applications running on multicore and accelerated systems. This tutorial aims to bridge this gap on both CPUs and GPUs by both exposing the fundamental aspects behind different Roofline modeling principles as well as providing several practical use case scenarios that highlight their efficacy for application optimization. This tutorial presents a unique combination of instruction in Roofline by its creator; hands-on instruction in using Roofline within Intel’s, NVIDIA’s, and AMD’s production performance tools; and discussions of real-world Roofline use cases at ALCF, NERSC, and OLCF computing centers. The tutorial presenters have a long history of collaborating on the Roofline model and have presented several Roofline-based tutorials.

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