Presentation
Unified Performance Modeling Stack for Distributed GPU Applications: Complementing Analytical Insights with Machine Learning
DescriptionModern HPC applications increasingly use GPUs to solve larger problems with higher accuracy and speed. However, committing resources to these large-scale systems is often costly and time-consuming. Hence, performance modeling enables developers to estimate runtime, analyze scalability, and identify resource bottlenecks in advance. In this work, we propose a unified software ecosystem for end-to-end performance modeling of distributed GPU applications. To this end, we propose a combination of analytical and machine learning-based modeling methodology, and design a comprehensive software stack to combine the various components for implementing such an approach. We validate the proposed framework using two real-life applications and provide performance estimations for the GPU kernel and inter-GPU MPI communications.
Event Type
Research and ACM SRC Posters
TimeTuesday, 18 November 20255:15pm - 7:00pm CST
LocationSecond Floor Atrium
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