Relative Performance Prediction using Few-Shot Learning

High-performance computing system architectures are evolving rapidly, making exhaustive data collection for each architecture to build predictive performance models increasingly impractical. Concurrently, the arrival of new applications daily necessitates efficient performance prediction methods. Traditional data collection can take days or weeks, making it more efficient for scientists to leverage existing models to predict an application's performance on new architectures or use data from one application to predict another on the same architecture. The growing heterogeneity in applications and resources further complicates the exact matches needed for effective knowledge transfer. This work systematically studies various Machine Learning (ML) models to predict the relative performance of new applications on new platforms using existing data. Our findings demonstrate that few-shot learning using a few samples significantly enhances cross-platform knowledge transfer, multi-source models outperform single-source models, and Large Language Models (LLMs)-generated samples can effectively improve knowledge transfer efficacy.