xAMM: “Attention” to Details Improves Cross-Platform Prediction Accuracy
Aakash Raj Dhakal, Tanzima Zerin Islam, Arunavo Dey, Daniel Nichols, Abhinav Bhatelé, Tapasya Patki, Tom Scogland, Jae-Seung Yeom · 2025
As computing becomes the major enabler in more and more fields, computing platforms also have become more heterogeneous than ever before to support different needs. Inevitably, high performance computing (HPC) centers and cloud vendors offer a diverse array of computing platforms to the user, often to a point where it overwhelms users as well as system managers. Therefore, a cross-platform performance prediction model, which leverages observations from one platform to predict performance on another, can be extremely valuable. However, building such a model for numerous platforms requires an enormous amount of effort to collect training data, which is often prohibitively expensive. To overcome this challenge, we propose$\times \text{AMM}^{1}$11Pronounced as “Exam”, an end-to-end Machine Learning (ML) pipeline that uses the attention mechanism, a transformative concept in generative AI, for two purposes: learning smart embeddings from raw application performance samples and constructing Abstract Machine Models (AMMs)-compact representations of machine properties. By integrating performance sample embeddings with AMMs where available, xAMM improves the accuracy of the state-of-the-art XGBoost model by 49.64 % for CPU$\rightarrow$CPU and 99.07 % for CPU$\rightarrow$GPU prediction compared to building the model using raw data, a common approach in the existing literature.