Kernkonzepte
Seer proposes a machine learning-based predictor, enabling runtime kernel selection for irregular workloads with significant performance improvements.
Zusammenfassung
The content discusses the challenges GPUs face with irregular data processing and introduces Seer, a predictive runtime framework. It covers the design, training, and inference process of Seer using Sparse Matrix Vector Multiplication as a case study. The methodology, model accuracy, and performance evaluation for single and multiple iterations are detailed. Key insights include the importance of feature collection cost consideration and the effectiveness of the classifier selection model in predicting kernel performance.
I. Introduction
GPUs designed for regular problems struggle with load imbalance in irregular data processing.
Seer offers a decision tree selector model for runtime kernel selection in irregular workloads.
A case study on Sparse Matrix Vector Multiplication (SpMV) showcases Seer's effectiveness.
II. Background and Related Works
Previous works highlight the importance of load balancing techniques and compressed sparse formats.
Comparison of various load balancing strategies and their impact on kernel performance is discussed.
III. Abstraction and Framework
Seer's two-level abstraction focuses on training models based on known and dynamically computed features.
Decision tree classifiers are used to predict the fastest kernel based on input features at runtime.
IV. Case Study
Evaluation of Seer using SpMV demonstrates 2× better performance over individual kernels.
Analysis includes single iteration and multiple iteration scenarios to assess predictor accuracy.
V. Conclusion
Seer provides an efficient solution for selecting optimal kernels at runtime in irregular workloads.
Future research directions include exploring additional feature collection strategies and expanding application areas.
Statistiken
Seer predicts the best strategy for SpMV with a 2× improvement over individual kernels across datasets.
Zitate
"Many libraries implement load balancing techniques without considering other potentially better strategies."
"Seer's decision tree model provides an explainable approach to kernel selection."