Performance prediction and ranking of SpMV kernels on GPU architectures

Abstract

Predicting the runtime of a sparse matrix-vector multiplication (SpMV) for different sparse matrix formats and thread mappings allows the dynamic selection of the most appropriate matrix format and thread mapping for a given matrix. This paper introduces two new generally applicable performance models for SpMV – for linear and non-linear relationships – based on machine learning techniques. This approach supersedes the common manual development of an explicit performance model for a new architecture or for a new format based on empirical data. The two new models are compared to an existing explicit performance model on different GPUs. Results show that the quality of performance prediction results, the ranking of the alternatives, and the adaptability to other formats/architectures of the two machine learning techniques is better than that of the explicit performance model.