Explaining the Performance of Supervised and Semi-Supervised Methods for Automated Sparse Matrix Format Selection
Sunidhi Dhandhania, Akshay Deodhar, Konstantin Pogorelov, Swarnendu Biswas, Johannes Langguth · 2021
The performance of sparse matrix-vector multiplication kernels (SpMV) depends on the sparse matrix storage format and the architecture and the memory hierarchy of the target processor. Many sparse matrix storage formats along with corresponding SpMV algorithms have been proposed for improved SpMV performance. Given a sparse matrix and a target architecture, supervised Machine Learning techniques automate selecting the best formats. However, existing supervised approaches suffer from several drawbacks. They depend on large representative datasets and are expensive to train. In addition, retraining to incorporate new classes of matrices or different processor architectures is just as costly since new training data must be generated by benchmarking many instances. Furthermore, it is hard to understand the results of many supervised systems.