Seer: Predictive Runtime Kernel Selection for Irregular Problems
<|reference_start|>Seer: Predictive Runtime Kernel Selection for Irregular Problems: Modern GPUs are designed for regular problems and suffer from load imbalance when processing irregular data. Prior to our work, a domain expert selects the best kernel to map fine-grained irregular parallelism to a GPU. We instead propose Seer, an abstraction for producing a simple, reproduceable, and understandable decision tree selector model which performs runtime kernel selection for irregular workloads. To showcase our framework, we conduct a case study in Sparse Matrix Vector Multiplication (SpMV), in which Seer predicts the best strategy for a given dataset with an improvement of 2$\times$ over the best single iteration kernel across the entire SuiteSparse Matrix Collection dataset.<|reference_end|>
@article{swann2024seer:,
    title={Seer: Predictive Runtime Kernel Selection for Irregular Problems},
    author={Ryan Swann, Muhammad Osama, Karthik Sangaiah, Jalal Mahmud},
    journal={arXiv preprint arXiv:2403.17017},
    year={2024},
    doi={10.1145/3640537.3641569},
    archivePrefix={arXiv},
    eprint={2403.17017},
    primaryClass={cs.DC}
}