Tootfinder

Seer: Predictive Runtime Kernel Selection for Irregular Problems
Ryan Swann, Muhammad Osama, Karthik Sangaiah, Jalal Mahmud
https://arxiv.org/abs/2403.17017

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 Multiplicati…