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arXiv · 2403.17017

Seer: Predictive Runtime Kernel Selection for Irregular Problems

Ryan Swann·Muhammad Osama·Karthik Sangaiah·Jalal Mahmud
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Abstract

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.

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Ryan Swann, Muhammad Osama, Karthik Sangaiah, Jalal Mahmud. 2024-02-19. Seer: Predictive Runtime Kernel Selection for Irregular Problems. https://doi.org/10.1145/3640537.3641569

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