29175

Seer: Predictive Runtime Kernel Selection for Irregular Problems

Ryan Swann, Muhammad Osama, Karthik Sangaiah, Jalal Mahmud
AMD Research, USA
arXiv:2403.17017 [cs.DC]

@inproceedings{Frenot_2024,

   series={CC ’24},

   title={Reducing the Overhead of Exact Profiling by Reusing Affine Variables},

   url={http://dx.doi.org/10.1145/3640537.3641569},

   DOI={10.1145/3640537.3641569},

   booktitle={Proceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction},

   publisher={ACM},

   author={Frenot, Leon and Pereira, Fernando Magno Quintão},

   year={2024},

   month={feb},

   collection={CC’24}

}

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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× over the best single iteration kernel across the entire SuiteSparse Matrix Collection dataset.
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