Seer: Predictive Runtime Kernel Selection for Irregular Problems

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


   series={CC ’24},

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



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


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





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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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