Automated Data Selection for Efficient Cost Model Training to Optimize Sparse Matrix Kernels on Emerging Hardware Accelerators
Automated Data Selection for Efficient Cost Model Training to Optimize Sparse Matrix Kernels on Emerging Hardware Accelerators
Chamika Sudusinghe,Gerasimos Gerogiannis,3 Authors,Charith Mendis
0 Citations
TLDR
This work frames optimizing sparse matrix kernels as a structured exploration problem and identifies key limitations in prior work, including its inability to generalize across kernels and hardware, and proposes a solution to automate the data collection effort for cost model training on emerging hardware accelerators.