(575d) Tuning with Hybrid Derivative-Free Optimization Initialization Strategies
In this work we investigate the benefits of autotuning with novel hybrid DFO algorithms. While many search techniques implemented in autotuners utilize local DFO solvers or heuristic based methods, by combining global DFO solvers  with local pattern search techniques, e are able to quickly find high quality tuning parameters. Given a problem, we initialize our algorithms by using the global DFO solver DIRECT to identify a local solution. That local solution is then passed to a local DFO solver, where a new solution is obtained. Then a new solver is chosen and this procedure is repeated until we have exhausted a computational budget.
We compare our proposed methodology against autotuning techniques such as OpenTuner , Active Harmony  and other sophisticated DFO techniques. We conduct experiments on the dense matrix-matrix multiplication kernel. We focus on tuning GPU algorithms as parameters have a significant impact on computational performance. Our results demonstrate that our hybrid techniques are able to improve the performance of dense matrix-matrix multiplication by 130% compared to the best results found by state-of-the-art autotuners, while only requiring several hundred function evaluations.
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