2020/12/08 by Jiwei Liu, Liu, Jiwei, Bojan Tunguz +3 · 1 citation
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification #Metaheuristic Optimization Algorithms Research
paper · pdf · doi:10.48550/arxiv.2012.04201
openalex publication_date 2020/12/08 · openalex created_date 2020/12/21 · openalex updated_date 2026/07/28
Black-box optimization is essential for tuning complex machine learning algorithms which are easier to experiment with than to understand. In this paper, we show that a simple ensemble of black-box optimization algorithms can outperform any single one of them. However, searching for such an optimal ensemble requires a large number of experiments. We propose a Multi-GPU-optimized framework to accelerate a brute force search for the optimal ensemble of black-box optimization algorithms by running many experiments in parallel. The lightweight optimizations are performed by CPU while expensive model training and evaluations are assigned to GPUs. We evaluate 15 optimizers by training 2.7 million models and running 541,440 optimizations. On a DGX-1, the search time is reduced from more than 10 days on two 20-core CPUs to less than 24 hours on 8-GPUs. With the optimal ensemble found by GPU-accelerated exhaustive search, we won the 2nd place of NeurIPS 2020 black-box optimization challenge.