Optimizing HPC I/O Performance with Regression Analysis and Ensemble Learning

To improve parallel I/O performance, it is imperative to optimize the adjustable parameters across the different layers of the I/O software stack. Finding an optimal configuration for different scenarios is hampered by the complex interaction dynamics between these parameters and the large parameter space. Previous research efforts have focused on tuning these parameters using independent algorithms; however, these approaches exhibit certain shortcomings such as unstable performance results and delayed convergence rates. This paper introduces OPRAEL, an auto-tuning approach on parallel I/O tasks by ensembles and performance modeling using regression analysis. To test its effectiveness, we applied this approach on the Tianhe-II supercomputer using one well-known I/O benchmark(IOR) and two I/O kernels(S3D-I/O, BT-I/O). Leveraging our experience in predictive modeling, we optimized the tuning of the I/O stack parameters. Our experimental results show a remarkable 10.2X improvement in write performance speedup for the optimization task with BT-I/O and a 500x500x500 input. We also compared the potential of using a single search algorithm versus using reinforcement learning search in the I/O parameter auto-optimization task. Our results show that OPRAEL outperforms the traditional approach, resulting in a maximum 8.4X improvement in write performance for the 128-process IOR optimization.