BLG-Tuning: Benchmark-Based Low-Cost General-Purpose I/O Modeling and Tuning

I/O performance has become a major bottleneck for many data-intensive applications. Each layer of the parallel I/O stack provides parameters that can optimize I/O performance, but determining the optimal performance parameters based on the operating configuration is a challenge. Previous work has required separate performance models for different programs for tuning, which is very costly in term of measurement data. We propose BLG-Tuning: a B enchmark-based L ow-cost G eneral-purpose I/O Modeling and Tuning. BLG-Tuning maps application I/O loads to benchmark parameters and uses the benchmark-trained performance model to achieve I/O performance prediction and thus avoid the additional computing and communication overhead for measurement. For applications, BLG-Tuning collects the application characteristics to calibrate the performance model and improve prediction accuracy. Experience shows that BLG-Tuning predicts the I/O time of MADbench2, Flash-IO, S3D-IO, BT-IO, and LAMMPS with MAPE of 22.2%, 18.2%, 29.3%, 21.5%, and 34.8%, respectively. After tuning, the five applications obtain I/O speedup from 5.6 × to 27.3 ×.