Title: GPU Performance and Power Estimation Using Machine Learning
Topic: Using machine learning for GPU performance and power rapid estimation
Idea: For training: Vary GPU setting from 3 perspective(They use 3 hardware params to illustrate the method, which can also be extend to many params): #compute units(CU), Mem Freq, Engine Freq to collect performance counters data, use K-means to cluster kernel performance: 1. CU scale, 2. Mem Freq & Engine Freq scale, use neural networks to classify kernel to cluster.
or prediction: run kernel on one setting once then classify to pattern cluster, use corresponding cluster centroid scaling values to make predictions on various GPU hardware setting.
Contribution: demonstrate on real hardware that the performance and power of GPGPU kernels scale in a limited number of ways as hardware configuration parameters are changed(finite unique scaling patterns), perform machine learning to estimate new kernel, describe an estimation model that run a kernel once on a hardware configuration then get performance and power over various configurations
The result shows that their accuracy is comparable to the accuracy of cycle-level simulators, and on real hardware the trained model runs equal or faster than native programs.