Kernel Tuner

Version 0.4.4 adds extended support for energy efficiency tuning. In particular, with the new capability to fit a performance model to the target GPUs power-frequency curve. How to use these features is demonstrated in: https://github.com/KernelTuner/kernel_tuner/blob/master/examples/cuda/going_green_performance_model.py

And described in the paper:

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning R. Schoonhoven, B. Veenboer, B. van Werkhoven, K. J. Batenburg International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) at Supercomputing (SC22) 2022 https://arxiv.org/abs/2211.07260

Other than that, we've implemented a new output and metadata JSON format that adheres to the 'T4' auto-tuning schema created by the auto-tuning community at the Lorentz Center workshop in March 2022.

From the changelog:

• Support for using time_limit in simulation mode
• Helper functions for energy tuning
• Example to show ridge frequency and power-frequency model
• Functions to store tuning output and metadata

• Changed what timings are stored in cache files
• No longer inserting partial loop unrolling factor of 0 in CUDA