Kernel-tuner

- HIP backend to support tuning HIP kernels on AMD GPUs
- Experimental features for mixed-precision and accuracy tuning
- Experimental features for OpenACC tuning
- Major speedup due to new parser and using revamped python-constraint for searchspace building
- Implemented ability to use `PySMT` and `ATF` for searchspace building
- Added Poetry for dependency and build management
- Switched from `setup.py` and `setup.cfg` to `pyproject.toml` for centralized metadata, added relevant tests
- Updated GitHub Action workflows to use Poetry
- Updated dependencies, most notably NumPy is no longer version-locked as scikit-opt is no longer a dependency
- Documentation now uses `pyproject.toml` metadata, minor fixes and changes to be compatible with updated dependencies
- Set up Nox for testing on all supported Python versions in isolated environments
- Added linting information, VS Code settings and recommendations
- Discontinued use of `OrderedDict`, as all dictionaries in the Python versions used are already ordered
- Dropped Python 3.7 support

Added
- PMTObserver to measure power and energy on various platforms

Changed
- Improved functionality for storing output and metadata files
- Updated PowerSensorObserver to support PowerSensor3
- Refactored interal interfaces of runners and backends
- Bugfix in interface to set objective and optimization direction

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

Added
- A new backend that uses Nvidia cuda-python
- Support for locked clocks in NVMLObserver
- Support for measuring core voltages using NVML
- Support for custom preprocessor definitions
- Support for boolean scalar arguments in PyCUDA backend

Changed
- Migrated from github.com/benvanwerkhoven to github.com/KernelTuner
- Significant update to the documentation pages
- Unified benchmarking loops across backends
- Backends are no longer context managers
- Replaced the method for measuring power consumption using NVML
- Improved NVML measurements of temperature and clock frequencies
- bugfix in parse_restrictions when using and/or in expressions
- bugfix in GreedyILS when using neighbor method "adjacent"
- bugfix in Bayesian Optimization for small problems

Added
- new optimization strategies: dual annealing, greedly ILS, ordered greedy MLS, greedy MLS
- support for constant memory in cupy backend
- constraint solver to cut down time spent in creating search spaces
- support for custom tuning objectives
- support for max_fevals and time_limit in strategy_options of all strategies

Removed
- alternative Bayesian Optimization strategies that could not be used directly
- C++ wrapper module that was too specific and hardly used

Changed
- string-based restrictions are compiled into functions for improved performance
- genetic algorithm, MLS, ILS, random, and simulated annealing use new search space object
- diff evo, firefly, PSO are initialized using population of all valid configurations
- all strategies except brute_force strictly adhere to max_fevals and time_limit
- simulated annealing adapts annealing schedule to max_fevals if supplied
- minimize, basinhopping, and dual annealing start from a random valid config

Added
- support for PyTorch Tensors as input data type for kernels
- support for smem_args in run_kernel
- support for (lambda) function and string for dynamic shared memory size
- a new Bayesian Optimization strategy

Changed
- optionally store the kernel_string with store_results
- improved reporting of skipped configurations