All notable changes to this project will be documented in this file. This project adheres to Semantic Versioning.
- no longer replacing kernel names with instance strings during tuning
- bugfix in tempfile creation that lead to too many open files error
- A minimal Fortran example and basic Fortran support
- Particle Swarm Optimization strategy, use strategy="pso"
- Simulated Annealing strategy, use strategy="simulated_annealing"
- Firefly Algorithm strategy, use strategy="firefly_algorithm"
- Genetic Algorithm strategy, use strategy="genetic_algorithm"
- bugfix for C backend for byte array arguments
- argument type mismatches throw warning instead of exception
- wrapper functionality to wrap C++ functions
- citation file and zenodo doi generation for releases
- bugfix for when using iterations smaller than 3
- the install procedure now uses extras, e.g. [cuda,opencl]
- option quiet makes tune_kernel completely quiet
- extensive updates to documentation
- type checking for kernel arguments and answers lists
- checks for reserved keywords in tunable paramters
- checks for whether thread block dimensions are specified
- printing units for measured time with CUDA and OpenCL
- option to print all measured execution times
- bugfix install when scipy not present
- bugfix for GPU cleanup when using Noodles runner
- reworked the way strings are handled internally
- option to set compiler name, when using C backend
- actively freeing GPU memory after tuning
- bugfix for 3D grids when using OpenCL
- support for dynamic parallelism when using PyCUDA
- option to use differential evolution optimization
- global optimization strategies basinhopping, minimize
- option to pass a fraction to the sample runner
- fixed a bug in memset for OpenCL backend
- parallel tuning on single node using Noodles runner
- option to pass new defaults for block dimensions
- option to pass a Python function as code generator
- option to pass custom function for output verification
- device and kernel name are printed by runner
- tune_kernel also returns a dict with environment info
- using different timer in C vector add example
- changed how scalar arguments are handled internally
- separate install and contribution guides
- allow non-tuple problem_size for 1D grids
- changed default for grid_div_y from None to block_size_y
- converted the tutorial to a Jupyter Notebook
- CUDA backend prints device in use, similar to OpenCL backend
- migrating from nosetests to pytest
- rewrote many of the examples to save results to json files
- full support for 3D grids, including option for grid_div_z
- separable convolution example
- changed the output format to list of dictionaries
- option to set compiler options
- verbose now also prints debug output when correctness check fails
- restructured the utility functions into util and core
- restructured the code to prepare for different strategies
- shortened the output printed by the tune_kernel
- allowing numpy integers for specifying problem size
- a public roadmap
- requirements.txt
- example showing GPU code unit testing with the Kernel Tuner
- support for passing a (list of) filenames instead of kernel string
- runner that takes a random sample of 10 percent
- support for OpenCL platform selection
- support for using tuning parameter names in the problem size
- A function to type check the arguments to the kernel
- Example (convolution) that tunes the number of streams
- Device interface to C functions, for tuning host code
- Correctness checks for kernels during tuning
- Function for running a single kernel instance
- CHANGELOG file
- Compute Cartesian product and process restrictions before main loop
- Python 3.5 compatible code, thanks to Berend
- Support for constant memory arguments to CUDA kernels
- Use of mocking in unittests
- Reporting coverage to codacy
- OpenCL support
- Documentation pages with Convolution and Matrix Multiply examples
- Inspecting device properties at runtime
- Basic Kernel Tuning functionality