Kernel-tuner

Changed
- 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

Added
- full support for 3D grids, including option for grid_div_z
- separable convolution example

Changed
- changed the output format to list of dictionaries

Added
- option to set compiler options

Changed
- 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

Added
- 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

Added
- 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