kernel-tuner 0.1.8

Documentation

The full documentation is available here.

Installation

The easiest way to install the Kernel Tuner is using pip:

To tune CUDA kernels:

• First, make sure you have the CUDA Toolkit installed

• Then type: pip install kernel_tuner[cuda]

To tune OpenCL kernels:

• First, make sure you have an OpenCL compiler for your intended OpenCL platform

• Then type: pip install kernel_tuner[opencl]

Or both:

• pip install kernel_tuner[cuda,opencl]

More information about how to install Kernel Tuner and its dependencies can be found in the installation guide

Example usage

The following shows a simple example for tuning a CUDA kernel:

kernel_string = """
__global__ void vector_add(float *c, float *a, float *b, int n) {
    int i = blockIdx.x * block_size_x + threadIdx.x;
    if (i<n) {
        c[i] = a[i] + b[i];
    }
}
"""

size = 10000000

a = numpy.random.randn(size).astype(numpy.float32)
b = numpy.random.randn(size).astype(numpy.float32)
c = numpy.zeros_like(b)
n = numpy.int32(size)
args = [c, a, b, n]

tune_params = dict()
tune_params["block_size_x"] = [32, 64, 128, 256, 512]

tune_kernel("vector_add", kernel_string, size, args, tune_params)

The exact same Python code can be used to tune an OpenCL kernel:

kernel_string = """
__kernel void vector_add(__global float *c, __global float *a, __global float *b, int n) {
    int i = get_global_id(0);
    if (i<n) {
        c[i] = a[i] + b[i];
    }
}
"""

The Kernel Tuner will detect the kernel language and select the right compiler and runtime. For every kernel in the parameter space, the Kernel Tuner will insert C preprocessor defines for the tunable parameters, compile, and benchmark the kernel. The timing results will be printed to the console, but are also returned by tune_kernel to allow further analysis. Note that this is just the default behavior, what and how tune_kernel does exactly is controlled through its many optional arguments.

You can find many - more extensive - example codes, in the examples directory and in the Kernel Tuner documentation pages.

Tuning host and kernel code

It is possible to tune for combinations of tunable parameters in both host and kernel code. This allows for a number of powerfull things, such as tuning the number of streams for a kernel that uses CUDA Streams or OpenCL Command Queues to overlap transfers between host and device with kernel execution. This can be done in combination with tuning the parameters inside the kernel code. See the convolution_streams example code and the documentation for a detailed explanation of the kernel tuner Python script.

Search strategies for tuning

Kernel Tuner supports several strategies: brute_force (default), random_sample, minimize, basinhopping, and diff_evo (differential evolution). Using different strategies is easy, you only need to specify to tune_kernel which strategy you would like to use, for example: strategy="basinhopping", method="TNC". For a full overview of the supported search strategies and methods please see the user api.

Correctness verification

Optionally, you can let the kernel tuner verify the output of every kernel it compiles and benchmarks, by passing an answer list. This list matches the list of arguments to the kernel, but contains the expected output of the kernel. Input arguments are replaced with None.

answer = [a+b, None, None]  # the order matches the arguments (in args) to the kernel
tune_kernel("vector_add", kernel_string, size, args, tune_params, answer=answer)

Contributing

Please see the Contributions Guide.

Citation

A scientific paper about the Kernel Tuner is in preparation, in the meantime please cite the Kernel Tuner as follows:

@misc{
  author = {Ben van Werkhoven},
  title = {Kernel Tuner: A simple CUDA/OpenCL Auto-Tuner in Python},
  year = {2018}
}

Related work

You may also like CLTune by Cedric Nugteren. CLTune is a C++ library for kernel tuning and supports various advanced features like machine learning to optimize the time spent on tuning kernels.