KTT is an autotuning framework for OpenCL, CUDA kernels and GLSL compute shaders. Version 2.1 which introduces API bindings for Python and new onboarding guide is now available.

Main features

• Ability to define kernel tuning parameters such as kernel thread sizes, vector data types and loop unroll factors to optimize computation for a particular device.
• Support for iterative kernel launches and composite kernels.
• Support for multiple compute queues and asynchronous operations.
• Support for online auto-tuning - kernel tuning combined with regular kernel running.
• Ability to automatically ensure the correctness of tuned computation with reference kernel or C++ function.
• Support for multiple compute APIs, switching between CUDA, OpenCL and Vulkan requires only minor changes in C++ code (e.g., changing the kernel source file), no library recompilation is needed.
• Public API available in C++ (native) and Python (bindings).
• Many customization options, including support for kernel arguments with user-defined data types, ability to change kernel compiler flags and more.

Getting started

• Introductory guide to KTT can be found here.
• Full documentation for KTT API can be found here.
• KTT FAQ can be found here.
• The newest release of the KTT framework can be found here.
• Prebuilt binaries are not provided due to many different combinations of compute APIs and build options available. The Building KTT section contains detailed instructions on how to perform a build.

Tutorials

Tutorials are short examples that serve as an introduction to the KTT framework. Each tutorial covers a specific part of the API. All tutorials are available for both OpenCL and CUDA backends. Most of the tutorials are also available for Vulkan. Tutorials assume that the reader has some knowledge about C++ and GPU programming. List of the currently available tutorials:

• Info: Retrieving information about compute API platforms and devices through KTT API.
• KernelRunning: Running simple kernel with KTT framework and retrieving output.
• KernelTuning: Simple kernel tuning using a small number of tuning parameters and reference computation to validate output.
• CustomArgumentTypes: Usage of kernel arguments with custom data types and validating the output with value comparator.
• ComputeApiInitializer: Providing tuner with custom compute context, queues and buffers.
• VectorArgumentCustomization: Showcasing different usage options for vector kernel arguments.
• PythonInterfaces: Implementing custom searchers and stop conditions in Python, which can afterward be used with the tuner.

Examples

Examples showcase how the KTT framework could be utilized in real-world scenarios. They are more complex than tutorials and assume that the reader is familiar with KTT API. List of some of the currently available examples:

• CoulombSum2d: Tuning of electrostatic potential map computation, focuses on a single slice.
• CoulombSum3dIterative: 3D version of the previous example, utilizes kernel from 2D version and launches it iteratively.
• CoulombSum3d: Alternative to iterative version, utilizes kernel which computes the entire map in a single invocation.
• Nbody: Tuning of N-body simulation.
• Reduction: Tuning of vector reduction, launches a kernel iteratively.
• Sort: Radix sort example, combines multiple kernels into a composite kernel.
• Bicg: Biconjugate gradients method example, features reference computation, composite kernels and constraints.

Building KTT

• KTT can be built as a dynamic (shared) library using the command line build tool Premake. Currently supported operating systems are Linux and Windows.
• The prerequisites to build KTT are:
  • C++17 compiler, for example Clang 7.0, GCC 9.1, MSVC 14.16 (Visual Studio 2017) or newer
  • OpenCL, CUDA or Vulkan library, supported SDKs are AMD OCL SDK, Intel SDK for OpenCL, NVIDIA CUDA Toolkit and Vulkan SDK
  • Command line build tool Premake 5
  • (Optional) Python 3 with NumPy for Python bindings support
• Build under Linux (inside KTT root folder):
  • ensure that path to vendor SDK is correctly set in the environment variables
  • run ./premake5 gmake to generate makefile
  • run cd Build to get inside the build directory
  • afterwards run make config={configuration}_{architecture} to build the project (e.g., make config=release_x86_64)
• Build under Windows (inside KTT root folder):
  • ensure that path to vendor SDK is correctly set in the environment variables; this should be done automatically during SDK installation
  • run premake5.exe vs20xx (e.g., premake5.exe vs2019) to generate Visual Studio project files
  • open generated solution file and build the project inside Visual Studio
• The following build options are available:
  • --outdir=path specifies custom build directory, default build directory is Build
  • --platform=vendor specifies SDK used for building KTT, useful when multiple SDKs are installed
  • --profiling=library enables compilation of kernel profiling functionality using specified library
  • --vulkan enables compilation of experimental Vulkan backend
  • --python enables compilation of Python bindings
  • --no-examples disables compilation of examples
  • --no-tutorials disables compilation of tutorials
  • --tests enables compilation of unit tests
  • --no-cuda disables the inclusion of CUDA API during compilation, only affects Nvidia platform
  • --no-opencl disables the inclusion of OpenCL API during compilation
• KTT and applications that utilize it rely on external dynamic (shared) libraries to work correctly. There are multiple ways to provide access to these libraries, e.g., copying a given library inside the application folder or adding the containing folder to the library path (example for Linux: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/shared/library). Libraries which are bundled with device drivers are usually visible by default. The list of libraries currently utilized by KTT:
  • OpenCL distributed with specific device drivers (OpenCL only)
  • cuda distributed with specific device drivers (CUDA only)
  • nvrtc distributed with specific device drivers (CUDA only)
  • cupti bundled with Nvidia CUDA Toolkit (CUDA profiling only)
  • nvperf_host bundled with Nvidia CUDA Toolkit (new CUDA profiling only)
  • nvperf_target bundled with Nvidia CUDA Toolkit (new CUDA profiling only)
  • GPUPerfAPICL bundled with KTT distribution (AMD OpenCL profiling only)
  • vulkan distributed with specific device drivers (Vulkan only)
  • shaderc_shared bundled with Vulkan SDK (Vulkan only)

Python bindings

To be able to use KTT Python API, the KTT module must be built with --python option. For the build option to work, access to Python development headers and library must be provided under environment variables PYTHON_HEADERS and PYTHON_LIB respectively. Once the build is finished, in addition to the regular C++ module, a Python module will be created (named pyktt.pyd under Windows, pyktt.so under Linux). This module can be imported into Python programs in the same way as regular modules. Note that Python must have access to all modules which depend on the KTT module (e.g., various profiling libraries), otherwise the loading will fail.

Related projects

KTT API is based on CLTune project. Certain parts of the API are similar to CLTune. However, the internal structure is completely rewritten from scratch. The ClTuneGemm and ClTuneConvolution examples are adopted from CLTune.

KTT search space generation and tuning configuration storage techniques are derived from ATF project. Due to differences in API and available framework features, certain modifications were made to the original ATF algorithms. The examples stored in AtfSamples folder are adopted from ATF.

How to cite

F. Petrovič et al. A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit. In Future Generation Computer Systems, Volume 108, 2020.