- #INTEL OPENCL CYGWIN TUTORIAL HOW TO#
- #INTEL OPENCL CYGWIN TUTORIAL CODE#
run cd Build to get inside build directory. ensure that path to vendor SDK is correctly set in the environment variables. OpenCL, CUDA or Vulkan library, supported SDKs are AMD OCL SDK, Intel SDK for OpenCL, NVIDIA CUDA Toolkitīuild under Linux (inside KTT root folder):. C++17 compiler, for example Clang 7.0, GCC 9.1, MSVC 14.16 (Visual Studio 2017) or newer. 
KTT can be built as a dynamic (shared) library using command line build tool Premake.
Bicg: Biconjugate gradients method example, features reference computation, composite kernels and constraints. Sort: Radix sort example, combines multiple kernels into composite kernel. Reduction: Tuning of vector reduction, launches a kernel iteratively. CoulombSum3d: Alternative to iterative version, utilizes kernel which computes the entire map in single invocation. CoulombSum3dIterative: 3D version of previous example, utilizes kernel from 2D version and launches it iteratively. CoulombSum2d: Tuning of electrostatic potential map computation, focuses on a single slice. List of some of the currently available examples: They are more complex than tutorials andĪssume that reader is familiar with KTT API. VectorArgumentCustomization: Showcasing different usage options for vector kernel arguments.Įxamples showcase how KTT framework could be utilized in real-world scenarios. ComputeApiInitializer: Providing tuner with custom compute context, queues and buffers. CustomArgumentTypes: Usage of kernel arguments with custom data types and validating the output with value comparator. KernelTuning: Simple kernel tuning using small number of tuning parameters and reference computation to validate output. KernelRunning: Running simple kernel with KTT framework and retrieving output. Info: Retrieving information about compute API platforms and devices through KTT API. Tutorials assume that reader has some knowledge about C++ and GPU programming. Most of the tutorials are also available for All tutorials are available for both OpenCL and CUDA backends. Tutorials are short examples which serve as an introduction to KTT framework. #INTEL OPENCL CYGWIN TUTORIAL HOW TO#
Please check the Building KTT section for detailed instructions on how to perform a build.
Prebuilt binaries are not provided due to many different combinations of compute APIs and build options available. The newest release of KTT framework can be found here. Documentation for KTT API can be found here. 
Large number of customization options, including support for kernel arguments with user-defined data types,Ībility to change kernel compiler flags and more. (e.g., changing the kernel source file), no library recompilation is needed. #INTEL OPENCL CYGWIN TUTORIAL CODE#
Support for multiple compute APIs, switching between CUDA, OpenCL and Vulkan requires only minor changes in C++ code. Ability to automatically ensure correctness of tuned computation with reference kernel or C++ function. Support for online auto-tuning - kernel tuning combined with regular kernel running. Support for multiple compute queues and asynchronous operations. Support for iterative kernel launches and composite kernels. In order to optimize computation for a particular device. Ability to define kernel tuning parameters such as kernel thread sizes, vector data types and loop unroll factors. Version 2.0 which contains majorĪPI overhaul as well as new features and improvements is now available. KTT is an auto-tuning framework for OpenCL, CUDA kernels and GLSL compute shaders.
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