here is my feeble attempt at learning OpenCL, please don't make fun of me too much 🍔

This code uses OpenCL 1.1 on a NVIDIA GPU.

(Only tested on Ubuntu). For NVIDIA GPUs, I've installed the following packages: nvidia-346 nvidia-346-dev nvidia-346-uvm nvidia-libopencl1-346 nvidia-modprobe nvidia-opencl-icd-346 nvidia-settings. Since the opencl-headers package in the main repository is for OpenCL 1.2, you can get the OpenCL 1.1 header files from here.

Then to compile the C++ code:

g++ -std=c++0x main.cpp -o main.out -lOpenCL

To compile the C code:

gcc main.c -o main.out -lOpenCL

For examples 04 and 05, you can run

make ex04  # executable is ./example04/bin/Example
make ex05  # executable is ./example05/bin/Example
make       # makes both!

OpenCL is installed on OS X by default, but since this code uses the C++ bindings, you'll need to get that too. Get the official C++ bindings from the OpenCL registr and copy it to the OpenCL framework directory, or do the following:

wget https://www.khronos.org/registry/cl/api/1.1/cl.hpp
sudo cp cl.hpp /System/Library/Frameworks/OpenCL.framework/Headers/

To compile:

clang++ -std=c++0x -framework OpenCL main.cpp -o main.out

For some reason, the makefile didn't want to work for Windows. I have no idea why.

For example 04, run (inside the directory):

gcc -I/c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v7.5/include -I/c/PATH/TO/CLFFT/include main.c -o main.exe -L/c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v7.5/lib/x64 -lOpenCL -L/c/PATH/TO/CLFFT/lib64/import -lclFFT

where PATH/TO/CLFFT is the path to the clFFT library.

For example 05, run (inside the directory):

gcc -I/c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v7.5/include -I/c/PATH/TO/CLFFT/include -I/c/PATH/TO/FFTW main.c -o main.exe -L/c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v7.5/lib/x64 -lOpenCL -L/c/PATH/TO/CLFFT/lib64/import -lclFFT -L/c/PATH/TO/FFTW -lfftw3-3

where PATH/TO/FFTW is the path to the FFTW3 library.

this example is based off of this example (example-ception), but it goes a bit further. In the blogspot example, two 10-element vectors are created and a thread is used for each pair of elements. In this example, 10 threads are spawned but two 100-element vectors are used, and it is shown how to split up a specific number of elements per thread.

Measures the duration of adding two vectors. See the README in the folder for more details.

Demonstrates that one array can be modified several times without having to re-read and re-write data to and from the GPU.

A simple example using the cl_khr_fp64 extension which allows for usage of doubles instead of floats.

An example of the CLFFT library for an in-place complex-planar transform. There is also Python code to check the answer; FFTW code will be added later, probably.

• clFFT is required; installation instructions can be found inside example04/README.md
• for Python, numpy and scipy are required

Another CLFFT example where an in-place real transform and an out-of-place real transform are performed. There's also FFTW code and Python code for checking the answer.

• clFFT is required; installation instructions can be found inside example04/README.md
• FFTW is required; installation is as simple as extracting FFTW's tar file, then running ./configure && sudo make && sudo make install
• for Python, numpy and scipy are required

From the guide on programming OpenCL for NVIDIA:

• CUDA streaming multiprocessor corresponds to an OpenCL compute unit
• CUDA thread corresponds to an OpenCL work-item
• CUDA thread block corresponds to an OpenCL work-group