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SYCL Academy

Exercise 15: Image Convolution

In this exercise there is no task, simply familiarize yourself with the image convolution reference code, as this will be used in later exercises.

1.) Reference image

For the purposes of this exercise we have provided an image in the exercise directory called "dogs.png", however feel free to replace this with any other 32bit PNG image, though note that this exercise will work best with images where the dimensions are multiples of 2, such as 512x512.

Note you will have to update the path to an image. There is the image in the repository but feel free to use any image you choose. Though it's recommend that you use a png image whose dimensions are multiple of 2 (for example 512x512) and has four channels (RGBA).

2.) Stb library

The source for this example provides a stub which loads and write an image using the STB image library.

3.) Benchmarking

The source also contains a call to a benchmarking utility that will print the time taken to execute the SYCL code, the SYCL code should go inside the lambda that is passed to the benchmark function.

Though note that the benchmark facility provided measures whole application time which is less accurate than measuring the kernel execution times alone.

Try running the application and recording the benchmark result timing you see so you can compare this with results in later exercises.

Note if you are running on the host device the default iterations for the benchmark of 100 will take a while to execute so try reducing this number.

4.) Dimensionality

The reference code uses a 2-dimensional range in parallel_for as this often simplifies the code when working with images.

5.) Convolution filters

The image convolution support code provides a filter_type enum which allows you to choose between identity and blur. The utility for generating the filter data; generate_filter takes a filter_type and a width.

Build and execution hints

For DPC++: Using CMake to configure then build the exercise:

mkdir build
cd build
cmake .. "-GUnix Makefiles" -DSYCL_ACADEMY_USE_DPCPP=ON -DSYCL_ACADEMY_ENABLE_SOLUTIONS=OFF -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
make exercise_15

Alternatively from a terminal at the command line:

icpx -fsycl -o sycl-ex-15 -I../../Utilities/include/ -I../../External/stb reference.cpp

For AdaptiveCpp:

# <target specification> is a list of backends and devices to target, for example
# "omp;generic" compiles for CPUs with the OpenMP backend and GPUs using the generic single-pass compiler.
# The simplest target specification is "omp" which compiles for CPUs using the OpenMP backend.
cmake -DSYCL_ACADEMY_USE_ADAPTIVECPP=ON -DSYCL_ACADEMY_INSTALL_ROOT=/insert/path/to/adaptivecpp -DACPP_TARGETS="<target specification>" ..
make exercise_15

alternatively, without CMake:

cd Code_Exercises/Exercise_15_Image_Convolution
/path/to/adaptivecpp/bin/acpp -o sycl-ex-15 -I../../Utilities/include/ -I../../External/stb --acpp-targets="<target specification>" reference.cpp