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solution.cpp
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solution.cpp
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/*
SYCL Academy (c)
SYCL Academy is licensed under a Creative Commons
Attribution-ShareAlike 4.0 International License.
You should have received a copy of the license along with this
work. If not, see <http://creativecommons.org/licenses/by-sa/4.0/>.
*/
#include <algorithm>
#include <iostream>
#define CATCH_CONFIG_MAIN
#include <catch2/catch.hpp>
#include <sycl/sycl.hpp>
#include <benchmark.h>
#include <image_conv.h>
class image_convolution;
inline constexpr util::filter_type filterType = util::filter_type::blur;
inline constexpr int filterWidth = 11;
inline constexpr int halo = filterWidth / 2;
TEST_CASE("image_convolution_tiled", "local_memory_tiling_solution") {
constexpr auto inputImageFile = "../Images/dogs.png";
constexpr auto outputImageFile = "../Images/blurred_dogs.png";
auto inputImage = util::read_image(inputImageFile, halo);
auto outputImage = util::allocate_image(
inputImage.width(), inputImage.height(), inputImage.channels());
auto filter = util::generate_filter(util::filter_type::blur, filterWidth);
try {
sycl::queue myQueue{sycl::gpu_selector_v};
std::cout << "Running on "
<< myQueue.get_device().get_info<sycl::info::device::name>()
<< "\n";
auto inputImgWidth = inputImage.width();
auto inputImgHeight = inputImage.height();
auto channels = inputImage.channels();
auto filterWidth = filter.width();
auto halo = filter.half_width();
auto globalRange = sycl::range(inputImgHeight, inputImgWidth);
auto localRange = sycl::range(8, 8);
auto ndRange = sycl::nd_range(globalRange, localRange);
auto inBufRange =
sycl::range(inputImgHeight + (halo * 2), inputImgWidth + (halo * 2)) *
sycl::range(1, channels);
auto outBufRange =
sycl::range(inputImgHeight, inputImgWidth) * sycl::range(1, channels);
auto filterRange = filterWidth * sycl::range(1, channels);
auto scratchpadRange = localRange + sycl::range(halo * 2, halo * 2);
{
auto inBuf = sycl::buffer{inputImage.data(), inBufRange};
auto outBuf = sycl::buffer<float, 2>{outBufRange};
auto filterBuf = sycl::buffer{filter.data(), filterRange};
outBuf.set_final_data(outputImage.data());
auto inBufVec = inBuf.reinterpret<sycl::float4>(inBufRange /
sycl::range(1, channels));
auto outBufVec = outBuf.reinterpret<sycl::float4>(
outBufRange / sycl::range(1, channels));
auto filterBufVec = filterBuf.reinterpret<sycl::float4>(
filterRange / sycl::range(1, channels));
util::benchmark(
[&] {
myQueue.submit([&](sycl::handler &cgh) {
sycl::accessor inputAcc{inBufVec, cgh, sycl::read_only};
sycl::accessor outputAcc{outBufVec, cgh, sycl::write_only};
sycl::accessor filterAcc{filterBufVec, cgh, sycl::read_only};
auto scratchpad = sycl::local_accessor<sycl::float4, 2>(
scratchpadRange, cgh);
cgh.parallel_for<image_convolution>(
ndRange, [=](sycl::nd_item<2> item) {
auto globalId = item.get_global_id();
auto groupId = item.get_group().get_group_id();
auto localId = item.get_local_id();
auto globalGroupOffset = groupId * localRange;
/*
* Each work group will need to read a tile of size
* (localRange[0] + halo * 2, localRange[1] + halo * 2) in order to write a
* tile of size (localRange[0], localRange[1]). Since the size of the tile
* we need to read is larger than the workgroup size (localRange), we must
* do multiple loads per work item. The iterations of the for loop work
* are as follows:
*
* <- localRange[0] + halo *2 ->
* +------------------------------+ ^
* |+-----------------++---------+| |
* ^ ||<-localRange[0]->|| || |
* | || || || |
* local || iteration 1 || it 2 || |
* Range[1] || load || load ||
* | || || || localRange[1] +
* | || || || halo * 2
* V || || ||
* |+-----------------++---------+| |
* |+-----------------++---------+| |
* || || || |
* || it 3 load ||it 4 load|| |
* || || || |
* |+-----------------++---------+| |
* +------------------------------+ V
*/
for (auto i = localId[0]; i < scratchpadRange[0];
i += localRange[0]) {
for (auto j = localId[1]; j < scratchpadRange[1];
j += localRange[1]) {
scratchpad[i][j] =
inputAcc[globalGroupOffset + sycl::range(i, j)];
}
}
sycl::group_barrier(item.get_group());
auto sum = sycl::float4{0.0f, 0.0f, 0.0f, 0.0f};
for (int r = 0; r < filterWidth; ++r) {
for (int c = 0; c < filterWidth; ++c) {
auto idx = sycl::range(r, c);
sum += scratchpad[localId + idx] * filterAcc[idx];
}
}
outputAcc[globalId] = sum;
});
});
myQueue.wait_and_throw();
},
100, "image convolution (tiled)");
}
} catch (sycl::exception e) {
std::cout << "Exception caught: " << e.what() << std::endl;
}
util::write_image(outputImage, outputImageFile);
REQUIRE(true);
}