Speed of UMat vs Mat in DFT OpenCV TAPI - c++

Question

I found some interesting results regarding performing the cv::dft function on cv::UMats vs cv::Mats. Essentially I found that UMats are actually much slower until images up to 4096x4096. Until then, cv::Mat consistently wins. Is this just be cause the dft is not implemented for the TAPI api and only the CV::Mat implementation? The test I ran looks something like this (I used the celero project to create the benchmark):
constexpr int num_samples = 2;
constexpr int num_iterations = 10;
constexpr int num_rows = 4096;
constexpr int num_cols = 4096;
cv::UMat a = cv::UMat(num_rows, num_cols, CV_32F);
cv::Mat b = cv::Mat(num_rows, num_cols, CV_32F);
void CreateUMat() { cv::randu(a, 0, 256); }
void CreateMat() { cv::randu(b, 0, 256); }
void DftUMat() {
CreateUMat();
cv::dft(a, a);
cv::idft(a, a, cv::DFT_SCALE | cv::DFT_INVERSE);
}
void DftMat() {
CreateMat();
cv::dft(b, b);
cv::idft(b, b, cv::DFT_SCALE | cv::DFT_INVERSE);
}
BASELINE(UMatBenchmarks, Baseline, num_samples, num_iterations) { DftUMat(); }
BENCHMARK(UMatBenchmarks, NoGPU, num_samples, num_iterations) { DftMat(); }
I got the following results:
cv::UMat iterations/sec = 4.51
cv::Mat iterations/sec = 4.70
for a smaller image, say 1024x1024 I got the following results:
cv::UMat iterations/sec = 63.21
cv::Mat iterations/sec = 85.83
From these results, you can see that there is almost no advantage to using UMat for large images sizes and there is especially no advantage on smaller images. This surprises me because I got significant speed ups with cv::matchTemplate when switching to the OpenCV TAPI. My guess is that cv::dft has not been implemented in with OpenCL, but is this truly the case? Is the DFT just a not good algorithm to offload to the GPUS? Thanks!