You'd think that, but I found all GPUs I'm using here to exhibit multimodal distribution of execution times in the FFT (this is for the cuFFT codepath). The GTX980 (not shown in the plot) and the Titan-X even have very prominent outliers. This is a figure that's going to be in the paper I'm currently writing:
https://dl.datenwolf.net/gpu_oct_benchmark_plots.pdf
I'm comparing the OCT processing execution times (with HOT caches, mind you) between a Titan-X and a GTX1080. The difference also shows up very prominently when looking at the kernel scheduling order as reported by NVPP.
Find our original publication here: https://doi.org/10.1364/BOE.5.002963
Since then we improved on that. For the resampling and complex tonemapping we determined empirically that a grid of 128 threads, each processing a whole line achieves the best throughput; there's a 2D parameter space of possible launch configurations and we brute force the whole thing (so far I didn't benchmark the RTX20xx and RTX30xx GPUs, but it was consistent between the GTX690 to GTX1080). The FFT plan is what cufftPlan1d is producing for a single axis transform over a 2D array, usually 2048 point FFT, but with up to 4096 lines (well, technically whatever the maximum dimension for 3D textures is).
> Do you launch a big grid that consists of multiple samples combined in a matrix
Of course!
> or you launch each sample separately?
Of course not, that'd be stupid.