Yet Another CUDA FFT

Usage

Compiling the program

Type make to compile the program. Alternatively, type the following commands:

nvcc --compiler-options=-Wall -g -c argparse.c 
nvcc --compiler-options=-Wall -g argparse.o HugoRiveraA3.cu -o fft -lm

The files argparse.h and argparse.c are used for command line argument parsing, thanks to the lightweight argparse library.

Usage

$ ./fft -h
Usage: fft [options]

Compute the FFT of a dataset with a given size, using a specified DFT algorithm.

    -h, --help                show this help message and exit

Algorithm and data options
    -a, --algorithm=<str>     algorithm for computing the DFT (dft|fft|gpu|fft_gpu|dft_gpu),
                              default is 'dft'
    -f, --fill_with=<int>     fill data with this integer
    -s, --no_samples          do not set first part of array to sample data
    -N, --data_length=<int>   data length

Benchmark options
    -t, --measure_time=<int>  measure runtime. runs algorithms <int> times. set to 0 if not needed.
    -p, --no_print            do not print results

Measuring runtime

Runtime is easy to measure.

$ ./fft --measure_time=10 --no_print -N1024 -afft
Running Cooley-Tukey FFT with N=1024
    0.00028737 (s)
    0.00027086 (s)
    0.00027070 (s)
    0.00027063 (s)
    0.00027062 (s)
    0.00027062 (s)
    0.00027062 (s)
    0.00027062 (s)
    0.00027062 (s)
    0.00027062 (s)
$ ./fft --measure_time=10 --no_print -N1024 -afft_gpu
Running Cooley-Tukey FFT on GPU with N=1024
    0.00054887 (s)
    0.00044085 (s)
    0.00044584 (s)
    0.00042513 (s)
    0.00042042 (s)
    0.00041740 (s)
    0.00041829 (s)
    0.00041808 (s)
    0.00041718 (s)
    0.00041853 (s)

The FFT on the GPU only starts to outperform the FFT on the CPU on larger datasets.

$ ./fft --measure_time=10 --no_print -N65536 -afft
Running Cooley-Tukey FFT with N=65536
    0.02675756 (s)
    0.02649335 (s)
    0.02648379 (s)
    0.02648249 (s)
    0.02648116 (s)
    0.02648694 (s)
    0.02650917 (s)
    0.02648482 (s)
    0.02648311 (s)
    0.02648319 (s)
$ ./fft --measure_time=10 --no_print -N65536 -afft_gpu
Running Cooley-Tukey FFT on GPU with N=65536
    0.00158091 (s)
    0.00115752 (s)
    0.00116558 (s)
    0.00115046 (s)
    0.00115190 (s)
    0.00116676 (s)
    0.00114784 (s)
    0.00114956 (s)
    0.00114897 (s)
    0.00117116 (s)

Performance

In seconds

N	fft_gpu	fft	dft_gpu	dft
256	0.00041 ± 2.7e-05	6.99e-05 ± 9.4e-06	0.0004048 ± 1.7e-05	0.01285 ± 0.0014
512	0.00044 ± 4.2e-05	0.000137 ± 1.7e-07	0.0005946 ± 1.8e-05	0.04353 ± 0.0029
1024	0.00048 ± 3.1e-05	0.000277 ± 1.2e-05	0.00128 ± 2.3e-05	0.4002 ± 0.67
2048	0.00049 ± 2.7e-05	0.000468 ± 5.2e-06	0.004396 ± 0.00066	2.069 ± 1.0
4096	0.00047 ± 1.9e-05	0.00108 ± 2e-05	0.0155 ± 0.00091
8192	0.00062 ± 3.7e-05	0.00211 ± 6.2e-05
16384	0.00095 ± 2.7e-05	0.00454 ± 0.00017
32768	0.00185 ± 0.00032	0.00924 ± 0.00066
65536	0.00349 ± 0.00048	0.0187 ± 0.0033
131072	0.00763 ± 0.0019	0.0308 ± 0.0026
262144	0.0146 ± 0.0025	0.0621 ± 0.0026
524288	0.0253 ± 0.0028	0.137 ± 0.002

Speedup and Efficiency

The scripts time.sh and plot.py are used to gather and plot timing data from multiple runs.

{width=624 height=366}

{width=624 height=366}

Definition of the DFT

Let x be an N dimensional complex vector. Then the DFT of x is an N dimensional complex vector called Y where each element of Y is defined as follows:

Y[k] = sum from n=0 to N-1 of x[n] * exp(-2i * pi * n * k / N)

roguh/cuda-fft

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