An Automatic Performance Tuning Framework for FFT on Heterogenous Platforms

The fast Fourier transform (FFT) is an important computational kernel in scientific and engineering computation which has broad applicability, especially in the field of signal processing, image processing and solving partial differential equation. In this paper, we propose an automatic performance tuning framework, called MPFFT (massively parallel FFT), which is well-suited to heterogeneous platforms such as GPU (graphic processing unit) and APU (accelerated processing unit). We employ two-stage adaptation methodology in two levels, namely installation time and runtime. At installation time, there is a code generator that could automatically generate FFT codelet for arbitrary size called by GPU kernel. The code generator could also generate high optimized code for GPU kernel according to auto-tuning techniques at runtime. Experimental results demonstrate that MPFFT substantially outperforms the clAmdFft library both on AMD GPU and APU. For 1D, 2D and 3D FFT, the average speedup of MPFFT compared with clAmdFft 1.6 achieves up to 3.45, 15.20, 4.47 on AMD APU A-360 and 1.75, 3.01, 1.69 on AMD HD7970. It also achieves comparable performance as the CUFFT library on NVIDIA GPU, and the overall performance is within 93% of CUFFT 4.1 on Tesla C2050, and the maximum speedup is 1.28.