Graphic Processing Units (GPU) has been proved to be a promising platform to accelerate large size Fast Fourier Transform (FFT) computation. However, GPU performance is severely restricted by the limited memory size and the low bandwidth of data transfer through PCI channel. Additionally, current GPU based FFT implementation only uses GPU to compute, but employs CPU as a mere memory-transfer controller. The computing power of CPUs is wasted. This paper proposes a hybrid parallel framework to use both multi-core CPU and GPU in heterogeneous systems to compute large-scale 2D and 3D FFTs that exceed GPU memory. This work introduces a flexible partitioning scheme that enables concurrent execution of CPU and GPU and integrates several FFT decomposition paradigms to tailor computation and communication. Moreover, our library exposes and exploits previously overlooked parallelism in FFT. Optimal load balancing is automatically achieved from effective performance modeling and empirical tuning process. On average, our large FFT library on GeForce GTX480, Tesla C2070, C2075 is 121% and 145% faster than 4-thread SSE-enabled FFTW and Intel MKL, with max speedups 4.61 and 2.81, respectively.
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