HI-FFT: Heterogeneous Parallel In-place Algorithm for Large-scale 2D-FFT

• Published in: IEEE access Vol. 9; p. 1
• Main Authors: Kang, Homin, Lee, Jaehong, Kim, Duksu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 2D-FFT; Algorithms; Central processing units; Computation; Computer memory; CPU; CPUs; Decomposition; Discrete Fourier transforms; Fast Fourier transformations; Fourier transforms; GPU; Graphics processing units; Heterogeneous; Heterogeneous networks; In-place; Libraries; Matrix decomposition; Memory management; Parallel; Parallel algorithms; Priority scheduling
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3108404

Fast Fourier Transform (FFT) is a fundamental operation for 2D data in various applications. To accelerate large-scale 2D-FFT computation, we propose a Heterogeneous parallel In-place 2D-FFT algorithm, HI-FFT. Our novel work decomposition method makes it possible to run our parallel algorithm on the original data (i.e., in-place), unlike prior parallel algorithms that require additional memory space (i.e., out-of-place) to guarantee independence among sub-tasks. Our work decomposition method also removes the duplicated operations on the out-of-place approaches. Using our decomposition method, we introduced an in-place heterogeneous parallel algorithm that utilizes both multi-core CPU and GPU simultaneously. To maximize the utilization efficiency of the computing resources, we also propose a priority-based dynamic scheduling method.We compared the performance of seven different 2D-FFT algorithms, including ours, for large-scale 2D-FFT problems whose sizes varied from 20K2 to 120K2. As a result, we found that our method achieved up to 2.92 and 4.42 times higher performance than the conventional homogeneous parallel algorithms based on the state-of-the-art CPU and GPU libraries, respectively. Also, our method showed up to 2.27 times higher performance than the prior heterogeneous algorithms while requiring two times less memory space. To check the benefit of our HI-FFT on an actual application, we applied it to a CGH (Computer Generated Holography) process. We found that it successfully reduces the hologram generation time. These results demonstrate the advantage of our approach for large-scale 2D-FFT computation.