|
|
- 2018
压缩感知重构算法的并行化及GPU加速
|
Abstract:
摘要： 针对压缩感知重构算法计算实时性太差的问题,提出压缩采样追踪匹配(compressive sampling matching pursuit,CoSaMP)算法的并行化加速算法。 基于多线程技术实现重构算法的粗粒度并行化,分析CoSaMP算法的计算热点,将其中耗时较多的矩阵操作移植在图形处理器(graphics processing unit, GPU)上,实现算法的细粒度并行化。在测试图像上进行试验,结果表明:并行化加速算法取得50倍的加速效果,有效地降低重构算法的计算时间开销。
Abstract: Aimed at the poor real-time performance of the compression sensing reconstruction algorithm, the parallel acceleration of the compressive sampling matching pursuit(CoSaMP)algorithm was proposed. Coarse grained parallelization of reconstruction algorithm was realized based on multithreading technology. The hotspot of CoSaMP algorithm was analyzed, and the matrix operation which was time-consuming was transplanted to graphics processing unit(GPU)to achieve fine grained parallelization of the algorithm. The experiments on the test image showed that 50-fold acceleration speedup was achieved and the study reduced the computing time cost of the reconstruction algorithm effectively
| [1] | CHO M, MISHRA K V, XU W. Computable performance guarantees for compressed sensing matrices[J]. Eurasip Journal on Advances in Signal Processing, 2018, 2018(1):16. |
| [2] | KARAHANOGLU N B, ERDOGAN H. A*orthogonal matching pursuit: best-first search for compressed sensing signal recovery[J]. Digital Signal Processing, 2012, 22(4):555-568. |
| [3] | BERNABé S, MARTíN G, NASCIMENTO J M P, et al. Parallel hyperspectral coded aperture for compressive sensing on GPUs[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016, 9(2):932-944. |
| [4] | JIANG H, GANESAN N. CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU[J]. Bmc Bioinformatics, 2016, 17(1):1-16. |
| [5] | WANG L, LU K, LIU P. Compressed sensing of a remote sensing image based on the priors of the reference image[J]. IEEE Geoscience & Remote Sensing Letters, 2015, 12(4):736-740. |
| [6] | BLANCHARD J D, TANNER J. GPU accelerated greedy algorithms for compressed sensing[J]. Mathematical Programming Computation, 2013, 5(3):267-304. |
| [7] | GILBERT R, MIJAILOVICH S. Distributed multi-scale muscle simulation in a hybrid MPI-CUDA computational environment[J]. Simulation, 2016, 92(1):19-31. |
| [8] | LUSTING M, DONOHO D, PAULY J M. Sparse MRI: the application of compressed sensing for rapid MR imaging [J]. Magnetic Resonance in Medicine, 2007, 58(6):1182-1195. |
| [9] | DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306. |
| [10] | HANAPPE P, BEURIVé A, LAGUZET F, et al. Famous, faster: using parallel computing techniques to accelerate the FAMOUS/HadCM3 climate model with a focus on the radiative transfer algorithm[J]. Geoscientific Model Development Discussions, 2011, 4(3):1273-1303. |
| [11] | GUNARATHNE T, ZHANG B, WU T L, et al. Scalable parallel computing on clouds using Twister4Azure iterative MapReduce[J]. Future Generation Computer Systems, 2013, 29(4):1035-1048. |
| [12] | LI S, FENG J. An optimized data processing model for computer big data platform based on parallel computing[J]. Boletin Tecnico/Technical Bulletin, 2017, 55(8):318-324. |
| [13] | BLANCHARD J D, TANNER J. GPU accelerated greedy algorithms for compressed sensing[J]. Mathematical Programming Computation, 2013, 5(3):267-304. |
| [14] | SHI L, CHEN H, SUN J. VCUDA: GPU accelerated high performance computing in virtual machines[J]. IEEE Transactions on Computers, 2012, 61(6):804-816. |
| [15] | MOUSTAFA M, EBEID H M, HELMY A, et al. Rapid real-time generation of super-resolution hyperspectral images through compressive sensing and GPU[J]. International Journal of Remote Sensing, 2016, 37(18):4201-4224. |
| [16] | FIGUEIREDO M A T, NOWAK R D, WRIGHT S J. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 1(4):586-597. |
| [17] | GHAHREMANI M, GHASSEMIAN H. Remote sensing image fusion using ripplet transform and compressed sensing[J]. IEEE Geoscience & Remote Sensing Letters, 2014, 12(3):502-506. |
| [18] | ZHAO Y, YOSHIGOE K, BIAN J, et al. A distributed graph-parallel computing system with lightweight communication overhead[J]. IEEE Transactions on Big Data, 2017, 2(3):204-218. |
| [19] | EGEL A, PATTELLI L, MAZZAMUTO G, et al. CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres[J]. Journal of Quantitative Spectroscopy & Radiative Transfer, 2017, 199:103-110. |
| [20] | ASANOVIC K, BODIK R, DEMMEL J, et al. A view of the parallel computing landscape[J]. Communications of the Acm, 2009, 52(10):56-67. |
| [21] | GARLAND M, GRAND S L, NICKOLLS J, et al. Parallel computing experiences with CUDA[J]. Micro IEEE, 2008, 28(4):13-27. |
| [22] | MAROOSI A, MUNIYANDI R C, SUNDARARAJAN E, et al. Parallel and distributed computing models on a graphics processing unit to accelerate simulation of membrane systems[J]. Simulation Modelling Practice & Theory, 2014, 47(47):60-78. |
| [23] | HUANG J W, ZHANG L Q, JIANG Z Y, et al. Heterogeneous parallel computing accelerated iterative subpixel digital image correlation[J]. Science China Technological Sciences, 2018, 61(1):74-85. |
| [24] | ROMERO-LAORDEN D, VILLAZóN-TERRAZAS J, MARTíNEZ-GRAULLERA O, et al. Analysis of parallel computing strategies to accelerate ultrasound imaging processes[J]. IEEE Transactions on Parallel & Distributed Systems, 2016, 27(12):3429-3440. |
