A GPU-Accelerated Wavelet Decompression System With SPIHT and Reed-Solomon Decoding for Satellite Images

Song, Changhe; Li, Yunsong; Huang, Bormin

doi:10.1109/jstars.2011.2159962

Cited by 52 publications

(30 citation statements)

References 12 publications

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“…[25] employs it in a real-time SPIHT decoding system that uses Reed-Solomon codes. The partitioning scheme used is similar to the row-block but without the sliding window, which forces the reading of more data from the global memory.…”

Section: Previous and Related Workmentioning

confidence: 99%

Implementation of the DWT in a GPU through a Register-based Strategy

Enfedaque

Aulí-Llinàs

Moure

2015

IEEE Trans. Parallel Distrib. Syst.

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Abstract-The release of the CUDA Kepler architecture in March 2012 has provided Nvidia GPUs with a larger register memory space and instructions for the communication of registers among threads. This facilitates a new programming strategy that utilizes registers for data sharing and reusing in detriment of the shared memory. Such a programming strategy can significantly improve the performance of applications that reuse data heavily. This paper presents a register-based implementation of the Discrete Wavelet Transform (DWT), the prevailing data decorrelation technique in the field of image coding. Experimental results indicate that the proposed method is, at least, four times faster than the best GPU implementation of the DWT found in the literature. Furthermore, theoretical analysis coincide with experimental tests in proving that the execution times achieved by the proposed implementation are close to the GPU's performance limits.

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Section: Previous and Related Workmentioning

confidence: 99%

Implementation of the DWT in a GPU through a Register-based Strategy

Enfedaque

Aulí-Llinàs

Moure

2015

IEEE Trans. Parallel Distrib. Syst.

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“…(GPU) has gained great popularity in the field of high-performance processing for scientific and engineering applications such as image processing (Song et al, 2011). As shown simple example in Fig.…”

Section: Recent Tendency Of Graphics Processing Unitmentioning

confidence: 99%

Performance Study of Satellite Image Processing on Graphics Processors Unit Using CUDA

Jeong

Hong

Hahn

et al. 2012

Korean Journal of Remote Sensing

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“…These make GPUs more effective than a massively parallel system built from commodity central processing units (CPUs). Usage of GPUs has been applied very successfully to deal with numerous computational problems in various domains, for instance, porting marine ecosystem model spin-up using transport matrices to GPUs (Siewertsen et al, 2013), GPU-accelerated long-wave radiation scheme of the rapid radiative transfer model for general circulation (RRTMG) models (Price et al, 2014), advances in multi-GPU smoothed particle hydrodynamics simulations (Rustico et al, 2014), speeding up the computation of WRF double moment 6-class microphysics scheme with GPU (Mielikainen et al, 2013), real-time implementation of the pixel purity index algorithm for end-member identification on GPUs (Wu et al, 2014), fat vs. thin threading approach on GPUs: application to stochastic simulation of chemical reactions (Klingbeil et al, 2012), ASAMgpu V1.0 -a moist fully compressible atmospheric model using GPUs (Horn, 2012), GPU acceleration of predictive partitioned vector quantization for ultra-spectral sounder data compression (Wei, 2011), clusters vs. GPUs for parallel automatic target detection in remotely sensed hyperspectral images (Paz et al, 2010), and a GPUaccelerated wavelet decompression system with set partitioning in hierarchical trees (SPIHT) and Reed-Solomon decoding for satellite images (Song et al, 2011), to name several.…”

Section: Introductionmentioning

confidence: 99%

Development of efficient GPU parallelization of WRF Yonsei University planetary boundary layer scheme

et al. 2015

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Abstract.The planetary boundary layer (PBL) is the lowest part of the atmosphere and where its character is directly affected by its contact with the underlying planetary surface. The PBL is responsible for vertical sub-grid-scale fluxes due to eddy transport in the whole atmospheric column. It determines the flux profiles within the well-mixed boundary layer and the more stable layer above. It thus provides an evolutionary model of atmospheric temperature, moisture (including clouds), and horizontal momentum in the entire atmospheric column. For such purposes, several PBL models have been proposed and employed in the weather research and forecasting (WRF) model of which the Yonsei University (YSU) scheme is one. To expedite weather research and prediction, we have put tremendous effort into developing an accelerated implementation of the entire WRF model using graphics processing unit (GPU) massive parallel computing architecture whilst maintaining its accuracy as compared to its central processing unit (CPU)-based implementation. This paper presents our efficient GPU-based design on a WRF YSU PBL scheme. Using one NVIDIA Tesla K40 GPU, the GPU-based YSU PBL scheme achieves a speedup of 193× with respect to its CPU counterpart running on one CPU core, whereas the speedup for one CPU socket (4 cores) with respect to 1 CPU core is only 3.5×. We can even boost the speedup to 360× with respect to 1 CPU core as two K40 GPUs are applied.

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A GPU-Accelerated Wavelet Decompression System With SPIHT and Reed-Solomon Decoding for Satellite Images

Cited by 52 publications

References 12 publications

Implementation of the DWT in a GPU through a Register-based Strategy

Implementation of the DWT in a GPU through a Register-based Strategy

Performance Study of Satellite Image Processing on Graphics Processors Unit Using CUDA

Development of efficient GPU parallelization of WRF Yonsei University planetary boundary layer scheme

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