Accelerating 2D orthogonal matching pursuit algorithm on GPU

Dai, Yuan; He, Dongjian; Fang, Yong; Yang, Lihong

doi:10.1007/s11227-014-1188-8

Cited by 10 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The complexity for computing the inner product is ( ) and using [24] this complexity is approximated to (log ) . Execution time of the implemented OMP algorithm is evaluated and are compared for both GPU and CPU.…”

Section: Execution Timementioning

confidence: 99%

Sparse Representation Based Multi Object Tracking using GPU

2019

IJITEE

View full text Add to dashboard Cite

This work proposes a sparse based representation for tracking multi object for the longer sequence of video frame. Object of interest are first identified and then represented with set of low dimensional feature covariance matrix. These feature of different object are kept in a dictionary. In order to classify the object, sparse based Orthogonal matching pursuit(OMP) algorithm is used. Furthermore, towards reducing the computational overhead, proposed model is implemented on a graphical processing unit enhanced with the multi threaded resource for parallelization of the task. Experimental results shows that proposed method out perform as compared with the state of art in identifying the objects.

show abstract

Section: Execution Timementioning

confidence: 99%

Sparse Representation Based Multi Object Tracking using GPU

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…For an acoustic emission signal The simulated underwater echo in Figure 3 is then compressed and reconstructed with CS. In this study, the discrete cosine transform [33,34] is used as the sparse matrix, and the reconstruction algorithm adopted is the orthogonal matching pursuit (OMP) [35][36][37]. The measurement matrix based on the cyclic direct product and QR decomposition is compared respectively with Gaussian matrix, Bernoulli matrix, partial Hadamard matrix and Toeplitz matrix.…”

Section: Simulation Of Underwater Echo and Its Compression And Reconsmentioning

confidence: 99%

Construction of Measurement Matrix Based on Cyclic Direct Product and QR Decomposition for Sensing and Reconstruction of Underwater Echo

et al. 2018

View full text Add to dashboard Cite

Compressive sensing is a very attractive technique to detect weak signals in a noisy background, and to overcome limitations from traditional Nyquist sampling. A very important part of this approach is the measurement matrix and how it relates to hardware implementation. However, reconstruction accuracy, resistance to noise and construction time are still open challenges. To address these problems, we propose a measurement matrix based on a cyclic direct product and QR decomposition (the product of an orthogonal matrix Q and an upper triangular matrix R). Using the definition and properties of a direct product, a set of high-dimensional orthogonal column vectors is first established by a finite number of cyclic direct product operations on low-dimension orthogonal “seed” vectors, followed by QR decomposition to yield the orthogonal matrix, whose corresponding rows are selected to form the measurement matrix. We demonstrate this approach with simulations and field measurements of a scaled submarine in a freshwater lake, at frequencies of 40 kHz–80 kHz. The results clearly show the advantage of this method in terms of reconstruction accuracy, signal-to-noise ratio (SNR) enhancement, and construction time, by comparison with Gaussian matrix, Bernoulli matrix, partial Hadamard matrix and Toeplitz matrix. In particular, for weak signals with an SNR less than 0 dB, this method still achieves an SNR increase using less data.

show abstract

“…GPUs are available in almost all current hardware platforms, from standard desktops to computer clusters and thus, provide easily accessible and low-cost parallel hardware to a broad community [11]. For early years, these processors were designed to compute the image displayed on the screen by a given set of functions [4].…”

Section: Gpu and Cuda Architecturementioning

confidence: 99%

Graphics processing unit-accelerated joint-bitplane belief propagation algorithm in DSC

et al. 2016

Self Cite

View full text Add to dashboard Cite

The M-ary source with nonstationary correlation can be encoded with a single binary low-density parity-check (LDPC) code and decoded together in distributed source coding. The joint-bitplane belief propagation (JBBP) is a useful decoding algorithm for multiple bitplanes of an M-ary source. However, it suffers from the drawbacks of low computational efficiency and long execution time. Motivated by the evolution of the Graphics Processing Unit (GPU) and the inherent parallel characteristic of the JBBP, we propose a novel approach for the computationally intensive processing of the JBBP algorithm on GPU using the compute unified device architecture programming model. Two different parallel modes are utilized for the belief passing between different nodes of the JBBP. It is found that the bottlenecks of the JBBP lie in computing the overall probability mass functions (pmfs) of symbol nodes and the overall beliefs of bit nodes. Thus, a data partitioning method is leveraged to split a large array of pmfs into small pieces which can be loaded into L1 cache instead of global memory. The optimal block size is selected which not only assigns as large L1 cache as possible for individual thread, but also guarantees multiple active warps in each stream multiprocessor. Experimental results show that when the length-6336 (length-50,688, resp.) LDPC accumulate (LDPCA) code is used to compress the source, the JBBP decoder can achieve about 20× (41×, resp.) speedup on GPU compared with the original C code on CPU. Better performance would be further obtained with longer LDPCA codes. Moreover, the parallel JBBP is also applied in hyperspectral image compression and video coding and it shows good speedup performance. B Yong Fang

show abstract

Accelerating 2D orthogonal matching pursuit algorithm on GPU

Cited by 10 publications

References 18 publications

Sparse Representation Based Multi Object Tracking using GPU

Sparse Representation Based Multi Object Tracking using GPU

Construction of Measurement Matrix Based on Cyclic Direct Product and QR Decomposition for Sensing and Reconstruction of Underwater Echo

Graphics processing unit-accelerated joint-bitplane belief propagation algorithm in DSC

Contact Info

Product

Resources

About