Introduction to OpenCL

Gaster, Benedict R.; Howes, Lee; Kaeli, David; Mistry, Perhaad; Schaa, Dana

doi:10.1016/b978-0-12-387766-6.00025-6

Cited by 23 publications

(23 citation statements)

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“…More recently, to reduce the coding difficulty, general APIs (i.e. Compute Unified Device Architecture (CUDA) [38], Open Computing Language (OpenCL) [39]) were developed. As a result, researchers and programmers only need to focus on the algorithm design instead of considering how to map their algorithms to graphic operation semantics.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

GPU-Based Branchless Distance-Driven Projection and Backprojection

Man

et al. 2017

IEEE Trans. Comput. Imaging

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Projection and backprojection operations are essential in a variety of image reconstruction and physical correction algorithms in CT. The distance-driven (DD) projection and backprojection are widely used for their highly sequential memory access pattern and low arithmetic cost. However, a typical DD implementation has an inner loop that adjusts the calculation depending on the relative position between voxel and detector cell boundaries. The irregularity of the branch behavior makes it inefficient to be implemented on massively parallel computing devices such as graphics processing units (GPUs). Such irregular branch behaviors can be eliminated by factorizing the DD operation as three branchless steps: integration, linear interpolation, and differentiation, all of which are highly amenable to massive vectorization. In this paper, we implement and evaluate a highly parallel branchless DD algorithm for 3D cone beam CT. The algorithm utilizes the texture memory and hardware interpolation on GPUs to achieve fast computational speed. The developed branchless DD algorithm achieved 137-fold speedup for forward projection and 188-fold speedup for backprojection relative to a single-thread CPU implementation. Compared with a state-of-the-art 32-thread CPU implementation, the proposed branchless DD achieved 8-fold acceleration for forward projection and 10-fold acceleration for backprojection. GPU based branchless DD method was evaluated by iterative reconstruction algorithms with both simulation and real datasets. It obtained visually identical images as the CPU reference algorithm.

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Section: Related Work and Backgroundmentioning

confidence: 99%

GPU-Based Branchless Distance-Driven Projection and Backprojection

Man

et al. 2017

IEEE Trans. Comput. Imaging

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“…2 illustrates the complete work flow for rock evaluation with digital imaging and GPU parallel computing. For details on GPU programming, refer to Gaster et al (2012).…”

Section: Gpu Parallel Computingmentioning

confidence: 99%

Optimization of Lattice Boltzmann Simulation With Graphics-Processing-Unit Parallel Computing and the Application in Reservoir Characterization

et al. 2016

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Shale permeability is sufficiently low to require an unconventional scale of stimulation treatments, such as very-large-volume, high-rate, multistage hydraulic-fracturing applications. Upscaling of hydrocarbon transport processes in shales is challenging because of the low permeability and strong heterogeneity. Rock characterization with high-resolution imaging [X-ray tomography and scanning electron microscope (SEM)] is usually highly localized and contains significant uncertainties because of the small field of view. Therefore, an effective high-performance computing method is required to collect information over a larger scale to meet the ergodicity requirement in upscaling. The lattice Boltzmann (LB) method has received significant attention in computational fluid dynamics because of its capability in coping with complicated boundary conditions. A combination of high-resolution imaging and LB simulation is a powerful approach for evaluating the transport properties of a porous medium in a timely manner, on the basis of the numerical solution of the Navier-Stokes equations and Darcy's law. In this work, a graphics-processing-unit (GPU) -enhanced lattice Boltzmann simulator (GELBS) was developed, which was optimized by GPU parallel computing on the basis of the inherent parallelism of the LB method. Specifically, the LB method was used to implement the computational kernel; a sparse data structure was applied to optimize memory allocation; the OCCA (Medina et al. 2014) portability library was used, which enables the GELBS codes to use different application-programming interfaces (APIs) including open computing language (OpenCL), compute unified device architecture (CUDA), and open multiprocessing (OpenMP).OpenCL is an open standard for cross-platform parallel computing, CUDA is supported only by NVIDIA devices, and OpenMP is primarily used on central processing units (CPUs). It was found that the GPU-accelerated code was approximately 1,000 times faster than the unoptimized serial code and 10 times faster than the parallel code run on a standalone CPU. The CUDA code was slightly faster than OpenCL code on the NVIDA GPU because of the extra cost of OpenCL used to adapt to a heterogeneous platform. The GELBS was validated by comparing it with analytical solutions, laboratory measurements, and other independent numerical simulators in previous studies, and it was proved to have a second-order global accuracy. The GELBS was then used to analyze thin cuttings extracted from a sandstone reservoir and a shale-gas reservoir. The sandstone permeabilities were found relatively isotropic, whereas the shale permeabilities were strongly anisotropic because of the horizontal lamination structure. In shale cuttings, the average permeability in the horizontal direction was higher than that in the vertical direction by approximately two orders of magnitude. Correlations between porosity and permeability were observed in both rocks. The combination of GELBS and high-resolution imaging methods makes for a power-ful tool for perme...

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“…The programming models for applications using mobile GPUs include OpenGL ES (for embedded system) [21], Computer Unified Device Architecture (CUDA) [16], and Open Computing Language (OpenCL) [12]. OpenGL ES was designed mainly for the graphic rendering targets.…”

Section: Introductionmentioning

confidence: 99%

“…1. And more details about implementing heterogeneous applications in OpenCL can be referred to the book [12].…”

Section: Introductionmentioning

confidence: 99%

An energy efficient OpenCL implementation of a fingerprint verification system on heterogeneous mobile device

Wen

Meng

et al. 2014

2014 IEEE 20th International Conference on Embedded and Real-Time Computing Systems and Applications

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With the increasing concerns over the personal privacy of mobile devices, biometrics algorithms plays an important role to enhance the security. As one of the most popular approaches, fingerprint verification as a personal identification interface is widely recognized and adopted by many commercial devices. However, its inherent computational complexity make the algorithm of fingerprint verification difficult to achieve high performance on mobile platforms, such a battery powered, size limited, and producing cost controlled device. In addition to the performance, energy efficiency is also of significant consideration of such a fingerprint verification system. In this paper, we present an energy efficient OpenCL based heterogeneous implementation of the fingerprint verification system on a commercial mobile platform, taking advantage of mobile CPUs and GPUs. We carefully analyze the workloads through system profiling to identify the parallelism then to partition the algorithm between the CPU and GPU . The experimental results show that our GPU implementation of DFT analysis achieves a 1.4X speedup and 36.87% energy reduction compared to the CPU only implementation in the mobile platform. This heterogenous implementation of the entire fingerprint verification system accomplishes 1.32X speedup and 16.70% energy superiority above the CPU only solution. To the best of the authors' knowledge, this work is the first published implementation of OpenCL based fingerprint verification system accelerated by mobile GPUs on a heterogeneous mobile device. We believe our mapping methodology of this fingerprint verification system can be generalized to map more similar applications onto heterogeneous mobile devices.

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Introduction to OpenCL

Cited by 23 publications

References 1 publication

GPU-Based Branchless Distance-Driven Projection and Backprojection

GPU-Based Branchless Distance-Driven Projection and Backprojection

Optimization of Lattice Boltzmann Simulation With Graphics-Processing-Unit Parallel Computing and the Application in Reservoir Characterization

An energy efficient OpenCL implementation of a fingerprint verification system on heterogeneous mobile device

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