A performance study of general-purpose applications on graphics processors using CUDA

Che, Shuai; Boyer, Michael; Meng, Jiayuan; Tarjan, David; Sheaffer, Jeremy W.; Skadron, Kevin

doi:10.1016/j.jpdc.2008.05.014

Cited by 477 publications

(255 citation statements)

References 13 publications

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“…The use of GPUs for general purpose computing (GPGPU) was, despite this fact, already popular five years ago (Owens et al, 2007). The release of the compute unified device architecture (CUDA) programming language made using Nvidia GPUs for more general purpose calculations much easier Garland et al, 2008;Che et al, 2008). The combination of a higher theoretical performance and an easy programming language has led to many reports of large computational gains compared to optimized CPU implementations, though some of these speedups have been questioned (Lee et al, 2010).…”

Section: Gpus For General Purpose Parallel Computingmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

342

191

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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Section: Gpus For General Purpose Parallel Computingmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

342

191

View full text Add to dashboard Cite

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“…Second, multi-core CPUs are able to solve high-performance applications more efficiently by using parallel computing [9]. Third, GPUs have gained an important role in the area of parallel computing [5,34]. In particular, the Compute Unified Device Architecture (CUDA) [33] is a parallel computing architecture developed by NVIDIA.…”

Section: Parallelization Approachesmentioning

confidence: 99%

Parallelization strategies for markerless human motion capture

Cano

Yeguas-Bolivar

Muñoz-Salinas

et al. 2014

J Real-Time Image Proc

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Markerless Motion Capture (MMOCAP) is the problem of determining the pose of a person from images captured by one or several cameras simultaneously without using markers on the subject. Evaluation of the solutions is frequently the most time-consuming task, making most of the proposed methods inapplicable in real-time scenarios. This paper presents an efficient approach to parallelize the evaluation of the solutions in CPUs and GPUs. Our proposal is experimentally compared on six sequences of the HumanEva-I dataset using the CMAES algorithm. Multiple algorithm's configurations were tested to analyze the best trade-off in regard to the accuracy and computing time. The proposed methods obtain speedups of 8× in multi-core CPUs, 30× in a single GPU and up to 110× using 4 GPUs.

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“…The application domain ranges from physics to finance and the medical field. The work in [11] gives a performance study of general purpose applications using CUDA and compares them with that of applications written using OpenMP.…”

Section: Related Workmentioning

confidence: 99%

Speeding Up Cycle Based Logic Simulation Using Graphics Processing Units

Şen

Akşanlı

Bozkurt

2011

Int J Parallel Prog

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Verification has grown to dominate the cost of electronic system design, consuming about 60% of design effort. Among several verification techniques, logic simulation remains the major verification technique. Speeding up logic simulation results in great savings and shorter time-to-market. We parallelize logic simulation using Graphics Processing Units (GPUs). In the past, GPUs were special-purpose application accelerators, suitable only for conventional graphics applications. The new generations of GPU architecture provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power of traditional GPUs. We develop a parallel cycle-based logic simulation algorithm that uses And Inverter Graphs (AIGs) as design representations. AIGs have proven to be an effective representation for various design automation applications, and we obtain similar benefits for speeding up logic simulation. We develop two clustering algorithms that partition the gates in the designs into independent blocks. Our algorithms exploit the massively parallel GPU architecture featuring thousands of concurrent threads, fast memory, and memory coalescing for optimizations. We demonstrate up-to 5x and 21x speedups on several benchmarks using our simulation system with the first and second clustering algorithms, respectively. Our work ultimately results in significant reduction in the overall design cycle.

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A performance study of general-purpose applications on graphics processors using CUDA

Cited by 477 publications

References 13 publications

Medical image processing on the GPU – Past, present and future

Medical image processing on the GPU – Past, present and future

Parallelization strategies for markerless human motion capture

Speeding Up Cycle Based Logic Simulation Using Graphics Processing Units

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