• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.337-340
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• 2009

Since a hardware accelerator for 3D graphics processing GPU(Graphics Processing Unit)'s performance has been improving constantly. This is the efficient way was introduced for complex graphics application, but it is rarely used to utilize 100% resources on GPU. GP-GPU(general-purpose GPU), including operations on the GPU and supporting common operations can be handled by the processor, is noted by depending on the distribution of resources that can be effectively controlled. In this paper, the simulator was implemented that supports virtual environment of GP-GPU and available for program design and debugging. Through this, the co-design development environment support simultaneous design fast and reliable verification that are available to build the interface of three-dimensional graphics display.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.5
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• pp.459-467
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• 2009

Purpose: The maximum likelihood-expectation maximization (ML-EM) is the statistical reconstruction algorithm derived from probabilistic model of the emission and detection processes. Although the ML-EM has many advantages in accuracy and utility, the use of the ML-EM is limited due to the computational burden of iterating processing on a CPU (central processing unit). In this study, we developed a parallel computing technique on GPU (graphic processing unit) for ML-EM algorithm. Materials and Methods: Using Geforce 9800 GTX+ graphic card and CUDA (compute unified device architecture) the projection and backprojection in ML-EM algorithm were parallelized by NVIDIA's technology. The time delay on computations for projection, errors between measured and estimated data and backprojection in an iteration were measured. Total time included the latency in data transmission between RAM and GPU memory. Results: The total computation time of the CPU- and GPU-based ML-EM with 32 iterations were 3.83 and 0.26 see, respectively. In this case, the computing speed was improved about 15 times on GPU. When the number of iterations increased into 1024, the CPU- and GPU-based computing took totally 18 min and 8 see, respectively. The improvement was about 135 times and was caused by delay on CPU-based computing after certain iterations. On the other hand, the GPU-based computation provided very small variation on time delay per iteration due to use of shared memory. Conclusion: The GPU-based parallel computation for ML-EM improved significantly the computing speed and stability. The developed GPU-based ML-EM algorithm could be easily modified for some other imaging geometries.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2355-2362
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• 2016

Seg fog removal is an important issue concerned by both computer vision and image processing. Sea fog or haze removal is widely used in lots of fields, such as automatic control system, CCTV, and image recognition. Color image dehazing techniques have been extensively studied, and expecially the dark channel prior(DCP) technique has been widely used. This paper propose a fast and efficient image prior - dark channel prior to remove seg-fog from a single digital image based on the GPU. We implement the basic parallel program and then optimize it to obtain performance acceleration with more than 250 times. While paralleling and the optimizing the algorithm, we improve some parts of the original serial program or basic parallel program according to the characteristics of several steps. The proposed GPU programming algorithm and implementation results may be used with advantages as pre-processing in many systems, such as safe navigation for ship, topographical survey, intelligent vehicles, etc.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.48-51
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• 2019

최근 기술이 발달함으로 인해 더 짧은시간에 더 많은 계산량이 필요해진 시대가 왔다. 본 연구에서는 CPU와 GPU의 구조를 파악하고 계산속도를 비교한다. 직렬 방식의 알고리즘에서의 병렬 방식의 알고리즘 및 현재 GPU 병렬처리 적용 사례 및 추후 적합한 모델 찾기에 대해 연구한다.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.194-198
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• 2011

In this study, an efficient algorithm for Delaunay triangulation of a number of points which can be used on a GPU-based parallel computation is studied The developed algorithm is programmed using CUDA library. and the program takes full advantage of parallel computation which are concurrently performed on each of the threads on GPU. The results of partitioned triangulation collected from the GPU computation requires proper stitching between neighboring partitions and calculation of connectivities among triangular cells on CPU In this study, the effect of number of threads on the efficiency and total duration for Delaunay grid generation is studied. And it is also shown that GPU computing using CUDA for Delaunay grid generation is feasible and it saves total time required for the triangulation of the large number points compared to the sequential CPU-based triangulation programs.

• Geophysics and Geophysical Exploration
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• v.20 no.2
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• pp.72-77
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• 2017

We calculated 3D frequency- and Laplace-domain wavefields using time-domain modeling and Fourier transform or Laplace transform. We adopted OpenACC and GPU for an efficient parallel calculation. The OpenACC makes it easy to use GPU accelerators by adding directives in conventional C, C++, and Fortran programming languages. Accordingly, one doesn't have to learn new GPGPU programming languages such as CUDA or OpenCL to use GPU. An OpenACC program allocates GPU memory, transfers data between the host CPU and GPU devices and performs GPU operations automatically or following user-defined directives. We compared performance of 3D wave propagation modeling programs using OpenACC and GPU to that using single-core CPU through numerical tests. Results using a homogeneous model and the SEG/EAGE salt model show that the OpenACC programs are approximately 53 and 30 times faster than those using single-core CPU.

• Atmosphere
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• v.23 no.2
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• pp.231-235
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• 2013

We parallelized WRF major physics routines for Nvidia GP-GPUs with CUDA Fortran. GP-GPUs are originally designed for graphic processing, but show high performance with low electricity for calculating numerical models. In the CUDA environment, a data domain is allocated into thread blocks and threads in each thread block are computing in parallel. We parallelized the WRF program to use of thread blocks efficiently. We validated the GP-GPU program with the original CPU program, and the WRF model using GP-GPUs shows efficient speedup.

• Tribology and Lubricants
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• v.28 no.4
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• pp.160-166
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• 2012

This paper presents a Reynolds equation solver for hydrostatic gas bearings, implemented to run on graphics processing units (GPUs). The original analysis code for the central processing unit (CPU) was modified for the GPU by using the compute unified device architecture (CUDA). The red-black Gauss-Seidel (RBGS) algorithm was employed instead of the original Gauss-Seidel algorithm for the iterative pressure solver, because the latter has data dependency between neighboring nodes. The implemented GPU program was tested on the nVidia GTX580 system and compared to the original CPU program on the AMD Llano system. In the iterative pressure calculation, the implemented GPU program showed 20-100 times faster performance than the original CPU codes. Comparison of the wall-clock times including all of pre/post processing codes showed that the GPU codes still delivered 4-12 times faster performance than the CPU code for our target problem.

• Journal of computational fluids engineering
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• v.19 no.3
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• pp.52-61
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• 2014

The present paper deals with the efficient computation of higher-order CFD methods for compressible flow using graphics processing units (GPU). The higher-order CFD methods, such as discontinuous Galerkin (DG) methods and correction procedure via reconstruction (CPR) methods, can realize arbitrary higher-order accuracy with compact stencil on unstructured mesh. However, they require much more computational costs compared to the widely used finite volume methods (FVM). Graphics processing unit, consisting of hundreds or thousands small cores, is apt to massive parallel computations of compressible flow based on the higher-order CFD methods and can reduce computational time greatly. Higher-order multi-dimensional limiting process (MLP) is applied for the robust control of numerical oscillations around shock discontinuity and implemented efficiently on GPU. The program is written and optimized in CUDA library offered from NVIDIA. The whole algorithms are implemented to guarantee accurate and efficient computations for parallel programming on shared-memory model of GPU. The extensive numerical experiments validates that the GPU successfully accelerates computing compressible flow using higher-order method.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1003-1006
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• 2005

Vertex shader of GPU in personal computer is advanced in functions as to be half of traditional fixed T&L functions. And, capacity of memory for saving resources to process instructions is unlimited. GPU that can be programmed by programmer is needed for mobile system as well as personal computer. In this paper, we implement software virtual machine for vertex shader using C++ Language. Our goal is designing hardware GPU that can apply to mobile system. The virtual machine consists of nVidia GPU instructions. Input Data to virtual machine is generated by Microsoft fxc compiler. That is to say, Input Data is compiled shader program written in HLSL, Cg, or ASM. The virtual machine will be a reference model for designing hardware GPU and can be used for Testbed to test added or modified instruction.