• Journal of KIISE:Computer Systems and Theory
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• v.34 no.4
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• pp.145-157
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• 2007

OpenMP is an attractive parallel programming model for a chip multiprocessor because there is no standard parallel programming method for a chip multiprocessor and it is easy to write a parallel program in OpenMP. Then, chip multiprocessor systems can have various architectures according to target application programs. So, we need to implement OpenMP in different way for each system. In this paper, we propose the implementation and the effective translation of major OpenMP directives for a chip multiprocessor without using OS to improve the performance without using special hardware and without extending the OpenMP directives. We present the experimental results on our target platform CT3400.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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• pp.414-418
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• 2007

멀티프로세서 환경에서 OpenMP는 MPI 에 비해 병렬 프로그래밍을 쉽게 할 수 있다는 장점을 가지고 있고, OpenMP는 표준이 없는 병렬 프로그래밍 세계에서 실질적인 표준으로써 인정받고 있다. OPenMP는 대상 플랫폼에 따라 OpenMP 구현을 다르게 해야 하기 때문에 새로운 프로세서가 등장하면 그에 맞는 OpenMP구현을 만들어야 한다. 이 논문에선 다중 프로세서 시스템-온-칩 시스템인 ARM11MPCore 시스템 위에 POSIX 쓰레드에 기반하여 OpenMP 환경을 구축하고 그 성능을 측정한다.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.253-260
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• 2009

Many algorithms for computer vision and pattern recognition have recently been implemented on GPU (graphic processing unit) for faster computational times. However, the implementation has two problems. First, the programmer should master the fundamentals of the graphics shading languages that require the prior knowledge on computer graphics. Second, in a job that needs much cooperation between CPU and GPU, which is usual in image processing and pattern recognition contrary to the graphic area, CPU should generate raw feature data for GPU processing as much as possible to effectively utilize GPU performance. This paper proposes more quick and efficient implementation of neural networks on both GPU and multi-core CPU. We use CUDA (compute unified device architecture) that can be easily programmed due to its simple C language-like style instead of GPU to solve the first problem. Moreover, OpenMP (Open Multi-Processing) is used to concurrently process multiple data with single instruction on multi-core CPU, which results in effectively utilizing the memories of GPU. In the experiments, we implemented neural networks-based text extraction system using the proposed architecture, and the computational times showed about 15 times faster than implementation on only GPU without OpenMP.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.289-290
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• 2008

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.11-17
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• 2008

The net shape effects by the various vectors in underwater. Each particle of the net calculating the effect of all vectors augments an accuracy and reality. But, the time complexity becomes larger because of huge calculation. The previous techniques reduced a physics reality. And embodied the underwater virtual reality which augments visual reality with simulation. In this paper, parallel processing the particles, it embodied the simulation which is satisfied a physical reality and time reality. The parallel processing used the OpenMP, and the reality graphic expression used the OpenGL. The simulation which this paper Proposes will be the possibility becoming the fundamental data for a model analysis or a specialist system from game and marine field.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.328-330
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• 2011

본 논문에서는 OpenMP를 이용하여 HEVC 복호화기의 디블록킹 필터를 병렬화하는 것을 제안한다. 본 논문에서는 HEVC 디블록킹 필터를 병렬화하기 위하여 슬라이스를 병렬 처리가 가능한 코어의 개수만큼의 영역으로 균등하게 분할 한 후 각 영역에 코어를 할당하였다. 각 영역에 할당된 코어들은 자신의 영역 내의 LCU에 대해서 순차 주사 순으로 필터링을 수행하는데, 먼저 영역 내의 모든 LCU에 대하여 수평방향으로 필터링을 수행한다. 이러한 수평방향 필터링이 완료된 후 동일한 영역에 대하여 다시 수직 방향으로 필터링을 수행한다. 본 논문에서 제안하는 OpenMP를 이용한 HEVC 디블록킹 필터 병렬화를 통하여 4-Core 환경에서 복호화기에서 디블록킹 필터링의 수행 시간을 약 2.51배 감소 시켰다.

• Journal of IKEEE
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• v.17 no.3
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• pp.371-377
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• 2013

This thesis propose an efficient parallel structure method for implementing a FAST and BRIEF algorithm based Augmented Reality. SURF algorithm that is well known in the object recognition algorithms is robust in object recognition. However, there is a disadvantage for real time operation because, SURF implementation requires a lot of computation. Therefore, we used a FAST and BRIEF algorithm for object recognition, and we improved Conventional Parallel Structure based on OpenMP Library. As a result, it achieves a 70%~100% improvement in execution time on the embedded system.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.59-60
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• 2015

반도체 공정에서는 소자 내부의 물리량 계산을 통해 결점를 검출하는 시뮬레이션을 수행하게 된다. 이를 위해 3차원적 형상을 표현하여 물리적 미분 방정식을 계산하는 유한 차분 시간 영역 알고리즘(Finite-Difference Time-Domain, 이하 FDTD)과 같은 수치해석 기법이 사용된다. 반도체 테스트 범위의 확장으로 인해 시뮬레이션 사이즈 또한 커지고 있는 추세이다. 이에 따라 하나의 프로세서에서 수행할 수 없는 문제가 발생하기도 한다. 이를 해결하기 위해 본 논문에서는 openMP와 MPI를 이용한 하이브리드 컴퓨팅 기법을 바탕으로 다중 GPU 제어를 통해 시뮬레이션 환경을 구축하였으며 정상 동작함을 확인하였다.

• KIISE Transactions on Computing Practices
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• v.22 no.7
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• pp.303-309
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• 2016

Recently, an increasing number of embedded processors such as ARM Mali begin to support GPGPU programming frameworks, such as OpenCL. Thus, GPGPU technologies that have been used in PC and server environments are beginning to be applied to the embedded systems. However, many embedded systems have different architectural characteristics compare to traditional PCs and low-power consumption and real-time performance are also important performance metrics in these systems. In this paper, we implement a parallel AES cryptographic algorithm for a modern embedded GPU using OpenCL, a standard parallel computing framework, and compare performance against various baselines. Experimental results show that the parallel GPU AES implementation can reduce the response time by about 1/150 and the energy consumption by approximately 1/290 compare to OpenMP implementation when 1000KB input data is applied. Furthermore, an additional 100 % performance improvement of the parallel AES algorithm was achieved by exploiting the characteristics of embedded GPUs such as removing copying data between GPU and host memory. Our results also demonstrate that higher performance improvement can be achieved with larger size of input data.

• Computers and Concrete
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• v.2 no.5
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• pp.345-366
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• 2005

This work presents an assessment of the computational performance of a vector-parallel implementation of probabilistic model for concrete cracking in 3D. This paper shows the continuing efforts towards code optimization as reported in earlier works Paz, et al. (2002a,b and 2003). The probabilistic crack approach is based on the direct Monte Carlo method. Cracking is accounted by means of 3D interface elements. This approach considers that all nonlinearities are restricted to interface elements modeling cracks. The heterogeneity governs the overall cracking behavior and related size effects on concrete fracture. Computational kernels in the implementation are the inexact Newton iterative driver to solve the non-linear problem and a preconditioned conjugate gradient (PCG) driver to solve linearized equations, using an element by element (EBE) strategy to compute matrix-vector products. In particular the paper analyzes code behavior using OpenMP directives in parallel vector processors (PVP), such as the CRAY SV1 and CRAY T94. The impact of the memory architecture on code performance, and also some strategies devised to circumvent this issue are addressed by numerical experiment.