• Title, Summary, Keyword: GPGPU(General Purpose Computing on GPU)

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Implememtation of Fast Rasterizer processing using GPGPU based on SIMT structure (SIMT 구조 기반 GPGPU를 이용한 고속 Rasterizer 구현)

  • Kim, Chiyong
    • Journal of IKEEE
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    • v.21 no.3
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    • pp.276-279
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    • 2017
  • In this paper, SIMT structure based GPGPU (General Purpose Computing on Graphics Processing Units) is used for accelerating the Rasterizer which constitutes the screen of the display device in pixel unit. The GPU has a large number of ALUs, and the processing is very fast because of parallel processing. Therefore, in this paper, we implemented a rasterizer that generates a 3D graphics model using a CPU that performs operations sequentially and a GPU that performs operations in parallel. We confirmed that proposed rasterizer in this paper is 1.45 times better than rasterizer using Intel CPU when generating one frame.

Research of accelerating method of video quality measurement program using GPGPU (GPGPU를 이용한 영상 품질 측정 프로그램의 가속화 연구)

  • Lee, Seonguk;Byeon, Gibeom;Kim, Kisu;Hong, Jiman
    • Smart Media Journal
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    • v.5 no.4
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    • pp.69-74
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    • 2016
  • Recently, parallel computing using GPGPU(General-Purpose computing on Graphics Processing Units) according to the development of the graphics processing unit is expanding. This can be achieved through the processing speeds faster than traditional computing environments across many fields, including science, medicine, engineering, and analysis. However, in using the GPU technology to implement the a parallel program there are many constraints. In this paper, we port a CPU-based program(Video Quality Measurement Program) to use technology. The program ported to GPU-based show about 1.83 times the execution speed than CPU-based program. We study on the acceleration of the GPU-based program. Also we discuss the technical constraints and problems that occur when you modify the CPU to the GPU-based programs.

Spark Framework Based on a Heterogenous Pipeline Computing with OpenCL (OpenCL을 활용한 이기종 파이프라인 컴퓨팅 기반 Spark 프레임워크)

  • Kim, Daehee;Park, Neungsoo
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.67 no.2
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    • pp.270-276
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    • 2018
  • Apache Spark is one of the high performance in-memory computing frameworks for big-data processing. Recently, to improve the performance, general-purpose computing on graphics processing unit(GPGPU) is adapted to Apache Spark framework. Previous Spark-GPGPU frameworks focus on overcoming the difficulty of an implementation resulting from the difference between the computation environment of GPGPU and Spark framework. In this paper, we propose a Spark framework based on a heterogenous pipeline computing with OpenCL to further improve the performance. The proposed framework overlaps the Java-to-Native memory copies of CPU with CPU-GPU communications(DMA) and GPU kernel computations to hide the CPU idle time. Also, CPU-GPU communication buffers are implemented with switching dual buffers, which reduce the mapped memory region resulting in decreasing memory mapping overhead. Experimental results showed that the proposed Spark framework based on a heterogenous pipeline computing with OpenCL had up to 2.13 times faster than the previous Spark framework using OpenCL.

Analysis on the Active/Inactive Status of Computational Resources for Improving the Performance of the GPU (GPU 성능 저하 해결을 위한 내부 자원 활용/비활용 상태 분석)

  • Choi, Hongjun;Son, Dongoh;Kim, Jongmyon;Kim, Cheolhong
    • The Journal of the Korea Contents Association
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    • v.15 no.7
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    • pp.1-11
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    • 2015
  • In recent high performance computing system, GPGPU has been widely used to process general-purpose applications as well as graphics applications, since GPU can provide optimized computational resources for massive parallel processing. Unfortunately, GPGPU doesn't exploit computational resources on GPU in executing general-purpose applications fully, because the applications cannot be optimized to GPU architecture. Therefore, we provide GPU research guideline to improve the performance of computing systems using GPGPU. To accomplish this, we analyze the negative factors on GPU performance. In this paper, in order to clearly classify the cause of the negative factors on GPU performance, GPU core status are defined into 5 status: fully active status, partial active status, idle status, memory stall status and GPU core stall status. All status except fully active status cause performance degradation. We evaluate the ratio of each GPU core status depending on the characteristics of benchmarks to find specific reasons which degrade the performance of GPU. According to our simulation results, partial active status, idle status, memory stall status and GPU core stall status are induced by computational resource underutilization problem, low parallelism, high memory requests, and structural hazard, respectively.

Implementation of IQ/IDCT in H.264/AVC Decoder Using Mobile Multi-Core GPGPU (모바일 멀티 코어 GP-GPU를 이용한 H.264/AVC 디코더 구현)

  • Kim, Dong-Han;Lee, Kwang-Yeob;Jeong, Jun-Mo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.321-324
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    • 2010
  • There have been lots of researches on a multi-core processor. The enhancement has been performed through parallelization method. Multi-core architecture in the mobile environment has emerged. But, there is a limit to a mobile CPU's performance. GP-GPU(General-Purpose computing on Graphics Processing Units) can improve performance without adding other dedicated hardware. This paper presents the implementation of Inverse Quantization, Inverse DCT and Color Space Conversion module in H.264/AVC decoder using Multi-Core GP-GPU for a mobile environments. The proposed architecture improves approximately 50% of performance when it use all the features.

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스마트폰에서의 영상처리를 위한 GPU 활용

  • Park, In-Gyu;Choe, Ho-Yeol
    • Information and Communications Magazine
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    • v.29 no.4
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    • pp.46-51
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    • 2012
  • 본 기고에서는 최근 스마트폰에서 요구되는 다양한 멀티미디어 어플리케이션을 embedded GPU(Graphics Processing Unit)를 이용하여 고속 병렬처리하기 위한 GPGPU (General-Purpose Computing on GPU) 기술 및 영상처리 분야의 응용 사례를 소개한다. 일반적인 데스크탑 컴퓨팅 환경과 달리 제약사항이 많은 embedded 환경에서의 GPGPU 응용 기술은 아직 초기단계이다. 그러나 급격히 발전하는 embedded GPU IP와 OpenCL과 같은 API의 등장으로 embedded GPU를 이용한 고속 병렬처리 환경이 수 년 이내에 일반화 될 것이다. 본 기고에서는 그 가능성을 점검하기 위하여 embedded GPU에서의 영상처리를 위한 최신 하드웨어와 소프트웨어 환경의 발전 동향을 소개한다. 더불어 최신 스마트폰에서의 GPGPU기술을 사용한 영상처리 사례와 영상처리 알고리즘의 GPGPU 알고리즘 구현시 고려해야 할 주요 사항을 정리한다.

Analysis on Memory Characteristics of Graphics Processing Units for Designing Memory System of General-Purpose Computing on Graphics Processing Units (범용 그래픽 처리 장치의 메모리 설계를 위한 그래픽 처리 장치의 메모리 특성 분석)

  • Choi, Hongjun;Kim, Cheolhong
    • Smart Media Journal
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    • v.3 no.1
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    • pp.33-38
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    • 2014
  • Even though the performance of microprocessor is improved continuously, the performance improvement of computing system becomes hard to increase, in order to some drawbacks including increased power consumption. To solve the problem, general-purpose computing on graphics processing units(GPGPUs), which execute general-purpose applications by using specialized parallel-processing device representing graphics processing units(GPUs), have been focused. However, the characteristics of applications related with graphics is substantially different from the characteristics of general-purpose applications. Therefore, GPUs cannot exploit the outstanding computational resources sufficiently due to various constraints, when they execute general-purpose applications. When designing GPUs for GPGPU, memory system is important to effectively exploit the GPUs since typically general-purpose applications requires more memory accesses than graphics applications. Especially, external memory access requiring long latency impose a big overhead on the performance of GPUs. Therefore, the GPU performance must be improved if hierarchical memory architecture which can reduce the number of external memory access is applied. For this reason, we will investigate the analysis of GPU performance according to hierarchical cache architectures in executing various benchmarks.

A Study of How to Improve Execution Speed of Grabcut Using GPGPU (GPGPU를 이용한 Grabcut의 수행 속도 개선 방법에 관한 연구)

  • Kim, Ji-Hoon;Park, Young-Soo;Lee, Sang-Hun
    • Journal of Digital Convergence
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    • v.12 no.11
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    • pp.379-386
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    • 2014
  • In this paper, the processing speed of Grabcut algorithm in order to efficiently improve the GPU (Graphics Processing Unit) for processing the data from the method. Grabcut algorithm has excellent performance object detection algorithm. Grabcut existing algorithms to split the foreground area and the background area, and then background and foreground K-cluster is assigned a cluster. And assigned to gradually improve the results, until the process is repeated. But Drawback of Grabcut algorithm is the time consumption caused by the repetition of clustering. Thus GPGPU (General-Purpose computing on Graphics Processing Unit) using the repeated operations in parallel by processing Grabcut algorithm to effectively improve the processing speed of the method. We proposed method of execution time of the algorithm reduced the average of about 95.58%.

Study of Cache Performance on GPGPU

  • Choi, Kyu Hyun;Kim, Seon Wook
    • IEIE Transactions on Smart Processing and Computing
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    • v.4 no.2
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    • pp.78-82
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    • 2015
  • General-purpose graphics processing units (GPGPUs) provide tremendous computational and processing power. Despite the latency hiding mechanism, a GPU architecture requires high memory bandwidth and lower latency between computational units and the memory system. For this reason, the current GPU architecture has private L1 caches in each core and a shared L2 cache to increase performance by reducing memory latency. But in some cases, this CPU-like cache design is not suitable for GPGPUs. In this paper, we analyze detailed cache performance related to GPGPU application characteristics, and suggest technical alternatives for the GPGPU architecture as future work.

Performance Improvement in Observation Probability Computation of Gaussian Mixture Models Using GPGPU (GPGPU를 이용한 가우시안 혼합 모델의 관측확률 계산 성능 향상)

  • Kim, Hyeong-Ju;Kim, Seung-Hi;Kim, Sanghun;Jang, Gil-Jin
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.148-151
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    • 2012
  • 범용 GPU (general-purpose computing on graphics processing units, GPGPU)는 GPU를 일반적인 목적으로 사용하고자 하는 병렬 컴퓨터 구조로써, 과학 연산 등 여러 분야에서 응용 프로그램의 성능을 향상시키기 위하여 사용되고 있다. 본 연구에서는 음성인식기에서 주로 사용되는 가우시안 혼합 모델(Gaussian mixture model, GMM)에서 많은 연산시간을 차지하는 관측확률 계산의 성능을 향상시키고자 GPGPU를 이용하는 알고리즘을 구현하였으며, 기존 CPU 기반 알고리즘 대비 약 13배 연산시간을 단축하였다.

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