Enablement and verification of GPGPU

doi:10.3778/j.issn.1002-8331.2009.33.021

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (33): 67-69.DOI: 10.3778/j.issn.1002-8331.2009.33.021

• 研发、设计、测试 • Previous Articles Next Articles

Enablement and verification of GPGPU

QI Ji¹，YANG Kong-qing¹，YANG Lei²

1.Center for Complex System College of Science，Jimei University，Xiamen，Fujian 361021，China
2.Institute of Modern Physics，Chinese Academy of Sciences，Lanzhou 730000，China

Received:2008-09-19 Revised:2008-12-23 Online:2009-11-21 Published:2009-11-21
Contact: QI Ji

图形处理器通用计算的实现与验证

齐记¹，杨孔庆¹，杨磊²

1.集美大学理学院复杂系统研究中心，福建厦门 361021
2.中国科学院近代物理研究所，兰州 730000

通讯作者: 齐记

Abstract

Abstract: In this paper，the general purpose computing on Graphics Processing Unit（GPU） is discussed，and the fundamental operations of large scale matrice by GPU are studied in detail to compare the difference between the calculation of CPU and GPU.In the fundamental operations of large scale matrice，computing speed of GPU can be 50 times faster than CPU on the suitable matrix blocking.Moreover，the cheapness of the graphic card makes it possible that more scientific workers can do large-sized calculation.

Key words: Graphics Processing Unit（GPU）, General Purpose Graphic Process Unit（GPGPU）, Central Processing Unit（CPU）

摘要： 讨论了显示卡用于通用科学计算的问题，并以大型矩阵的基本运算问题详细比较了CPU和GPU计算之间的差别。在基本的矩阵运算中，运用适当的矩阵分块，GPU的计算速度比CPU快50倍左右。而且，显示卡低廉的价格为更多科研工作者实现大规模运算提供了可能。

关键词: 图形处理器（GPU）, 图形处理器通用计算技术（GPGPU）, 中央处理器（CPU）

CLC Number:

TP303

QI Ji¹，YANG Kong-qing¹，YANG Lei². Enablement and verification of GPGPU[J]. Computer Engineering and Applications, 2009, 45(33): 67-69.

齐记¹，杨孔庆¹，杨磊². 图形处理器通用计算的实现与验证[J]. 计算机工程与应用, 2009, 45(33): 67-69.

[1]	YANG Jie, WU Suping. Parallel Algorithm for Point Cloud Reconstruction [J]. Computer Engineering and Applications, 2020, 56(6): 213-219.
[2]	ZHU Chao, WU Suping. Feature Point Detection DoG Parallel Algorithm [J]. Computer Engineering and Applications, 2020, 56(10): 36-43.
[3]	ZHANG Bin, ZHANG Zhengqiang, WANG Hongkai. Research on Cone-Beam CT Reconstruction Algorithm Based on GPU Acceleration [J]. Computer Engineering and Applications, 2019, 55(4): 208-213.
[4]	QIN Jinbo1, ZENG Zhiqiang1，2, LIANG Ji1, YANG Mingxiang2, ZHANG Jian1. Review of application GPU technology in hydraulic parallel optimization calculation [J]. Computer Engineering and Applications, 2018, 54(3): 23-29.
[5]	CHENG Huan. Method of marine radar echo simulation based on DEM image enhancement [J]. Computer Engineering and Applications, 2018, 54(10): 186-191.
[6]	FAN Yuling1, WANG Meili1, HE Dongjian2. Method of point cloud segmentation based on OpenCL [J]. Computer Engineering and Applications, 2018, 54(1): 191-195.
[7]	CAO Guogang, ZHANG Qing, ZHANG Peijun, WANG Zhimin. Multicore-based parallelized differential evolution for image registration [J]. Computer Engineering and Applications, 2017, 53(20): 166-172.
[8]	WANG Yan1，2, WU Junmin1，2, ZHENG Huanxin1. Application and optimization for generalized Hough transform in field of multiple object detection [J]. Computer Engineering and Applications, 2016, 52(17): 203-207.
[9]	LI Yongda, WANG Danghui, HUANG Xiaoping. ZIP password recovery on GPU [J]. Computer Engineering and Applications, 2015, 51(2): 190-193.
[10]	LONG Mansheng, LUO Wenlang. Parallel computing with OpenCL for flow accumulation [J]. Computer Engineering and Applications, 2014, 50(3): 22-29.
[11]	LIU Jinfeng. Comparation of several CUDA accelerated Gaussian filtering algorithms [J]. Computer Engineering and Applications, 2013, 49(23): 14-18.
[12]	WANG Jun. Fast voxelization of geological models on GPUs [J]. Computer Engineering and Applications, 2012, 48(33): 167-172.
[13]	ZHANG Jian, JIAO Liangbao, CHEN Rui. Investigation of ray tracing in dynamic scenes on CPU-GPU hybrid paltform [J]. Computer Engineering and Applications, 2012, 48(21): 151-154.
[14]	YIN Huaguo1, HE Xingwu1，2, ZHOU Honglin1. Ultrasound pulsed wave doppler based on CUDA [J]. Computer Engineering and Applications, 2012, 48(19): 140-144.
[15]	YE Xiaomin，ZHOU Xuegong，CAO Wei，WANG Lingli. Accelerating sparse-grid based SSTA using Graphics Processing Uni [J]. Computer Engineering and Applications, 2011, 47(27): 60-64.

Enablement and verification of GPGPU

图形处理器通用计算的实现与验证

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics