计算机工程与应用 ›› 2011, Vol. 47 ›› Issue (17): 203-206.
• 图形、图像、模式识别 • 上一篇 下一篇
王 聪，冯衍秋
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WANG Cong，FENG Yanqiu
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摘要： 利用GPGPU（General Purpose GPU）强大的并行处理能力，基于NVIDIA CUDA框架对已有的稀疏磁共振（Sparse MRI）重建算法进行了并行化改造，使其能够适应实际应用的要求。稀疏磁共振成像的重建算法包含大量的浮点运算，计算耗时严重，难以应用于实际，必须对其进行加速和优化。实验结果显示，NVIDIA GTX275 GPU使运算时间从4分多钟缩短到3.4秒左右，与Intel Q8200 CPU相比，达到了76倍的加速。
关键词: 通用计算图形处理器（GPGPU）, 统一计算设备架构（CUDA）, 并行计算, 压缩传感, 稀疏磁共振
Abstract: The existing Sparse MRI reconstruction algorithm is made practical by parallelizing it under the framework of NVIDIA CUDA using GPGPU（General Purpose GPU）.A big problem preventing compressed sensing based Sparse MRI from being applied to practice is the reconstruction time due to massive floating-point operation.A speedup up to 76 times on NVIDIA GTX275 GPU against the Intel Q8200 CPU is gained to reduce the processing time from more than 4 minutes to about 3.4 seconds.
Key words: General Purpose GPU（GPGPU）, Compute Unified Device Architecture（CUDA）, parallel computing, compressed sensing, sparse Magnetic Resonance Imaging（MRI）
王 聪，冯衍秋. 利用GPGPU进行快速稀疏磁共振数据重建[J]. 计算机工程与应用, 2011, 47(17): 203-206.
WANG Cong，FENG Yanqiu. Rapid sparse MRI reconstruction with GPGPU[J]. Computer Engineering and Applications, 2011, 47(17): 203-206.
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http://cea.ceaj.org/CN/Y2011/V47/I17/203
