Accelerated Network Coding with Dynamic Stream Decomposition on Graphics Processing Unit
Accelerated Network Coding with Dynamic Stream Decomposition on Graphics Processing Unit
复制标题
图形处理单元上的动态流分解加速网络编码
DOI：
--
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
W. Ro
中科院分区：
文献类型：
--
作者：
Sangpil Lee;W. Ro
网络编码是一种用于优化有线和无线网络系统中的数据流的公知技术，在各个领域引起了相当大的关注。然而，网络编码中的解码复杂度成为实际网络系统中的主要性能瓶颈，因此，针对网络编码中解码性能的提高进行了多项研究。然而，以前提出的并行网络编码算法已经显示出有限的可扩展性和性能不平衡的不同大小的传输单元和多个流。在本文中，我们提出了一种新的并行解码算法的网络编码使用的图形处理单元（GPU）。该算法可以同时处理多个输入流，并且无论传输单元的大小和数量如何，都可以保持其最大的解码性能。实验结果表明，该算法在GeForce GTX 285 GPU系统上的解码带宽为682.2 Mbps，与现有的128 × 128系数矩阵和不同大小数据块的单流解码过程相比，速度提高了26倍。
Network coding, a well-known technique for optimizing data-flow in wired and wireless network systems, has attracted considerable attention in various fields. However, the decoding complexity in network coding becomes a major performance bottleneck in the practical network systems; thus, several researches have been conducted for improving the decoding performance in network coding. Nevertheless, previously proposed parallel network coding algorithms have shown limited scalability and performance imbalance for different-sized transfer units and multiple streams. In this paper, we propose a new parallel decoding algorithm for network coding using a graphics processing unit (GPU). This algorithm can simultaneously process multiple incoming streams and can maintain its maximum decoding performance irrespective of the size and number of transfer units. Our experimental results show that the proposed algorithm exhibits a 682.2 Mbps decoding bandwidth on a system with GeForce GTX 285 GPU and speed-ups of up to 26 as compared to the existing single stream decoding procedure with a 128 × 128 coefficient matrix and different-sized data blocks.