Low-Density Parity-Check (LDPC) codes are widely used from hard-disk systems to satellite communications. Sliding-Window Belief Propagation (SWBP) is an effective decoding algorithm of LDPC codes for time-varying channels and demonstrates near-optimal performance in many experiments. However, to adaptively find the best window size, SWBP may need very long computing time. Inspired by Graphics Processing Unit and Compute Unified Device Architecture, in this paper we propose a novel method to address the issue of SWBP’s computing complexity. Different from sequential SWBP, we simultaneously compute the metrics of different window sizes in parallel, which enables us to quickly find the best window size. We use coalesced memory access to accelerate reading and writing processes. Registers and shared memory are also considered in our program to reduce memory latency. On the GTX 1080Ti platform, experimental results show that parallel SWBP can achieve about 14 $$\times $$ × to 118 $$\times $$ × speedup ratio for different regular LDPC codes, and about 8 $$\times $$ × to 120 $$\times $$ × speedup ratio for different irregular LDPC codes, respectively. According to the trend of our experiments, we strongly believe that, as the length of LDPC codes increases, a higher speedup ratio can be obtained.

中文翻译：

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用于 LDPC 码的滑动窗口置信度传播的 GPU 加速并行算法

低密度奇偶校验 (LDPC) 码广泛用于从硬盘系统到卫星通信。滑动窗口置信度传播 (SWBP) 是一种用于时变信道的 LDPC 码的有效解码算法，并在许多实验中表现出接近最佳的性能。然而，为了自适应地找到最佳窗口大小，SWBP 可能需要很长的计算时间。受图形处理单元和计算统一设备架构的启发，在本文中，我们提出了一种解决 SWBP 计算复杂性问题的新方法。与顺序 SWBP 不同，我们同时并行计算不同窗口大小的度量，这使我们能够快速找到最佳窗口大小。我们使用合并内存访问来加速读取和写入过程。我们的程序中还考虑了寄存器和共享内存，以减少内存延迟。在GTX 1080Ti平台上，实验结果表明，对于不同的常规LDPC码，并行SWBP可以实现约14 $$\times $$ × 到118 $$\times $$ × 的加速比，约8 $$\times $$ ×分别为 120 $$\times $$ × 不同不规则 LDPC 码的加速比。根据我们的实验趋势，我们坚信，随着 LDPC 码长度的增加，可以获得更高的加速比。