基于GPU的LDPC增强准最大似然译码器并行实现

Parallel Implementation of GPU-Based LDPC Enhanced Quasi-Maximum Likelihood Decoder

增强准最大似然(EQML)译码器对于码长较短的低密度奇偶校验(LDPC)码的译码性能优于传统置信传播(BP)译码器,可较好满足5G移动通信的高可靠性要求,但由于其计算结构复杂导致译码速度大幅降低。为提高EQML译码器的译码速度,提出一种基于GPU的EQML译码器并行化加速方案,压缩并存储不规则LDPC码的奇偶校验矩阵,通过对传统BP译码算法进行重新排序以最大化利用Kernel中的线程,并对再处理过程中的每个阶段进行多码字并行译码,实现内存访问优化及流并行译码。实验结果表明,基于GPU的EQML译码器在保持纠错性能的同时,相比基于CPU的EQML译码器的译码速度约提升了2个数量级。

The Enhanced Quasi-Maximum Likelihood(EQML)decoder has better decoding performance for short Low Density Parity Check(LDPC)codes than traditional Belief Propagation(BP)decoder,and can meet the high reliability requirements of 5G mobile communication.However,its decoding speed is greatly reduced due to its complex computational structure.To address the problem,this paper proposes a parallel acceleration scheme based on Graphics Processing Unit(GPU)for EQML decoder.The scheme compresses and stores the parity check matrix of irregular LDPC codes,and resorts the traditional BP decoding algorithms to maximize the utilization of threads in Kernel.Then parallel decoding is implemented for multi-code words in each stage of reprocessing,so as to realize memory access optimization and parallel decoding of streams.Experimental results show that the GPU-based EQML decoder improves the speed by two orders of magnitude compared with the CPU-based decoder,while keeping the error correction performance.