GPU-based Implementation of Belief Propagation Decoding for Polar Codes

Abstract

Belief Propagation (BP) decoding provides soft outputs and features high-level parallelism. In this paper, we propose an optimized software BP decoder for polar codes on graphics processing units (GPUs). A full-parallel decoding architecture for codes with length n ≤ 2048 is presented to simultaneously update n/2 processing elements (PEs) within each stage and achieve high on-chip memory utilization by using 8-bit quantization. And, for codes with length n > 2048, a partial-parallel decoding architecture is proposed to partly update PEs of each stage in parallel and coalesced global memory accesses are performed. Experimental results show that, with incorporation of the G-matrix based early termination criterion, more than 1 Gbps throughput for codes n ≤ 1024 can be achieved on NVIDIA TITAN Xp at 5 dB while the decoding latency is less than 1 ms. Compared with the state-of-the-art works, the proposed decoder achieves throughput speedups from 2.59× to 131× and provides good tradeoff between error performance and throughput.