  In this paper, we propose a parallel block-based Viterbi decoder (PBVD) on
the graphic processing unit (GPU) platform for the decoding of convolutional
codes. The decoding procedure is simplified and parallelized, and the
characteristic of the trellis is exploited to reduce the metric computation.
Based on the compute unified device architecture (CUDA), two kernels with
different parallelism are designed to map two decoding phases. Moreover, the
optimal design of data structures for several kinds of intermediate information
are presented, to improve the efficiency of internal memory transactions.
Experimental results demonstrate that the proposed decoder achieves high
throughput of 598Mbps on NVIDIA GTX580 and 1802Mbps on GTX980 for the 64-state
convolutional code, which are 1.5 times speedup compared to the existing
fastest works on GPUs.
