A Gb/s Parallel Block-based Viterbi Decoder for Convolutional Codes on GPU

TL;DR

This paper presents a highly efficient parallel Viterbi decoder for convolutional codes on GPUs, significantly improving decoding speed by leveraging parallelism and optimized data structures.

Contribution

It introduces a novel parallel block-based Viterbi decoding method on GPUs with optimized data structures, achieving higher throughput than previous GPU-based decoders.

Findings

Achieves 598 Mbps on GTX580 and 1802 Mbps on GTX980 for 64-state codes.

Provides 1.5x speedup over existing GPU decoders.

Demonstrates effective parallelization and memory optimization techniques.

Abstract

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.