Massive parallel LDPC decoding on GPU

Gabriel Falcao, Leonel Sousa, Vitor Silva
Instituto de Telecomunicacoes/FCTUC, University of Coimbra, Coimbra, Portugal
In PPoPP ’08: Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming (2008), pp. 83-90


   title={Massive parallel LDPC decoding on GPU},

   author={Falc{~a}o, G. and Sousa, L. and Silva, V.},

   booktitle={Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming},





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Low-Density Parity-Check (LDPC) codes are powerful error correcting codes (ECC). They have recently been adopted by several data communication standards such as DVB-S2 and WiMax. LDPCs are represented by bipartite graphs, also called Tanner graphs, and their decoding demands very intensive computation. For that reason, VLSI dedicated architectures have been investigated and developed over the last few years. This paper proposes a new approach for LDPC decoding on graphics processing units (GPUs). Efficient data structures and an new algorithm are proposed to represent the Tanner graph and to perform LDPC decoding according to the stream-based computing model. GPUs were programmed to efficiently implement the proposed algorithms by applying data-parallel intensive computing. Experimental results show that GPUs perform LDPC decoding nearly three orders of magnitude faster than modern CPUs. Moreover, they lead to the conclusion that GPUs with their tremendous processing power can be considered as a consistent alternative to state-of-the-art hardware LDPC decoders.
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