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Asilomar Conference on Signals, Systems and Computers (ACSSC)

Sequential decoding of non-binary LDPC codes on graphics processing units

David L. Romero, Nicholas B. Chang

Published Nov 1, 2012• 10 citations
Publisher

Abstract

Non-binary low-density parity-check (LDPC) codes have been shown to attain near capacity error correcting performance in noisy wireless communication channels. It is well known that these codes require a very large number of operations per-bit to decode. This high computational complexity along with a parallel decoder structure makes graphics processing units (GPUs) an attractive platform for acceleration of the decoding algorithm. The seemingly random memory access patterns associated with decoding are generally beneficial to error-correcting performance but present a challenge to designers who want to leverage the computational capabilities of the GPU. In this paper we describe the design of an efficient decoder implementation based on GPUs and a corresponding set of powerful non-binary LDPC codes. Using the belief propagation algorithm with a sequential message updating scheme it is shown that we are able to exploit parallelism inherent in the decoding algorithm while decreasing the number of decoding iterations required for convergence.

DOI
10.1109/acssc.2012.6489227

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Non-binary low-density parity-check (LDPC) codes have been shown to attain near capacity error correcting performance in noisy wireless communication channels. It is well known that these codes require a very large number of operations per-bit to decode. This high computational complexity along with a parallel decoder structure makes graphics processing units (GPUs) an attractive platform for acceleration of the decoding algorithm. The seemingly random memory access patterns associated with decoding are generally beneficial to error-correcting performance but present a challenge to designers who want to leverage the computational capabilities of the GPU. In this paper we describe the design of an efficient decoder implementation based on GPUs and a corresponding set of powerful non-binary LDPC codes. Using the belief propagation algorithm with a sequential message updating scheme it is shown that we are able to exploit parallelism inherent in the decoding algorithm while decreasing the number of decoding iterations required for convergence.
Low-Density Parity-Check (LDPC) codes are very powerful channel coding schemes with a broad range of applications. The existence of low complexity (i.e., linear time) iterative message passing decoders with close to optimum error correction performance is one of the main strengths of LDPC codes. It has been shown that the performance of these decoders can be further enhanced if the LDPC codes are extended to higher order Galois fields, yielding so called non-binary LDPC codes. However, this performance gain comes at the cost of rapidly increasing decoding complexity. To deal with this increased complexity, we present an efficient implementation of a signed-log domain FFT decoder for non-binary irregular LDPC codes that exploits the inherent massive parallelization capabilities of message passing decoders. We employ Nvidia's Compute Unified Device Architecture (CUDA) to incorporate the available processing power of state-of-the-art Graphics Processing Units (GPU s).
Low density parity check (LDPC) decoding process is known as compute intensive. This kind of digital communication applications was recently implemented onto graphic processing unit (GPU) devices for LDPC code performance estimation and/or for real-time measurements. Overall previous studies about LDPC decoding on GPU were based on the implementation of the flooding-based decoding algorithm that provides massive computation parallelism. More efficient layered schedules were proposed in literature because decoder iteration can be split into sublayer iterations. These schedules seem to badly fit onto GPU devices due to restricted computation parallelism and complex memory access patterns. However, the layered schedules enable the decoding convergence to speed up by two. In this letter, we show that: 1) layered schedule can be efficiently implemented onto a GPU device; and 2) this approach—implemented onto a low-cost GPU device—provides higher throughputs with identical correction performances (BER) compared to previously published results.
Low-Density Parity-Check (LDPC) code is a powerful error correcting code. It has been widely adopted by many communication systems. Finding a fast and efficient design of LDPC has been an active research area. This paper proposes a high performance design for irregular LDPC decoding on a general purpose graphic processing unit (GPGPU). A GPGPU is a many-core architecture which enables massively parallel computing. In this paper, a high degree of computation parallelism has been exposed by decoding multiple LDPC code-words concurrently. An innovative data structure is proposed to more efficiently leverage memory coalescing for the irregular data accesses of LDPC decoding. Data spatial locality is maximized by keeping more reusable data within the on-chip cache of a GPGPU. The data communication overhead between a host and a GPGPU is minimized through a single word copy for the convergence check. The experiment results show that the proposed design can achieve up to 55.68X runtime improvement, when compared with a sequential LDPC program on a CPU.
This paper proposes a flexible low-density parity-check (LDPC) decoder which leverages graphic processor units (GPU) to provide high decoding throughput. LDPC codes are widely adopted by the new emerging standards for wireless communication systems and storage applications due to their near-capacity error correcting performance. To achieve high decoding throughput on GPU, we leverage the parallelism embedded in the check-node computation and variable-node computation and propose a parallel strategy of partitioning the decoding jobs among multi-processors in GPU. In addition, we propose a scalable multi-codeword decoding scheme to fully utilize the computation resources of GPU. Furthermore, we developed a novel adaptive performance-tuning method to make our decoder implementation more flexible and scalable. The experimental results show that our LDPC decoder is scalable and flexible, and the adaptive performance-tuning method can deliver the peak performance based on the GPU architecture.
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.