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SiPS ... design and implementation - IEEE Workshop on Signal Processing Systems

Parallel block-layered nonbinary QC-LDPC decoding on GPU

Huyen Thi Pham, Sabooh Ajaz, Hanho Lee

Published Oct 1, 2015• 8 citations
Publisher

Abstract

This paper presents an efficient implementation of a parallel block-layered nonbinary quasi-cyclic low-density parity-check (NB-QC-LDPC) decoder on a graphics processing unit (GPU) to achieve significant improvements in both flexibility and scalability. An efficient block-layered scheme and a data structure suitable for parallel computing are proposed to perform decoding on the GPU. The scheme is applied to a min-max decoding algorithm that exploits the inherent massive parallelization capabilities of NB-QC-LDPC decoder. The results of the proposed approach demonstrate that the layered scheme can be efficiently implemented in a GPU device. Moreover, experimental results show that the proposed GPU-based block-layered NB-QC-LDPC decoder provides a faster decoding runtime compare to CPU-based implementation and obtains a coding gain under a low 10-10 BER and low 10-7 FER.

DOI
10.1109/sips.2015.7345000

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This paper presents an efficient implementation of a parallel block-layered nonbinary quasi-cyclic low-density parity-check (NB-QC-LDPC) decoder on a graphics processing unit (GPU) to achieve significant improvements in both flexibility and scalability. An efficient block-layered scheme and a data structure suitable for parallel computing are proposed to perform decoding on the GPU. The scheme is applied to a min-max decoding algorithm that exploits the inherent massive parallelization capabilities of NB-QC-LDPC decoder. The results of the proposed approach demonstrate that the layered scheme can be efficiently implemented in a GPU device. Moreover, experimental results show that the proposed GPU-based block-layered NB-QC-LDPC decoder provides a faster decoding runtime compare to CPU-based implementation and obtains a coding gain under a low 10-10 BER and low 10-7 FER.
This paper presents an novel modified Min-Max algorithm (MMMA) and an efficient implementation of an nonbinary LDPC (NB-LDPC) decoder on a graphics processing unit (GPU) to achieve both great flexibility and scalability. The MMMA for check node processing removes the multiplications over Galois-field in merger step and significantly reduces the decoding latency. The proposed MMMA provides a better BER performance than previous algorithm. The experimental results show that the GPU-based implementation of the proposed NB-LDPC decoder provides higher throughput and the coding gain under low 10−8BER comparted to CPU-based implementation.
Nonbinary low-density parity-check (NB-LDPC) codes constructed over GF(q) (q >; 2) can achieve higher coding gain than binary LDPC codes when the code length is moderate. A complete partial-parallel decoder architecture based on the Min-max algorithm is proposed for quasi-cyclic NB-LDPC codes in this paper. A novel scheme and corresponding architecture are developed to implement the elementary step of the check node processing. In our design, layered decoding is applied and only nm5) has also been synthesized on a Xilinx Virtex-2 Pro FPGA device. It can achieve a throughput of 9.30 Mbps when 15 decoding iterations are carried out.
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.
A flexible and scalable approach for LDPC decoding on CUDA based Graphics Processing Unit (GPU) is presented in this paper. Layered decoding is a popular method for LDPC decoding and is known for its fast convergence. However, efficient implementation of the layered decoding algorithm on GPU is challenging due to the limited amount of data-parallelism available in this algorithm. To overcome this problem, a kernel execution configuration that can decode multiple codewords simultaneously on GPU is developed. This paper proposes a compact data packing scheme to reduce the number of global memory accesses and parity-check matrix representation to reduce constant memory latency. Global memory bandwidth efficiency is improved by coalescing simultaneous memory accesses of threads in a half-warp into a single memory transaction. Asynchronous data transfers are used to hide host memory latency by overlapping kernel execution with data transfers between CPU and GPU. The proposed implementation of LDPC decoder on GPU performs two orders of magnitude faster than the LDPC decoder on a CPU and four times faster than the previously reported LDPC decoder on GPU. This implementation achieves a throughput of 160Mbps, which is comparable to dedicated hardware solutions.
The Enhanced quasi-maximum likelihood (EQML) decoder has better decoding performance than the traditional belief propagation (BP) decoder, but its decoding speed is greatly reduced due to its complicated computational structure. Therefore, an efficient graphics processing unit (GPU) implementation structure for EQML decoder of low-density parity-check (LDPC) code is proposed. Intra-codeword parallel and inter-codeword parallel decoding schemes on GPU are adopted instead of serial decoding on CPU. Several optimization methods have also been employed to enhance the decoder throughput, such as compressed storing and Kernel thread arrangement scheme, which improve the decoding speed without degrading the decoding performance of the decoder. Experiment results show that our GPU based implementation structure which is about two orders of magnitude faster than that of CPU implementation.
An efficient parallel simulation scheme of quasi-cyclic (QC) low-density parity-check (LDPC) decoding is proposed to improve the simulation efficiency greatly. It employs multi-threads with the multi-processors of a graphic processing unit (GPU) to perform the simulation of LDPC decoding in parallel. Other than full hardware based LDPC decoding, it obtains good features of low cost, easy programming complexity by using the compute unified device architecture (CUDA) techniques. The CUDA also provides parallel computing by the GPU with efficient multi-thread computation and very high memory bandwidth. Based on the proposed scheme, all bit nodes or check nodes can be updated in an LDPC decoding iteration simultaneously. Therefore, it provides an efficient and fast approach of QC LDPC decoding.