An Optimizing Strategy Research of LDPC Decoding Based on GPGPU

As powerful error correcting codes, Low-Density Parity-Check (LDPC) codes have been adopted as a fundamental building block by dirty paper coding (DPC), which indicates that lossless precoding is theoretically possible at any signal-to-noise ratio (SNR), and is a promising strategy in future communication systems. However, to achieve this performance gain demands huge computation complexity. For its lower cost and better flexibility, the GPU-based LDPC decoder is an emerging research subject. Based on the perspective of GPU hardware architecture, a multi-stage optimizing mapping strategy (MSOMS) is proposed and implemented to accelerate LDPC decoding. The performance is boosted significantly by balancing the memory access and computation load, optimizing execution configuration and the memory access pattern, and fully utilizing the on-chip high speed resources. Proposed decoders can achieve 383-and 442-speedup compared to CPU-based decoder for LDPC and RA code (another ensemble of LDCP code), and the achieved throughput is comparable to existed GPU-based decoders, which confirm the efficiency of the MSOMS strategy.