  This paper presents a computationally efficient implementation of a Hamming
code decoder on a graphics processing unit (GPU) to support real-time
software-defined radio (SDR), which is a software alternative for realizing
wireless communication. The Hamming code algorithm is challenging to
parallelize effectively on a GPU because it works on sparsely located data
items with several conditional statements, leading to non-coalesced, long
latency, global memory access, and huge thread divergence. To address these
issues, we propose an optimized implementation of the Hamming code on the GPU
to exploit the higher parallelism inherent in the algorithm. Experimental
results using a compute unified device architecture (CUDA)-enabled NVIDIA
GeForce GTX 560, including 335 cores, revealed that the proposed approach
achieved a 99x speedup versus the equivalent CPU-based implementation.
