  Generation of optimal codes is a well known problem in coding theory. Many
computational approaches exist in the literature for finding record breaking
codes. However generating codes with long lengths $n$ using serial algorithms
is computationally very expensive, for example the worst case time complexity
of a Greedy algorithm is $\mathcal{O}(n\; 4^n)$. In order to improve the
efficiency of generating codes with long lengths, we propose and investigate
some parallel algorithms using General Purpose Graphic Processing Units
(GPGPU). This paper considers the implementation of parallel Greedy algorithm
using GPGPU-CUDA (Computed Unified Device Architecture) framework and discusses
various optimization techniques to accelerate the GPU code. The performance
achieved for optimized parallel implementations is more than two to three
orders of magnitude faster than that of serial implementation and shows a great
potential of GPGPU in the field of coding theory applications.
