We consider the problem of non-adaptive group testing of $N$ items out of which $K$ or less items are known to be defective. We propose a testing scheme based on left-and-right-regular sparse-graph codes and a simple iterative decoder. We show that for any arbitrarily small $ε>0$ our scheme requires only $m=c_εK\log \frac{c_1N}{K}$ tests to recover $(1-ε)$ fraction of the defective items with high probability (w.h.p) i.e., with probability approaching $1$ asymptotically in $N$ and $K$, where the value of constants $c_ε$ and $\ell$ are a function of the desired error floor $ε$ and constant $c_1=\frac{\ell}{c_ε}$ (observed to be approximately equal to 1 for various values of $ε$). More importantly the iterative decoding algorithm has a sub-linear computational complexity of $\mathcal{O}(K\log \frac{N}{K})$ which is known to be optimal. Also for $m=c_2 K\log K\log \frac{N}{K}$ tests our scheme recovers the \textit{whole} set of defective items w.h.p. These results are valid for both noiseless and noisy versions of the problem as long as the number of defective items scale sub-linearly with the total number of items, i.e., $K=o(N)$. The simulation results validate the theoretical results by showing a substantial improvement in the number of tests required when compared to the testing scheme based on left-regular sparse-graphs.