In this study, a parallel computation model named delta-stepping synchronous parallel (DSP) is introduced, which is a more general form than BSP (bulk synchronous parallel). Compared with BSP, delta steps of local computation substitute the single local computation in each superstep. The added local computation is named as speculative computation step (SCStep). SCStep could further explore the locality hidden in data and accelerate value diffusion. It turns out to be dramatically effective on reducing the number of iterations and shortening the convergence time. Meanwhile, it is found that excessively using the SCStep is not appropriate considering the increased computation overhead. To identify applicable algorithms and also prove the correctness, the iterative process is formalized and the convergence condition is deduced. Finally, case studies and evaluations show that DSP model could significantly reduce the number of iterations and shorten the convergence time by dozens of times.