This paper proposes a new and efficient parallel implementation of support vector machines based on decom- position method for handling large scale datasets. The paral- lelizing is performed on the most time-and-memory consuming work of training, i.e., to update the vector f . The inner problems are dealt by sequential minimal optimization solver. Since the underlying parallelism is realized by the shared memory version of Map-Reduce paradigm, our system is easy to build and particularly suitable to apply to multi-core and multiprocessor systems. Experimental results show that on most of the tested datasets, our system offers higher than four- fold increase in speed compared to Libsvm, and it is also far more efficient than the MPI implementation Pisvm.