Computational modeling of the heart has demonstrated to be a useful tool for the investigation and comprehension of the complex biophysical processes that underlie cardiac function. Unfortunately, large scale simulations, such as those resulting from the discretization of an entire heart, remain a computational challenge. In order to reduce simulation execution times, parallel implementations have traditionally exploited data parallelism via numerical schemes based on domain-decomposition. However, it has been verified that the parallel efficiency of these implementations severely degrades as the number of processors increases. In this work, we propose and implement a new parallel algorithm for the solution of cardiac models. By relaxing the coherence of the execution, a new level of parallelism could be identified and exploited: pipelining. A synchronous parallel algorithm that uses both pipelining and data decomposition techniques was implemented and used the MPI library for communication. Numerical tests were performed in a 8-node linux-cluster. Our preliminary results indicate that the proposed algorithm is able to increase the parallel efficiency up to 20% when compared to the traditional approach that uses pure data-level parallelism. In addition, the numerical precision was kept under control (relative errors under 4%) when the relaxed coherence execution was adopted.