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The ever increasing complexity of scientific applications has led to utilization of new HPC paradigms such as Graphical Processing Units (GPUs). However, modifying existing applications to enable them to be executed on GPU can be challenging. Furthermore, the considerable speedup achieved by execution of linear algebra operations on GPUs has added a huge heterogeneity to HPC clusters. In this work, we enabled NPAIRS, a neuro-imaging application, to be executed on GPU with slight modifications to its original code. This important feature of our implementation enables current users of NPAIRS, i.e. non-expert bio-medical scientists, to get benefit from GPU without having to apply fundamental changes to their existing application. As the second part of our research, we investigated the efficiency of several scheduling algorithms for a heterogeneous cluster that contains GPU nodes. Experimental results show that we achieved 7x speedup for NPAIRS. Moreover, although scheduling does not play an important role when there is no GPU node in the cluster, it can highly improve the makespan for a CPU-GPU cluster. We compared our scheduling results with Torque and MCT, two of the most commonly used schedulers in current HPC platforms. Our results show that the Sufferage scheduling can improve the makespan of Torque and MCT by 47% and 4% respectively.