Parallel computing systems provide hardware redundancy that helps to achieve low cost fault-tolerance, by duplicating the task into more than a single processor, and comparing the states of the processors at checkpoints. This paper suggests a novel technique, based on a Markov reward model (MRM), for analyzing the performance of checkpointing schemes with task duplication. We show how this technique can be used to derive the average execution time of a task and other important parameters related to the performance of checkpointing schemes. Our analytical results match well the values we obtained using a simulation program. We compare the average task execution time and total work of four checkpointing schemes, and show that generally increasing the number of processors reduces the average execution time, but increases the total work done by the processors. However, in cases where there is a big difference between the time it takes to perform different operations, those results can change. >