The parallel computing platforms available today are increasingly larger. Typically the emerging parallel platforms will be composed of several millions of CPU cores running up to a billion of threads. This intensive growth of the number of parallel threads will make the application subject to more and more failures. Consequently it is necessary to develop efficient strategies providing safe and reliable completion for HPC parallel applications. Checkpointing is one of the most popular and efficient technique for developing fault-tolerant applications on such a context. However, checkpoint operations are costly in terms of time, computation and network communications. This will certainly affect the global performance of the application. In the first part of this thesis, we propose a performance model that expresses formally the checkpoint scheduling problem. Two variants of the problem have been considered. In the first variant, the objective is the minimization of the expected completion time. Under this model we prove that when the failure rate and the checkpoint cost are constant the optimal checkpoint strategy is necessarily periodic. For the general problem when the failure rate and the checkpoint cost are arbitrary we provide a numerical solution for the problem. In the second variant if the problem, we exhibit the tradeoff between the impact of the checkpoints operations and the lost computation due to failures. In particular, we prove that the checkpoint scheduling problem is NP-hard even in the simple case of uniform failure distribution. We also present a dynamic programming scheme for determining the optimal checkpointing times in all the variants of the problem. In the second part of this thesis, we design several fault tolerant scheduling algorithms that minimize the application makespan and in the same time maximize the application reliability. Mainly, in this part we point out that the growth rate of the failure distribution determines the relationship between both objectives. More precisely we show that when the failure rate is decreasing the two objectives are antagonist. In the second hand when the failure rate is increasing both objective are congruent. Finally, we provide approximation algorithms for both failure rate cases.
