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The scale-up of system brings improvement in performance as well as reliability degradation, so there is a need to apply some fault tolerance mechanism to tolerate hardware failure or recover data. Currently, the popular fault tolerance mechanisms, such as Checkpoint/Restart and N-modular redundancy, all need additional overhead, which limits the scalability of parallel computing to some extent. Therefore, it is very important to develop scalable fault tolerance mechanisms for increasingly high performance supercomputing. This paper takes triple modular redundancy (TMR) as an example, describes the implementation of TMR on large-scale MPI parallel computing, and argues that traditional TMR fault-tolerant mechanism limits the scalability of parallel computing. To solve these practical problems, the paper proposes the scalable triple modular redundancy (STMR), and verifies the validity and scalability of it. STMR can not only handle the fail-stop failures that are traditionally handled by Checkpoint/Restart, but can also deal with most of data errors not perceived directly by the hardware. Finally, the study conducts the simulation using the system parameters of BlueGene/L, which shows the scalability change of parallel computing with the TMR and the STMR respectively when the system size increases. The results further validate STMR position as scalable fault-tolerant mechanism.

Scalable Triple Modular Redundancy Fault Tolerance Mechanism for MPI-Oriented Large Scale Parallel Computing