Using Genetic Algorithm to Identify Soft-Error Derating Blocks of an Application Program

Soft-errors are increasingly considered as a major cause for computer system failures. Software techniques are used as cost-effective and flexible techniques to tolerate soft-errors but the introduced overhead is not acceptable in some safety-critical real-time systems. The identification of the program blocks and protecting only vulnerable blocks against soft-errors reduces the performance overhead. In this paper, we present a genetic algorithm to identify the vulnerable program blocks as well as the derating program blocks against soft-errors. Then, only vulnerable blocks are protected by some software-based soft-error tolerance techniques to achieve a lower performance and space overhead. This genetic algorithm is implemented by the C++ programming languages as an automatic tool. To evaluate the algorithm, errors are injected using the Simple scalar toolset. The experimental results indicate that the effectiveness of this method is higher than the previous methods.

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