Enhancing Fault-Tolerance of Large-Scale MPI Scientific Applications

Abstract

Abstract. The running times of large-scale computational science and engineering parallel applications, executed on clusters or Grid platforms, are usually longer than the mean-time-between-failures (MTBF). Therefore, hardware failures must be tolerated to ensure that not all computation done is lost on machine failures. Checkpointing and rollback recovery are very useful techniques to implement fault-tolerant applications. Although extensive research has been carried out in this field, there are few available tools to help parallel programmers to enhance their applications with fault tolerance support. This work presents an experience to endow with fault tolerance two large MPI scientific applications: an air quality simulation model and a crack growth analysis. A fault tolerant solution has been implemented by means of a checkpointing and recovery tool, the CPPC framework. Detailed experimental results are presented to show the practical usefulness and low overhead of this checkpointing approach.

OpenCluster: A Flexible Distributed Computing Framework for Astronomical Data Processing

“…However, in general, MPI is rarely selected for developing real-time data processing systems because it does not provide standardized fault tolerance interfaces and semantics. Although extensive research [27,28,29] has been conducted in this area, few available tools exist to help parallel programmers enhance their applications with fault tolerance support. Moreover, the exploitation of MPI is impeded by difficulties in software development.…”