Data storage optimization of application-level checkpointing on heterogeneous systems

General purpose GPU´s (GPGPU) appearance made it possible that heterogeneous computing can be used by human beings. And it´s also produce a reform for GPU´s general purpose computing and parallel computing. Heterogeneous Systems has been adopted by large-scale of high-performance computers. Nowadays, fault tolerance technique is necessary among these large-scale kinds of scientific computing, but in a few years of GPGPU and heterogeneous system appearance, there is not an effective fault tolerance method come out, therefore, towards this situation, this paper will apply the traditional fault tolerance technique—application-level checkpointing to heterogeneous system. Cause the main solution of reducing overhead of the application-level checkpointing is reducing checkpoint data size, so after analyzing the heterogeneous system and GPGPU program, we propose a method to optimize the data storage of application-level checkpointing technique and validate its optimization by experiments.