Analyzing Data-Error Propagation Effects in High-Performance Computing

Algorithmic codes for scientific computing may exhibit diverse levels of tolerance to memory errors, depending on the program behavior when accessing data. For example, tolerance to errors may depend on the specific access patterns used while accessing memory, due to the application data structures and arrays. In this paper, we analyze the impact of the propagation of the errors originated from memory accesses on a solver running on a cluster. The application is written on top of the MPI programming model, and we evaluate the speed of the error propagation to other MPI processes. In addition, we propose a preliminary model to represent the most probable number of iterations needed to propagate the error from one process to any other process of the application. We implement and validate our model with the execution of miniFE miniapplication in a supercomputer platform. Our preliminary experimental results show that our model can make a quite accurate prediction of the memory error propagation within 14% of error.