The X10 Global Matrix Library (GML) is designed to simplify the development of scalable linear algebra applications. By hiding the communication and parallelism details, GML programs are written in a sequential style that is easy to use and understand by non expert programmers.

Resilience is becoming a major challenge for HPC applications as the number of components in a typical system continues to increase. To address this challenge, we improved GML’s adaptability to process failure and provided a mechanism for automatic data recovery. As iterative algorithms are commonly used in linear algebra applications, we also created a checkpoint/restore framework for developing resilient iterative applications using GML.

Using three example machine learning applications, we demonstrate that this framework supports resilient application development with minimal additional code compared to a non-resilient implementation. Performance measurements in a typical cluster environment show that the major cost of resilient execution is due to resilient X10 itself, and that the additional cost due to our framework is acceptable.