The current system reaction to the loss of a single MPI process is to kill all the remaining processes and restart the application from the most recent checkpoint. This approach will become unfeasible for future extreme scale systems. We address this issue using an emerging resilient computing model called Local Failure Local Recovery (LFLR) that provides application developers with the ability to recover locally and continue application execution when a process is lost. We discuss the design of our software framework to enable the LFLR model using MPI-ULFM and demonstrate the resilient version of MiniFE that achieves a scalable recovery from process failures.
For example, the Local Failure Local Recovery (LFLR) REF approach uses an early implementation of ULFM MPI to facilitate recovery using spare processes.
Toward Local Failure Local Recovery Resilience Model using MPI-ULFM
EuroMPI/ASIA '14
Computer Science Engineering