Publications

ACM International Conference Proceeding Series

Teranishi, Keita T.; Heroux, Michael A.

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.

Teranishi, Keita T.; Heroux, Michael A.; Gamell Balmana, Marc G.; Parashar, Manish P.

Abstract not provided.

Teranishi, Keita T.; Gamell Balmana, Marc G.; Heroux, Michael A.; Parashar, Manish, R.

Abstract not provided.

Teranishi, Keita T.; Heroux, Michael A.

Abstract not provided.

Heroux, Michael A.; Teranishi, Keita T.

Recovery from process loss during the execution of a distributed memory parallel application is presently achieved by restarting the program, typically from a checkpoint file. Future computer system trends indicate that the size of data to checkpoint, the lack of improvement in parallel file system performance and the increase in process failure rates will lead to situations where checkpoint restart becomes infeasible. In this report we describe and prototype the use of a new application level resilient computing model that manages persistent storage of local state for each process such that, if a process fails, recovery can be performed locally without requiring access to a global checkpoint file. LFLR provides application developers with an ability to recover locally and continue application execution when a process is lost. This report discusses what features are required from the hardware, OS and runtime layers, and what approaches application developers might use in the design of future codes, including a demonstration of LFLR-enabled MiniFE code from the Matenvo mini-application suite.