Scalable, Fault-Tolerant Membership for MPI Tasks on HPC Systems
Abstract
Reliability is increasingly becoming a challenge for high-performance computing (HPC) systems with thousands of nodes, such as IBM's Blue Gene/L. A shorter mean-time-to-failure can be addressed by adding fault tolerance to reconfigure working nodes to ensure that communication and computation can progress. However, existing approaches fall short in providing scalability and small reconfiguration overhead within the fault-tolerant layer. This paper contributes a scalable approach to reconfigure the communication infrastructure after node failures. We propose a decentralized (peer-to-peer) protocol that maintains a consistent view of active nodes in the presence of faults. Our protocol shows response times in the order of hundreds of microseconds and single-digit milliseconds for reconfiguration using MPI over BlueGene/L and TCP over Gigabit, respectively. The protocol can be adapted to match the network topology to further increase performance. We also verify experimental results against a performance model, which demonstrates the scalability of the approach. Hence, the membership service is suitable for deployment in the communication layer of MPI runtime systems, and we have integrated an early version into LAM/MPI.
- Authors:
-
- North Carolina State University (NCSU), Raleigh
- ORNL
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1003542
- DOE Contract Number:
- AC05-00OR22725
- Resource Type:
- Conference
- Resource Relation:
- Conference: 20th ACM International Conference on Supercomputing (ICS) 2006, Cairns, Australia, 20060628, 20060701
- Country of Publication:
- United States
- Language:
- English
- Subject:
- Reliability; high-performance computing; node failure; message passing; group communication; scalability
Citation Formats
Varma, Jyothish S., Wang, Chao, Mueller, Frank, Engelmann, Christian, and Scott, Steven L. Scalable, Fault-Tolerant Membership for MPI Tasks on HPC Systems. United States: N. p., 2006.
Web. doi:10.1145/1183401.1183433.
Varma, Jyothish S., Wang, Chao, Mueller, Frank, Engelmann, Christian, & Scott, Steven L. Scalable, Fault-Tolerant Membership for MPI Tasks on HPC Systems. United States. doi:10.1145/1183401.1183433.
Varma, Jyothish S., Wang, Chao, Mueller, Frank, Engelmann, Christian, and Scott, Steven L. Sun .
"Scalable, Fault-Tolerant Membership for MPI Tasks on HPC Systems". United States.
doi:10.1145/1183401.1183433.
@article{osti_1003542,
title = {Scalable, Fault-Tolerant Membership for MPI Tasks on HPC Systems},
author = {Varma, Jyothish S. and Wang, Chao and Mueller, Frank and Engelmann, Christian and Scott, Steven L},
abstractNote = {Reliability is increasingly becoming a challenge for high-performance computing (HPC) systems with thousands of nodes, such as IBM's Blue Gene/L. A shorter mean-time-to-failure can be addressed by adding fault tolerance to reconfigure working nodes to ensure that communication and computation can progress. However, existing approaches fall short in providing scalability and small reconfiguration overhead within the fault-tolerant layer. This paper contributes a scalable approach to reconfigure the communication infrastructure after node failures. We propose a decentralized (peer-to-peer) protocol that maintains a consistent view of active nodes in the presence of faults. Our protocol shows response times in the order of hundreds of microseconds and single-digit milliseconds for reconfiguration using MPI over BlueGene/L and TCP over Gigabit, respectively. The protocol can be adapted to match the network topology to further increase performance. We also verify experimental results against a performance model, which demonstrates the scalability of the approach. Hence, the membership service is suitable for deployment in the communication layer of MPI runtime systems, and we have integrated an early version into LAM/MPI.},
doi = {10.1145/1183401.1183433},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2006},
month = {Sun Jan 01 00:00:00 EST 2006}
}