Evaluation of Simple Causal Message Logging for Large-Scale Fault Tolerant HPC Systems
The era of petascale computing brought machines with hundreds of thousands of processors. The next generation of exascale supercomputers will make available clusters with millions of processors. In those machines, mean time between failures will range from a few minutes to few tens of minutes, making the crash of a processor the common case, instead of a rarity. Parallel applications running on those large machines will need to simultaneously survive crashes and maintain high productivity. To achieve that, fault tolerance techniques will have to go beyond checkpoint/restart, which requires all processors to roll back in case of a failure. Incorporating some form of message logging will provide a framework where only a subset of processors are rolled back after a crash. In this paper, we discuss why a simple causal message logging protocol seems a promising alternative to provide fault tolerance in large supercomputers. As opposed to pessimistic message logging, it has low latency overhead, especially in collective communication operations. Besides, it saves messages when more than one thread is running per processor. Finally, we demonstrate that a simple causal message logging protocol has a faster recovery and a low performance penalty when compared to checkpoint/restart. Running NAS Parallel Benchmarks (CG, MG and BT) on 1024 processors, simple causal message logging has a latency overhead below 5%.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 1021072
- Report Number(s):
- LLNL-CONF-471680; TRN: US201116%%985
- Resource Relation:
- Conference: Presented at: Dependable Parallel, Distributed and Network-. Centric System, Anchorage, AK, United States, May 16 - May 16, 2011
- Country of Publication:
- United States
- Language:
- English
Similar Records
A case for Virtual Machine based Fault Injection in a High-Performance Computing Environment
...And Eat it Too: High Read Performance in Write-Optimized HPC I/O Middleware File Formats