11.5. Fault Tolerance
11.5.1. What is “fault tolerance”?
The phrase “fault tolerance” means many things to many people. Typical definitions include user processes dumping vital state to disk periodically, checkpoint/restart of running processes, elaborate recreate-process-state-from-incremental-pieces schemes, and many others.
In the scope of Open MPI, we typically define “fault tolerance” to mean the ability to recover from one or more component failures in a well defined manner with either a transparent or application-directed mechanism. Component failures may exhibit themselves as a corrupted transmission over a faulty network interface or the failure of one or more serial or parallel processes due to a processor or node failure. Open MPI strives to provide the application with a consistent system view while still providing a production quality, high performance implementation.
Yes, that’s pretty much as all-inclusive as possible — intentionally so! Remember that in addition to being a production-quality MPI implementation, Open MPI is also a vehicle for research. So while some forms of “fault tolerance” are more widely accepted and used, others are certainly of valid academic interest.
11.5.2. What fault tolerance techniques has / does / will Open MPI support?
Open MPI was a vehicle for research in fault tolerance and over the years provided support for a wide range of resilience techniques:
User Level Fault Mitigation techniques similar to those implemented in FT-MPI.
- Deprecated / no longer available
Coordinated and uncoordinated process checkpoint and restart. Similar to those implemented in LAM/MPI and MPICH-V, respectively.
<strike>Message logging techniques. Similar to those implemented in MPICH-V</strike>
<strike>Data Reliability and network fault tolerance. Similar to those implemented in LA-MPI</strike>
The Open MPI team will not limit their fault tolerance techniques to those mentioned above, but intend on extending beyond them in the future.
11.5.3. Does Open MPI support checkpoint and restart of parallel jobs (similar to LAM/MPI)?
Old versions of OMPI (starting from v1.3 series) had support for the transparent, coordinated checkpointing and restarting of MPI processes (similar to LAM/MPI).
Open MPI supported both the the BLCR checkpoint/restart system and a “self” checkpointer that allows applications to perform their own checkpoint/restart functionality while taking advantage of the Open MPI checkpoint/restart infrastructure. For both of these, Open MPI provides a coordinated checkpoint/restart protocol and integration with a variety of network interconnects including shared memory, Ethernet, and InfiniBand.
The implementation introduces a series of new frameworks and components designed to support a variety of checkpoint and restart techniques. This allows us to support the methods described above (application-directed, BLCR, etc.) as well as other kinds of checkpoint/restart systems (e.g., Condor, libckpt) and protocols (e.g., uncoordinated, message induced).
The checkpoint/restart support was last released as part of the v1.6 series.
11.5.4. Where can I find the fault tolerance development work?
The only active work in resilience in Open MPI targets the User Level Fault Mitigation (ULFM) approach, a technique discussed in the context of the MPI standardization body.
For information on the Fault Tolerant MPI prototype in Open MPI see the links below:
Support for other types of resilience (e.g., data reliability, checkpoint) has been deprecated over the years due to lack of adoption and lack of maintenance. If you are interested in doing some archeological work, traces are still available on the main repository.
11.5.5. Does Open MPI support end-to-end data reliability in MPI message passing?
Current Open MPI releases have no support for end-to-end data reliability, at least not more than currently provided by the underlying network.
The data reliability PML component (
on some past releases has been deprecated), assumed that the
underlying network is unreliable. It could drop / restart connections,
retransmit corrupted or lost data, etc. The end effect is that data
sent through MPI API functions will be guaranteed to be reliable.
For example, if you’re using TCP as a message transport, chances of data corruption are fairly low. However, other interconnects do not guarantee that data will be uncorrupted when traveling across the network. Additionally, there are nonzero possibilities that data can be corrupted while traversing PCI buses, etc. (some corruption errors at this level can be caught/fixed, others cannot). Such errors are not uncommon at high altitudes (!).
Note that such added reliability does incur a performance cost — latency and bandwidth suffer when Open MPI performs the consistency checks that are necessary to provide such guarantees.
Most clusters/networks do not need data reliability. But some do
(e.g., those operating at high altitudes). The
dr PML was intended for
these rare environments where reliability was an issue; and users were
willing to tolerate slightly slower applications in order to guarantee
that their job does not crash (or worse, produce wrong answers).