I've been working on MPI piggyback technique as a part of my PhD work.
Although MPI does not provide a native support, there are several different
solutions to transmit piggyback data over every MPI communication. You may
find a brief overview in papers [1, 2]. This includes copying the original
message and the extra data to a bigger buffer, sending additional message or
changing the sendtype to a dynamically created wrapper datatype that
contains a pointer to the original data and the piggyback data. I have tried
all mechanisms and they work, but considering the overhead, there is no "the
best" technique that outperforms the others in all scenarios. Jeff Squyres
had interesting comments on this subject before (in this mailing list).
Finally after some benchmarking, I have implemented *a *hybrid technique
that combines existing mechanisms. For small, point-to-point messages
datatype wrapping seems to be the less intrusive, at least considering
OpenMPI implementation of derived datatypes. For large, point-to-point
messages, experiments confirmed that sending an additional message is much
cheaper than wrapping (and besides the intrusion is small as we are already
sending a large message). Moreover, the implementation may interleave the
original send with an asynchronous send of piggyback data. This optimization
partially hides the latency of additional send and lowers overall intrusion.
The same criteria can be applied for collective operations, except barrier
and reduce operations. As the former does not transmit any data and the
latter transforms the data, the only solution is to send additional
There is a penalty of course. Especially for collective operations with very
small messages the intrusion may reach 15% and that's a lot. It than
decreases down to 0.1% for bigger messages, but anyway it's still there. I
don't know what are your requirements/expectations for that issue. The only
work that reported lower overheads is  but they added native piggyback
support by changing underlying MPI implementation.
I think the best possible option is to add piggyback support for MPI as a
part of the standard. A growing number of runtime tools use this
functionality for multiple reasons and certainly PMPI itself is not enough.
References of interest:
 Shende, S., Malony, A., Morris, A., Wolf, F. "Performance
Profiling Overhead Compensation for MPI Programs". 12th EuroPVM-MPI
Conference, LNCS, vol. 3666, pp. 359-367, 2005. They review various
techniques and come up with datatype wrapping.
 Schulz, M., "Extracting Critical Path Graphs from MPI
Applications". Cluster Computing 2005, IEEE International, pp. 1-10,
September 2005. They use datatype wrapping.
-  Jeffrey Vetter, "Dynamic Statistical Profiling of Communication
Activity in Distributed Applications". They add support for piggyback at MPI
implementation level and report very low overheads (no surprise).
On Feb 1, 2008 5:08 PM, Aurélien Bouteiller <bouteill_at_[hidden]> wrote:
> I don't know of any work in that direction for now. Indeed, we plan to
> eventually integrate at least causal message logging in the pml-v,
> which also includes piggybacking. Therefore we are open for
> collaboration with you on this matter. Please let us know :)
> Le 1 févr. 08 à 09:51, Thomas Ropars a écrit :
> > Hi,
> > I'm currently working on optimistic message logging and I would like
> > to
> > implement an optimistic message logging protocol in OpenMPI.
> > Optimistic
> > message logging protocols piggyback information about dependencies
> > between processes on the application messages to be able to find a
> > consistent global state after a failure. That's why I'm interested in
> > the problem of piggybacking information on MPI messages.
> > Is there some works on this problem at the moment ?
> > Has anyone already implemented some mechanisms in OpenMPI to piggyback
> > data on MPI messages?
> > Regards,
> > Thomas
> > Oleg Morajko wrote:
> >> Hi,
> >> I'm developing a causality chain tracking library and need a
> >> mechanism
> >> to attach an extra data to every MPI message, so called piggyback
> >> mechanism.
> >> As far as I know there are a few solutions to this problem from which
> >> the two fundamental ones are the following:
> >> * Dynamic datatype wrapping - if a user MPI_Send, let's say 1024
> >> doubles, the wrapped send call implementation dynamically
> >> creates a derived datatype that is a structure composed of a
> >> pointer to 1024 doubles and extra fields to be piggybacked. The
> >> datatype is constructed with absolute addresses to avoid copying
> >> the original buffer. The receivers side creates the equivalent
> >> datatype to receive the original data and extra data. The
> >> performance of this solution depends on the how good is derived
> >> data type handling, but seems to be lightweight.
> >> * Sending extra data in a separate message -- seems this can have
> >> much more significant overhead
> >> Do you know any other portable solution?
> >> I have implemented the first solution for P2P operations and it works
> >> pretty well. However there are problems with collective operations.
> >> There are 2 classes of collective calls that are problematic:
> >> 1. Single receiver calls, like MPI_Gather. The sender tasks in
> >> gather can be handled in the same way as a normal send, a data
> >> item is wrapped and extra data is piggybacked with the message.
> >> The problem is at the receiver side when a root gathers N data
> >> items that must be received in an array big enough to receive
> >> all items strided by datatype extent.
> >> In particular, it seems impossible to construct a datatype that
> >> contains data item and extra data (i.e. structure type with
> >> absolute addresses) AND make an array of these datatypes
> >> separated by a fixed extent. For example: data item to receive
> >> from every process is a vector of 1024 doubles. Extra data is a
> >> single integer. User provides a receive buffer with place for N
> >> * 1024 * double. The library allocates an array of N integers to
> >> receive piggybacked data. How to construct a datatype that can
> >> be used to receive data in MPI_Gather?
> >> 2. MPI_Reduce calls. There is no problem with datatypes as the
> >> receiver gets the single data item and not an array as in
> >> previous case. The problem is the reduction operator itself
> >> (MPI_Op) because these operators do not work with wrapped data
> >> types. So I can create a new operator to recognize the wrapped
> >> data type that extracts the original data (skipping extra data)
> >> and performs the original reduction. The point is how to invoke
> >> the original reduction on an existing datatype. I have found
> >> that Open MPI calls internally ompi_op_reduce(op, inbuf, rbuf,
> >> count, dtype) this solves a problem. However this makes the code
> >> MPI-implementation dependent. Any idea on more portable options?
> >> Thank you in advance for any comment.
> >> --Oleg
> >> _______________________________________________
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> Dr. Aurélien Bouteiller
> Sr. Research Associate - Innovative Computing Laboratory
> Suite 350, 1122 Volunteer Boulevard
> Knoxville, TN 37996
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