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openmpi/ompi/mca/coll/ml/mca-coll-ml.config

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Adding Hierarchical Collectives project to the Open MPI trunk. The project includes following components and frameworks: - ML Collective component - NETPATTERNS and COMMPATTERNS common components - BCOL framework - SBGP framework Note: By default the ML collective component is disabled. In order to enable new collectives user should bump up the priority of ml component (coll_ml_priority) ============================================= Primary Contributors (in alphabetical order): Ishai Rabinovich (Mellanox) Joshua S. Ladd (ORNL / Mellanox) Manjunath Gorentla Venkata (ORNL) Mike Dubman (Mellanox) Noam Bloch (Mellanox) Pavel (Pasha) Shamis (ORNL / Mellanox) Richard Graham (ORNL / Mellanox) Vasily Filipov (Mellanox) This commit was SVN r27078.
2012-08-16 23:11:35 +04:00
##################################
# ML collective configuration file
##################################
# NOTE (by Pasha):
# Since ML configuration infrastructure is limited on this stage we do not support some tunings, even so parser
# understands this values and keys, but we do not have place to load all this values.
# threshold - ML infrastructure does not handle multiple thresholds.
# fragmentation - ML infrastructure does not fragmentation tuning per collective.
##################################
# Defining collective section
[BARRIER]
# Defining message size section. We will support small/large. In future we may add more options. Barrier is very specific case, because it is only collective that does not transfer any data, so for this specific case we use small
<small>
# Since ML does not define any algorithm for BARRIER, we just use default. Later we have to introduce some algorithm name for Barrier
algorithm = ML_BARRIER_DEFAULT
# Hierarchy setup:
#
# full_hr - means all possible levels of hierarchy (list of possible is defined by user command line)
# full_hr_no_basesocket - means all possible levels of hierarchy (list of possible is defined by user command line)
# except the basesocket subgroup.
# ptp_only - only ptp hierarchy
# iboffload_only - only iboffload hierarhcy
hierarchy = full_hr
[IBARRIER]
<small>
algorithm = ML_BARRIER_DEFAULT
hierarchy = full_hr
[BCAST]
<small>
# bcast supports: ML_BCAST_SMALL_DATA_KNOWN, ML_BCAST_SMALL_DATA_UNKNOWN, ML_BCAST_SMALL_DATA_SEQUENTIAL
algorithm = ML_BCAST_SMALL_DATA_KNOWN
hierarchy = full_hr
<large>
# bcast supports: ML_BCAST_LARGE_DATA_KNOWN, ML_BCAST_LARGE_DATA_UNKNOWN, ML_BCAST_LARGE_DATA_SEQUENTIAL
algorithm = ML_BCAST_LARGE_DATA_KNOWN
hierarchy = full_hr
[IBCAST]
<small>
algorithm = ML_BCAST_SMALL_DATA_KNOWN
hierarchy = full_hr
<large>
algorithm = ML_BCAST_LARGE_DATA_KNOWN
hierarchy = full_hr
[GATHER]
<small>
# gather supports: ML_SMALL_DATA_GATHER
algorithm = ML_SMALL_DATA_GATHER
hierarchy = full_hr
<large>
# gather supports: ML_LARGE_DATA_GATHER
algorithm = ML_LARGE_DATA_GATHER
hierarchy = full_hr
[IGATHER]
<small>
# gather supports: ML_SMALL_DATA_GATHER
algorithm = ML_SMALL_DATA_GATHER
hierarchy = full_hr
<large>
# gather supports: ML_LARGE_DATA_GATHER
algorithm = ML_LARGE_DATA_GATHER
hierarchy = full_hr
[ALLGATHER]
<small>
# allgather supports: ML_SMALL_DATA_ALLGATHER
algorithm = ML_SMALL_DATA_ALLGATHER
hierarchy = full_hr
<large>
# allgather supports: ML_LARGE_DATA_ALLGATHER
algorithm = ML_LARGE_DATA_ALLGATHER
hierarchy = full_hr
[IALLGATHER]
<small>
# allgather supports: ML_SMALL_DATA_ALLGATHER
algorithm = ML_SMALL_DATA_ALLGATHER
hierarchy = full_hr
<large>
# allgather supports: ML_LARGE_DATA_ALLGATHER
algorithm = ML_LARGE_DATA_ALLGATHER
hierarchy = full_hr
[ALLTOALL]
<small>
# alltoall supports: ML_SMALL_DATA_ALLTOALL
algorithm = ML_SMALL_DATA_ALLTOALL
hierarchy = ptp_only
<large>
# alltoall supports: ML_LARGE_DATA_ALLTOALL
algorithm = ML_LARGE_DATA_ALLTOALL
hierarchy = ptp_only
[IALLTOALL]
<small>
# alltoall supports: ML_SMALL_DATA_ALLTOALL
algorithm = ML_SMALL_DATA_ALLTOALL
hierarchy = ptp_only
<large>
# alltoall supports: ML_LARGE_DATA_ALLTOALL
algorithm = ML_LARGE_DATA_ALLTOALL
hierarchy = ptp_only
[ALLREDUCE]
<small>
# allreduce supports: ML_SMALL_DATA_ALLREDUCE
algorithm = ML_SMALL_DATA_ALLREDUCE
hierarchy = full_hr
<large>
# allreduce supports: ML_LARGE_DATA_ALLREDUCE
algorithm = ML_LARGE_DATA_ALLREDUCE
hierarchy = full_hr
[IALLREDUCE]
<small>
# allreduce supports: ML_SMALL_DATA_ALLREDUCE
algorithm = ML_SMALL_DATA_ALLREDUCE
hierarchy = full_hr
<large>
# allreduce supports: ML_LARGE_DATA_ALLREDUCE
algorithm = ML_LARGE_DATA_ALLREDUCE
hierarchy = full_hr
coll/ml: add support for blocking and non-blocking allreduce, reduce, and allgather. The new collectives provide a signifigant performance increase over tuned for small and medium messages. We are initially setting the priority lower than tuned until this has had some time to soak in the trunk. Please set coll_ml_priority to 90 for MTT runs. Credit for this work goes to Manjunath Gorentla Venkata (ORNL), Pavel Shamis (ORNL), and Nathan Hjelm (LANL). Commit details (for reference): Import ORNL's collectives for MPI_Allreduce, MPI_Reduce, and MPI_Allgather. We need to take the basesmuma header into account when calculating the ptpcoll small message thresholds. Add a define to bcol.h indicating the maximum header size so we can take the header into account while not making ptpcoll dependent on information from basesmuma. This resolves an issue with allreduce where ptpcoll overwrites the header of the next buffer in the basesmuma bank. Fix reduce and make a sequential collective launcher in coll_ml_inlines.h The root calculation for reduce was wrong for any root != 0. There are four possibilities for the root: - The root is not the current process but is in the current hierarchy. In this case the root is the index of the global root as specified in the root vector. - The root is not the current process and is not in the next level of the hierarchy. In this case 0 must be the local root since this process will never communicate with the real root. - The root is not the current process but will be in next level of the hierarchy. In this case the current process must be the root. - I am the root. The root is my index. Tested with IMB which rotates the root on every call to MPI_Reduce. Consider IMB the reproducer for the issue this commit solves. Make the bcast algorithm decision an enumerated variable Resolve various asset failures when destructing coll ml requests. Two issues: - Always reset the request to be invalid before returning it to the free list. This will avoid an asset in ompi_request_t's destructor. OMPI_REQUEST_FINI does this (and also releases the fortran handle index). - Never explicitly construct or destruct the superclass of an opal object. This screws up the class function tables and will cause either an assert failure or a segmentation fault when destructing coll ml requests. Cleanup allgather. I removed the duplicate non-blocking and blocking functions and modeled the cleanup after what I found in allreduce. Also cleaned up the code somewhat. Don't bother copying from the send to the recieve buffer in bcol_basesmuma_allreduce_intra_fanin_fanout if the pointers are the same. The eliminates a warning about memcpy and aliasing and avoids an unnecessary call to memcpy. Alwasy call CHECK_AND_RELEASE on memsync collectives. There was a call to OBJ_RELEASE on the collective communicator but because CHECK_AND_RECYLCE was never called there was not matching call to OBJ_RELEASE. This caused coll ml to leak communicators. Make allreduce use the sequential collective launcher in coll_ml_inlines.h Just launch the next collective in the component progress. I am a little unsure about this patch. There appears to be some sort of race between collectives that causes buffer exhaustion in some cases (IMB Allreduce is a reproducer). Changing progress to only launch the next bcol seems to resolve the issue but might not be the best fix. Note that I see little-no performance penalty for this change. Fix allreduce when there are extra sources. There was an issue with the buffer offset calculation when there are extra sources. In the case of extra sources == 1 the offset was set to buffer_size (just past the header of the next buffer). I adjusted the buffer size to take into accoun the maximum header size (see the earlier commit that added this) and simplified the offset calculation. Make reduce/allreduce non-blocking. This is required for MPI_Comm_idup to work correctly. This has been tested with various layouts using the ibm testsuite and imb and appears to have the same performance as the old blocking version. Fix allgather for non-contiguous layouts and simplify parsing the topology. Some things in this patch: - There were several comments to the effect that level 0 of the hierarchy MUST contain all of the ranks. At least one function made this assumption but it was not true. I changed the sbgp components and the coll ml initization code to enforce this requirement. - Ensure that hierarchy level 0 has the ranks in the correct scatter gather order. This removes the need for a separate sort list and fixes the offset calculation for allgather. - There were several passes over the hierarchy to determine properties of the hierarchy. I eliminated these extra passes and the memory allocation associated with them and calculate the tree properties on the fly. The same DFS recursion also handles the re-order of level 0. All these changes have been verified with MPI_Allreduce, MPI_Reduce, and MPI_Allgather. All functions now pass all IBM/Open MPI, and IMB tests. coll/ml: correct pointer usage for MPI_BOTTOM Since contiguous datatypes are copied via memcpy (bypassing the convertor) we need to adjust for the lb of the datatype. This corrects problems found testing code that uses MPI_BOTTOM (NULL) as the send pointer. Add fallback collectives for allreduce and reduce. cmr=v1.7.5:reviewer=pasha This commit was SVN r30363.
2014-01-22 19:39:19 +04:00
[REDUCE]
<small>
# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
algorithm = ML_SMALL_DATA_REDUCE
hierarchy = full_hr
<large>
# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
algorithm = ML_LARGE_DATA_REDUCE
hierarchy = full_hr
[IREDUCE]
<small>
# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
algorithm = ML_SMALL_DATA_REDUCE
hierarchy = full_hr
<large>
# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
algorithm = ML_LARGE_DATA_REDUCE
hierarchy = full_hr
Adding Hierarchical Collectives project to the Open MPI trunk. The project includes following components and frameworks: - ML Collective component - NETPATTERNS and COMMPATTERNS common components - BCOL framework - SBGP framework Note: By default the ML collective component is disabled. In order to enable new collectives user should bump up the priority of ml component (coll_ml_priority) ============================================= Primary Contributors (in alphabetical order): Ishai Rabinovich (Mellanox) Joshua S. Ladd (ORNL / Mellanox) Manjunath Gorentla Venkata (ORNL) Mike Dubman (Mellanox) Noam Bloch (Mellanox) Pavel (Pasha) Shamis (ORNL / Mellanox) Richard Graham (ORNL / Mellanox) Vasily Filipov (Mellanox) This commit was SVN r27078.
2012-08-16 23:11:35 +04:00
[SCATTER]
<small>
# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
algorithm = ML_SCATTER_SMALL_DATA_SEQUENTIAL
hierarchy = full_hr
<large>
# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
algorithm = ML_SCATTER_SMALL_DATA_SEQUENTIAL
hierarchy = full_hr
[ISCATTER]
<small>
# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
algorithm = ML_SCATTER_SMALL_DATA_SEQUENTIAL
hierarchy = full_hr
<large>
# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
algorithm = ML_SCATTER_SMALL_DATA_SEQUENTIAL
hierarchy = full_hr
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