171 строка
4.6 KiB
Plaintext
171 строка
4.6 KiB
Plaintext
##################################
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# ML collective configuration file
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##################################
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# NOTE (by Pasha):
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# Since ML configuration infrastructure is limited on this stage we do not support some tunings, even so parser
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# understands this values and keys, but we do not have place to load all this values.
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# threshold - ML infrastructure does not handle multiple thresholds.
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# fragmentation - ML infrastructure does not fragmentation tuning per collective.
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##################################
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# Defining collective section
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[BARRIER]
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# 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
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<small>
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# Since ML does not define any algorithm for BARRIER, we just use default. Later we have to introduce some algorithm name for Barrier
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algorithm = ML_BARRIER_DEFAULT
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# Hierarchy setup:
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#
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# full_hr - means all possible levels of hierarchy (list of possible is defined by user command line)
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# full_hr_no_basesocket - means all possible levels of hierarchy (list of possible is defined by user command line)
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# except the basesocket subgroup.
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# ptp_only - only ptp hierarchy
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# iboffload_only - only iboffload hierarhcy
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hierarchy = full_hr
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[IBARRIER]
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<small>
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algorithm = ML_BARRIER_DEFAULT
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hierarchy = full_hr
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[BCAST]
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<small>
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# bcast supports: ML_BCAST_SMALL_DATA_KNOWN, ML_BCAST_SMALL_DATA_UNKNOWN, ML_BCAST_SMALL_DATA_SEQUENTIAL
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algorithm = ML_BCAST_SMALL_DATA_KNOWN
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hierarchy = full_hr
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<large>
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# bcast supports: ML_BCAST_LARGE_DATA_KNOWN, ML_BCAST_LARGE_DATA_UNKNOWN, ML_BCAST_LARGE_DATA_SEQUENTIAL
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algorithm = ML_BCAST_LARGE_DATA_KNOWN
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hierarchy = full_hr
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[IBCAST]
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<small>
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algorithm = ML_BCAST_SMALL_DATA_KNOWN
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hierarchy = full_hr
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<large>
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algorithm = ML_BCAST_LARGE_DATA_KNOWN
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hierarchy = full_hr
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[GATHER]
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<small>
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# gather supports: ML_SMALL_DATA_GATHER
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algorithm = ML_SMALL_DATA_GATHER
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hierarchy = full_hr
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<large>
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# gather supports: ML_LARGE_DATA_GATHER
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algorithm = ML_LARGE_DATA_GATHER
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hierarchy = full_hr
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[IGATHER]
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<small>
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# gather supports: ML_SMALL_DATA_GATHER
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algorithm = ML_SMALL_DATA_GATHER
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hierarchy = full_hr
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<large>
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# gather supports: ML_LARGE_DATA_GATHER
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algorithm = ML_LARGE_DATA_GATHER
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hierarchy = full_hr
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[ALLGATHER]
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<small>
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# allgather supports: ML_SMALL_DATA_ALLGATHER
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algorithm = ML_SMALL_DATA_ALLGATHER
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hierarchy = full_hr
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<large>
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# allgather supports: ML_LARGE_DATA_ALLGATHER
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algorithm = ML_LARGE_DATA_ALLGATHER
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hierarchy = full_hr
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[IALLGATHER]
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<small>
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# allgather supports: ML_SMALL_DATA_ALLGATHER
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algorithm = ML_SMALL_DATA_ALLGATHER
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hierarchy = full_hr
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<large>
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# allgather supports: ML_LARGE_DATA_ALLGATHER
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algorithm = ML_LARGE_DATA_ALLGATHER
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hierarchy = full_hr
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[ALLTOALL]
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<small>
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# alltoall supports: ML_SMALL_DATA_ALLTOALL
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algorithm = ML_SMALL_DATA_ALLTOALL
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hierarchy = ptp_only
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<large>
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# alltoall supports: ML_LARGE_DATA_ALLTOALL
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algorithm = ML_LARGE_DATA_ALLTOALL
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hierarchy = ptp_only
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[IALLTOALL]
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<small>
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# alltoall supports: ML_SMALL_DATA_ALLTOALL
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algorithm = ML_SMALL_DATA_ALLTOALL
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hierarchy = ptp_only
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<large>
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# alltoall supports: ML_LARGE_DATA_ALLTOALL
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algorithm = ML_LARGE_DATA_ALLTOALL
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hierarchy = ptp_only
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[ALLREDUCE]
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<small>
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# allreduce supports: ML_SMALL_DATA_ALLREDUCE
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algorithm = ML_SMALL_DATA_ALLREDUCE
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hierarchy = full_hr
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<large>
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# allreduce supports: ML_LARGE_DATA_ALLREDUCE
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algorithm = ML_LARGE_DATA_ALLREDUCE
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hierarchy = full_hr
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[IALLREDUCE]
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<small>
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# allreduce supports: ML_SMALL_DATA_ALLREDUCE
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algorithm = ML_SMALL_DATA_ALLREDUCE
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hierarchy = full_hr
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<large>
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# allreduce supports: ML_LARGE_DATA_ALLREDUCE
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algorithm = ML_LARGE_DATA_ALLREDUCE
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hierarchy = full_hr
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[REDUCE]
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<small>
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# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
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algorithm = ML_SMALL_DATA_REDUCE
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hierarchy = full_hr
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<large>
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# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
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algorithm = ML_LARGE_DATA_REDUCE
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hierarchy = full_hr
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[IREDUCE]
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<small>
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# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
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algorithm = ML_SMALL_DATA_REDUCE
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hierarchy = full_hr
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<large>
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# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
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algorithm = ML_LARGE_DATA_REDUCE
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hierarchy = full_hr
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[SCATTER]
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<small>
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# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
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algorithm = ML_SCATTER_SMALL_DATA_SEQUENTIAL
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hierarchy = full_hr
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<large>
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# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
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algorithm = ML_SCATTER_SMALL_DATA_SEQUENTIAL
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hierarchy = full_hr
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[ISCATTER]
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<small>
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# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
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algorithm = ML_SCATTER_SMALL_DATA_SEQUENTIAL
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hierarchy = full_hr
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<large>
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# scatter supports: ML_SCATTER_SMALL_DATA_SEQUENTIAL
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algorithm = ML_SCATTER_SMALL_DATA_SEQUENTIAL
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hierarchy = full_hr
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