/usr/share/openmpi/mca-coll-ml.config is in openmpi-common 1.10.2-8ubuntu1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | ##################################
# 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
[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
[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
|