wraprun is a utility that enables independent execution of multiple MPI
applications under a single aprun call.
wraprun includes a Smithy formula to automate deployment, for centers not
using Smithy the build looks as follow:
$ mkdir build
$ cmake -DCMAKE_INSTALL_PREFIX=/path/to/install ..
$ make
$ make install
Inside of /path/to/install a bin directory will be created containing the
wraprun scripts and a lib directory will be created containing
libsplit.so. The WRAPRUN_PRELOAD environment variable must be correctly set
to point to libsplit.so and in the case of fortran applications
libfmpich.so at runtime. e.g.
WRAPRUN_PRELOAD=/path/to/install/lib/libsplit.so:/path/to/mpi_install/lib/libfmpich.so
On some systems libfmpich has a programming environment specific suffix that
must be taken into account: e.g.
WRAPRUN_PRELOAD=/path/to/install/lib/libsplit.so:/path/to/mpi_install/lib/libfmpich_pgi.so
Assuming that the module file created by the Smithy formula is used, or a similar one created, basic running looks like the following examples.
$ module load python wraprun
$ wraprun -n 80 ./foo.out : -n 160 ./bar.out ...
A maximum of 2048 separate : separated task groups is enforced to protect
ALPS stability.
In addition to the standard process placement flags available to aprun the
--w-cd flag can be set to change the current working directory for each
executable:
$ wraprun -n 80 --w-cd /foo/dir ./foo.out : -n 160 --w-cd /bar/dir ./bar.out ...
This is particularly useful for legacy Fortran applications that use hard coded input and output file names.
Multiple instances of an application can be placed on a node using
comma-separated PES syntax PES1,PES2,...,PESN syntax, for instance:
$ wraprun -n 2,2,2 ./foo.out : ...
would launch 3 two-process instances of foo.out on a single node.
In this case the number of allocated nodes must be at least equal to the sum of processes in the comma-separated list of processing elements divided by the maximum number of processes per node.
This may also be combined with the --w-cd flag :
$ wraprun -n 2,2,2 --w-cd /foo/dir1,/foo/dir2,/foo/dir3 ./foo.out : ...
For non MPI executables a wrapper application, serial, is provided. This
wrapper ensures that all executables will run to completion before aprun exits.
To use, place serial in front of your application and arguments:
$ wraprun -n 1 serial ./foo.out -foo_args : ...
The stdout/stderr streams for each task are directed to a unique file for
that task since it is generally undesirable for the standard output of all
tasks to be merged into a single stream. By default, each task writes their
own streams to files in the task's own current working directory named:
${JOBNAME}.${JOBID}_w${INSTANCE}.${TASKID}.out
${JOBNAME}.${JOBID}_w${INSTANCE}.${TASKID}.err
where JOBNAME is the batch job name (value of $PBS_JOBNAME for instance),
JOBID is the batch job number (or PID of parent shell if $PBS_JOBID is
unavailable), INSTANCE is the unique wraprun invocation called within the
parent shell, and TASKID is the task index among all bundled tasks. The
instance index is required so that multiple concurrent wraprun invocations in a
single batch job do not collide with each other. The task index is fixed in the
order that tasks are passed to wraprun such that for the following invocation:
$ wraprun -n 1,2 ./foo.out : -n 3 ./bar.out
task '0' is the instance of foo.out having 1 PE; task '1' is the 2 PE split of
foo.out, and task '2' is the instance of bar.out.
In many cases, it is desirable to change the default standard output/error file names.
The default names can be overridden by supplying a basename path to the group
flag --w-oe:
$ wraprun -n 1,2 --w-oe name_a ./a.out : \
-n 1,2 --w-oe name_b1,name_b2 ./b.out : \
-n 1 --w-oe name_c1,sub/name_c2 ./c.out : \
-n 1,1 ./d.out : \
-n 1 ./e.out
The names can be either relative or absolute paths to an aprun-writable
location. Relative names are taken from the task's CWD. Wraprun will not
create missing directories. Wraprun will add .out and .err extensions to
the given path for each stream.
When ambiguous names are provided as for a.out in the above example, wraprun
will add _w${SPLITID} to the given path for each task split. To avoid this, a
specific filename can be given to each task as a comma-separated list for each
group split. The output of the above example would then be
.
├── name_a_w0.err
├── name_a_w0.out
├── name_a_w1.err
├── name_a_w1.out
├── name_b1.err
├── name_b1.out
├── name_b2.err
├── name_b2.out
├── name_c1.err
├── name_c1.out
├── sub/
│ ├── name_c2.err
│ └── name_c2.out
├── nameofbatchjob.123456._w0.6.err
├── nameofbatchjob.123456._w0.6.out
├── nameofbatchjob.123456._w0.7.err
├── nameofbatchjob.123456._w0.7.out
├── nameofbatchjob.123456._w0.8.err
└── nameofbatchjob.123456._w0.8.out
WARNING: Under no circumstances can standard shell redirection (>) be
used to separate the stdout and stderr streams of individual task groups. In
other words, the following is a shell syntax error and will not produce the
desired outcome:
# This will fail - redirection operators in the middle of
# a command incantation is a syntax error.
wraprun -n 1 ./a.out > a.log : -n 1 ./b.out > b.log
Wraprun version 0.2.1 introduces a minimal API that can be used to bundle and launch tasks programatically via python scripts.
To use the API, load the wraprun environment module and import the wraprun
python module into your script:
#!/usr/bin/env python
import wraprun
bundle = wraprun.Wraprun()
# Add tasks by single strings as would be passed to the commandline interface between `:`'s.
bundle.add_task('-n 2,4,6 --w-cd ./a,./b,./c -cc 2 -ss ./bin_a -cc')
bundle.add_task('-n 3,5,7 ./bin_b input_b')
# Add tasks by passing parameters as keyword arguments.
bundle.add_task(pes=[1,2,3], pes_per_node=5, exe='./bin_c with args', cd=['./aa', './bb','./cc'])
# Launch the job on the compute nodes (starts the `aprun` subprocess).
bundle.launch()
The Wraprun constructor accepts keyword arguments for global options. The two
principle options are conf for passing a configuration file path (see below)
or running in debug mode which does not launch a job.
wraprun.Wraprun(conf='/path/to/conf.yml')
wraprun.Wraprun(debug=True)
The add_task method adds a new group to the bundle. It accepts either a string
as would be passed via the command-line utility or keyword arguments for each
group option.
| Parameter | CLI Flag | API keyword | API format |
|---|---|---|---|
| Task working directory | --w-cd | 'cd' | str or [str,...] |
| Task stdout/stderr file basename | --w-oe | 'oe' | str or [str,...] |
| Number of processing elements (PEs). REQUIRED | -n | 'pes' | int or [int,...] |
| Host architecture | -a | 'arch' | str |
| CPU list | -cc | 'cpu_list' | str |
| CPU placement file | -cp | 'cpu_placement' | str |
| Process affinity depth | -d | 'depth' | int |
| CPUs per CU | -j | 'cpus_per_cu' | int |
| Node list | -L | 'node_list' | str |
| PEs per node | -N | 'pes_per_node' | int |
| PEs per NUMA node | -S | 'pes_per_numa_node' | int |
| NUMA node list | -sl | 'numa_node_list' | int |
| Number of NUMA nodes per node | -sn | 'numa_nodes_per_node' | int |
| Use strict memory containment | -ss | 'strict_memory' | bool |
| Executable and argument string. REQUIRED | 'exe' | str |
The launch method launches an aprun subprocess call for the added tasks.
Bundle five instances of the same program.
from wraprun import Wraprun
bundle = Wraprun()
for _ in range(5):
bundle.add_task('-n 1 a.out')
bundle.launch()Loop over many similar input files organized in different directories, with customized stdout/stderr filenames for each task:
from os import environ
from wraprun import Wraprun
dir_template = './run_{run_id}x{ranks:02}'
log_template = 'run_{run_id}x{ranks:02}.log.{pbs_job}'.format(pbs_job=environ.get('PBS_JOBID', 'none'))
ranks_list = [5, 10, 15]
binary_path = '/ccs/proj/stf007/bin/my_executable'
input_template = '{binary} data_{run_id}.dat'
bundle = Wraprun()
for run_id in range(5):
bundle.add_task(
pes=ranks_list,
pes_per_node=5,
cd=[dir_template.format(run_id=run_id, ranks=ranks) for ranks in ranks_list],
oe=[log_template.format(run_id=run_id, ranks=ranks) for ranks in ranks_list],
exe=input_template.format(binary=binary_path, run_id=run_id)
)
bundle.launch()This is equivalent to the following command line call, but is arguably easier to read and certainly easier to edit, maintain, and scale to larger numbers of runs.
wraprun -n 5,10,15 -N 5 --w-cd './run_0x05,./run_0x10,./run_0x15' --w-oe "run_0x05.log.${PBS_JOBID},run_0x10.log.${PBS_JOBID},run_0x15.log.${PBS_JOBID}" /ccs/proj/stf007/bin/my_executable data_0.dat : \
-n 5,10,15 -N 5 --w-cd './run_1x05,./run_1x10,./run_1x15' --w-oe "run_1x05.log.${PBS_JOBID},run_1x10.log.${PBS_JOBID},run_1x15.log.${PBS_JOBID}" /ccs/proj/stf007/bin/my_executable data_1.dat : \
-n 5,10,15 -N 5 --w-cd './run_2x05,./run_2x10,./run_2x15' --w-oe "run_2x05.log.${PBS_JOBID},run_2x10.log.${PBS_JOBID},run_2x15.log.${PBS_JOBID}" /ccs/proj/stf007/bin/my_executable data_2.dat : \
-n 5,10,15 -N 5 --w-cd './run_3x05,./run_3x10,./run_3x15' --w-oe "run_3x05.log.${PBS_JOBID},run_3x10.log.${PBS_JOBID},run_3x15.log.${PBS_JOBID}" /ccs/proj/stf007/bin/my_executable data_3.dat : \
-n 5,10,15 -N 5 --w-cd './run_4x05,./run_4x10,./run_4x15' --w-oe "run_4x05.log.${PBS_JOBID},run_4x10.log.${PBS_JOBID},run_4x15.log.${PBS_JOBID}" /ccs/proj/stf007/bin/my_executable data_4.datBoth the command line tool and the API can accept a YAML format configuration
file for specifing tasks. See the command line utility help documentation
wraprun --help for CLI usage.
See the testing/example_config.yaml file for format information.
-
It is recommended that applications be dynamically linked.
- On Titan this can be accomplished by loading the dynamic-link module before invoking the Cray compile wrappers
CC,cc,ftn. - The library may be statically linked although this is not fully supported.
- On Titan this can be accomplished by loading the dynamic-link module before invoking the Cray compile wrappers
-
All executables must reside in a compute node visible filesystem, e.g. Lustre. Executables will not be copied as they normally are.
wraprun works by intercepting all MPI function calls that contain an
MPI_Comm argument. If an application calls an MPI function, containing an
MPI_Comm argument, not included in src/split.c the results are undefined.
If any executable is not dynamically linked the results are undefined.
If you spot a bug or missing functionality please submit a pull request!
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