11.12. General Tuning
11.12.1. What is the Modular Component Architecture (MCA)?
The Modular Component Architecture (MCA) is the backbone for much of Open MPI’s functionality. It is a series of projects, frameworks, components, and modules that are assembled at run-time to create an MPI implementation.
Projects: An Open MPI project is essentially the highest abstraction layer division of code.
Note
The word “project” is unfortunately overloaded. It can be used to mean the code/resources/people in the greater Open MPI community associated with the development of a particular software package, but it can also be used to mean a section of code within the Open MPI code base.
For the purposes of this documentation, “project” means the latter: a section of code within the Open MPI code base.
Frameworks: An MCA framework manages zero or more components at run-time and is targeted at a specific task (e.g., providing MPI collective operation functionality). Each MCA framework supports a single component type, but may support multiple versions of that type. The framework uses the services from the MCA base functionality to find and/or load components.
Components: An MCA component is an implementation of a framework’s interface. It is a standalone collection of code that can be bundled into a plugin that can be inserted into the Open MPI code base, either at run-time and/or compile-time.
Modules: An MCA module is an instance of a component (in the C++ sense of the word “instance”; an MCA component is analogous to a C++ class). For example, if a node running an Open MPI application has multiple ethernet NICs, the Open MPI application will contain one TCP MPI point-to-point component, but two TCP point-to-point modules.
11.12.2. What are MCA parameters?
MCA parameters are the basic unit of run-time tuning for Open MPI. They are simple “key = value” pairs that are used extensively throughout the code base. The general rules of thumb that the developers use are:
Instead of using a constant for an important value, make it an MCA parameter.
If a task can be implemented in multiple, user-discernible ways, implement as many as possible and make choosing between them be an MCA parameter.
For example, an easy MCA parameter to describe is the boundary between short and long messages in TCP wire-line transmissions. “Short” messages are sent eagerly whereas “long” messages use a rendezvous protocol. The decision point between these two protocols is the overall size of the message (in bytes). By making this value an MCA parameter, it can be changed at run-time by the user or system administrator to use a sensible value for a particular environment or set of hardware (e.g., a value suitable for 1Gpbs Ethernet is probably not suitable for 100 Gigabit Ethernet, and may require even a third different value for 40 Gigabit Ethernet).
Note that MCA parameters may be set in several different ways (described in another FAQ entry). This allows, for example, system administrators to fine-tune the Open MPI installation for their hardware / environment such that normal users can simply use the default values.
More specifically, HPC environments — and the applications that run on them — tend to be unique. Providing extensive run-time tuning capabilities through MCA parameters allows the customization of Open MPI to each system’s / user’s / application’s particular needs.
11.12.3. What projects are included in the Open MPI code base?
The following projects exist in Open MPI head of development:
Open Porability Access Layer (OPAL): Low-level, operating system and architecture portability code.
Open MPI (OMPI): The MPI API and supporting infrastructure.
OpenSHMEM (OSHMEM): The OpenSHMEM API and supporting infrastructure.
Note
Prior versions of Open MPI also included an Open MPI Runtime Envionrment (ORTE) project. ORTE essentially evolved into the standalone PMIx Runtime Reference Environment (PRRTE), and is now considered a 3rd-party dependency of Open MPI – not one of its included projects.
11.12.4. What frameworks are in Open MPI?
Each project has its own frameworks.
Error
TODO This question may be moot due to this list already in the higher-level doc.
11.12.5. How do I know what components are in my Open MPI installation?
The ompi_info command, in addition to providing a wealth of
configuration information about your Open MPI installation, will list
all components (and the frameworks that they belong to) that are
available. These include system-provided components as well as
user-provided components.
Please note that starting with Open MPI v1.8, ompi_info categorizes its
parameter parameters in so-called levels, as defined by the MPI_T
interface. You will need to specify --level 9 (or
--all) to show all MCA parameters.
See this Cisco Blog entry
for further information.
11.12.6. How do I install my own components into an Open MPI installation?
By default, Open MPI looks in two places for components at run-time (in order):
$prefix/lib/openmpi/: This is the system-provided components directory, part of the installation tree of Open MPI itself.$HOME/.openmpi/components/: This is where users can drop their own components that will automatically be “seen” by Open MPI at run-time. This is ideal for developmental, private, or otherwise unstable components.
Note that the directories and search ordering used for finding
components in Open MPI is, itself, an MCA parameter. Setting the
mca_component_path changes this value (a colon-delimited list of
directories).
Note also that components are only used on nodes where they are
“visible”. Hence, if your $prefix/lib/openmpi/ is a directory on a
local disk that is not shared via a network filesystem to other nodes
where you run MPI jobs, then components that are installed to that
directory will only be used by MPI jobs running on the local node.
More specifically: components have the same visibility as normal files. If you need a component to be available to all nodes where you run MPI jobs, then you need to ensure that it is visible on all nodes (typically either by installing it on all nodes for non-networked filesystem installs, or by installing them in a directory that is visible to all nodes via a networked filesystem). Open MPI does not automatically send components to remote nodes when MPI jobs are run.
11.12.7. How do I know what MCA parameters are available?
The ompi_info command can list the parameters for a given
component, all the parameters for a specific framework, or all
parameters. Most parameters contain a description of the parameter;
all will show the parameter’s current value.
For example, the following shows all the MCA parameters for all
components that ompi_info finds:
# Starting with Open MPI v1.7, you must use "--level 9" to see
# all the MCA parameters (the default is "--level 1"):
shell$ ompi_info --param all all --level 9
# Before Open MPI v1.7, the "--level" command line options
# did not exist; do not use it.
shell$ ompi_info --param all all
This example shows all the MCA parameters for all BTL components that
ompi_info finds:
# All remaining examples assume Open MPI v1.7 or later (i.e.,
# they assume the use of the "--level" command line option)
shell$ ompi_info --param btl all --level 9
This example shows all the MCA parameters for the TCP BTL component:
shell$ ompi_info --param btl tcp --level 9
11.12.8. How do I set the value of MCA parameters?
There are multiple ways to set MCA parameters, each of which are listed below, and are resolved in the following priority order:
Command line: The highest-precedence method is setting MCA parameters on the command line. For example:
shell$ mpirun --mca mpi_show_handle_leaks 1 -n 4 a.out
This sets the MCA parameter
mpi_show_handle_leaksto the value of 1 before runninga.outwith four processes. In general, the format used on the command line is--mca <param_name> <value>.Note that when setting multi-word values, you need to use quotes to ensure that the shell and Open MPI understand that they are a single value. For example:
shell$ mpirun --mca param "value with multiple words" ...Environment variable: Next, environment variables are searched. Any environment variable named
OMPI_MCA_<param_name>will be used. For example, the following has the same effect as the previous example (for sh-flavored shells):shell$ OMPI_MCA_mpi_show_handle_leaks=1 shell$ export OMPI_MCA_mpi_show_handle_leaks shell$ mpirun -n 4 a.out
Note that setting environment variables to values with multiple words requires quoting, such as:
shell$ OMPI_MCA_param="value with multiple words"
Tuning MCA parameter files: Simple text files can be used to set MCA parameter values for a specific application. See this FAQ entry for more details.
Aggregate MCA parameter files: Simple text files can be used to set MCA parameter values for a specific application. See this FAQ entry for more details.
Warning
The use of AMCA param files is deprecated.
Files: Finally, simple text files can be used to set MCA parameter values. Parameters are set one per line (comments are permitted). For example:
# This is a comment # Set the same MCA parameter as in previous examples mpi_show_handle_leaks = 1
Note that quotes are not necessary for setting multi-word values in MCA parameter files. Indeed, if you use quotes in the MCA parameter file, they will be used as part of the value itself. For example:
# The following two values are different: param1 = value with multiple words param2 = "value with multiple words"
By default, two files are searched (in order):
$HOME/.openmpi/mca-params.conf: The user-supplied set of values takes the highest precedence.$prefix/etc/openmpi-mca-params.conf: The system-supplied set of values has a lower precedence.
More specifically, the MCA parameter
mca_param_filesspecifies a colon-delimited path of files to search for MCA parameters. Files to the left have lower precedence; files to the right are higher precedence.Note
Keep in mind that, just like components, these parameter files are only relevant where they are “visible” (see this FAQ entry). Specifically, Open MPI does not read all the values from these files during startup and then send them to all nodes in the job — the files are read on each node during each process’ startup. This is intended behavior: it allows for per-node customization, which is especially relevant in heterogeneous environments.
11.12.9. What are Aggregate MCA (AMCA) parameter files?
Error
TODO This entire entry needs to be checked for correctness. Are AMCA files actually deprecated?
Warning
The use of AMCA param files is still available in Open MPI head of development, but is deprecated, and may disappear in a future version of Open MPI.
Aggregate MCA (AMCA) parameter files contain MCA parameter key/value
pairs similar to the $HOME/.openmpi/mca-params.conf file described
in this FAQ entry.
The motivation behind AMCA parameter sets came from the realization that certain applications require a large number of MCA parameters are to run well and/or execute as the user expects. Since these MCA parameters are application-specific (or even application-run-specific) they should not be set in a global manner, but only pulled in as determined by the user.
MCA parameters set in AMCA parameter files will override any MCA
parameters supplied in global parameter files (e.g.,
$HOME/.openmpi/mca-params.conf), but not command line or
environment parameters.
AMCA parameter files are typically supplied on the command line via
the --am option.
For example, consider an AMCA parameter file called foo.conf
placed in the same directory as the application a.out. A user
will typically run the application as:
shell$ mpirun -n 2 a.out
To use the foo.conf AMCA parameter file, this command line
changes to:
shell$ mpirun -n 2 --am foo.conf a.out
If the user wants to override a parameter set in foo.conf they
can add it to the command line:
shell$ mpirun -n 2 --am foo.conf --mca btl tcp,self a.out
AMCA parameter files can be coupled if more than one file is to be
used. If we have another AMCA parameter file called bar.conf
that we want to use, we add it to the command line as follows:
shell$ mpirun -n 2 --am foo.conf:bar.conf a.out
AMCA parameter files are loaded in priority order. This means that
foo.conf AMCA file has priority over the bar.conf file. So
if the bar.conf file sets the MCA parameter
mpi_leave_pinned=0 and the foo.conf file sets this MCA
parameter to mpi_leave_pinned=1 then the latter will be used.
The location of AMCA parameter files are resolved in a similar way as the shell:
If no path operator is provided (i.e.,
foo.conf), then Open MPI will search the$sysconfdir/amca-param-setsdirectory, then the current working directory.If a relative path is specified, then only that path will be searched (e.g.,
./foo.conf,baz/foo.conf).If an absolute path is specified, then only that path will be searched (e.g.,
/bip/boop/foo.conf).
Although the typical use case for AMCA parameter files is to be
specified on the command line, they can also be set as MCA parameters
in the environment. The MCA parameter mca_base_param_file_prefix
contains a :-delimited list of AMCA parameter files exactly as
they would be passed to the --am command line option. The MCA
parameter mca_base_param_file_path specifies the path to search
for AMCA files with relative paths. By default this is
$sysconfdir/amca-param-sets/:$CWD.
11.12.10. How do I set application specific environment variables in global parameter files?
Error
TODO This entire entry needs to be checked for correctness.
The mpirun --tune CLI options allows users to specify both MCA
parameters and environment variables from within a single file.
MCA parameters set in tuned parameter files will override any MCA
parameters supplied in global parameter files (e.g.,
$HOME/.openmpi/mca-params.conf), but not command line or
environment parameters.
Tuned parameter files are typically supplied on the command line via
the --tune option.
For example, consider an tuned parameter file called foo.conf
placed in the same directory as the application a.out. A user
will typically run the application as:
shell$ mpirun -n 2 a.out
To use the foo.conf tuned parameter file, this command line
changes to:
shell$ mpirun -n 2 --tune foo.conf a.out
Tuned parameter files can be coupled if more than one file is to be
used. If we have another tuuned parameter file called bar.conf
that we want to use, we add it to the command line as follows:
shell$ mpirun -n 2 --tune foo.conf,bar.conf a.out
The contents of tuned files consist of one or more lines, each of which contain zero or more -x and –mca options. Comments are not allowed. For example, the following tuned file:
-x envvar1=value1 -mca param1 value1 -x envvar2
-mca param2 value2
-x envvar3
is equivalent to:
shell$ mpirun \
-x envvar1=value1 -mca param1 value1 -x envvar2 \
-mca param2 value2
-x envvar3 \
...rest of mpirun command line...
Although the typical use case for tuned parameter files is to be
specified on the command line, they can also be set as MCA parameters
in the environment. The MCA parameter mca_base_envvar_file_prefix
contains a ,-delimited list of tuned parameter files exactly as
they would be passed to the --tune command line option. The MCA
parameter mca_base_envvar_file_path specifies the path to search
for tune files with relative paths.
Error
TODO Check that these MCA var names ^^ are correct.
11.12.11. How do I select which components are used?
Each MCA framework has a top-level MCA parameter that helps guide which components are selected to be used at run-time. Specifically, there is an MCA parameter of the same name as each MCA framework that can be used to include or exclude components from a given run.
For example, the btl MCA parameter is used to control which BTL
components are used (e.g., MPI point-to-point communications;
see the MCA frameworks listing for a full
listing). It can take as a value a comma-separated list of components
with the optional prefix ^. For example:
# Tell Open MPI to exclude the tcp and uct BTL components
# and implicitly include all the rest
shell$ mpirun --mca btl ^tcp,uct ...
# Tell Open MPI to include *only* the components listed here and
# implicitly ignore all the rest (i.e., the loopback, shared memory,
# etc.) MPI point-to-point components):
shell$ mpirun --mca btl self,sm,usnic ...
Note that ^ can only be the prefix of the entire value because
the inclusive and exclusive behavior are mutually exclusive.
Specifically, since the exclusive behavior means “use all components
except these”, it does not make sense to mix it with the inclusive
behavior of not specifying it (i.e., “use all of these components”).
Hence, something like this:
shell$ mpirun --mca btl self,sm,usnic,^tcp ...
does not make sense because it says both “use only the self, sm,
and usnic components” and “use all components except tcp” and
will result in an error.
Just as with all MCA parameters, the btl parameter (and all
framework parameters) can be set in multiple ways.
11.12.12. What is processor affinity? Does Open MPI support it?
Open MPI supports processor affinity on a variety of systems through process binding, in which each MPI process, along with its threads, is “bound” to a specific subset of processing resources (cores, packages, etc.). That is, the operating system will constrain that process to run on only that subset.
Note
The operating system may allow other processes to run on the same resources.
Affinity can improve performance by inhibiting excessive process movement — for example, away from “hot” caches or NUMA memory. Judicious bindings can improve performance by reducing resource contention (by spreading processes apart from one another) or improving interprocess communications (by placing processes close to one another). Binding can also improve performance reproducibility by eliminating variable process placement.
Warning
Processor affinity probably should not be used when a node is over-subscribed (i.e., more processes are launched than there are processors).
This can lead to a serious degradation in performance (even more than simply oversubscribing the node). Open MPI will usually detect this situation and automatically disable the use of processor affinity (and display run-time warnings to this effect).
11.12.13. What is memory affinity? Does Open MPI support it?
Memory affinity is increasingly relevant on modern servers because most architectures exhibit Non-Uniform Memory Access (NUMA) architectures. In a NUMA architecture, memory is physically distributed throughout the machine even though it is virtually treated as a single address space. That is, memory may be physically local to one or more processors — and therefore remote to other processors.
Simply put: some memory will be faster to access (for a given process) than others.
Open MPI supports general and specific memory affinity, meaning that it generally tries to allocate all memory local to the processor that asked for it. When shared memory is used for communication, Open MPI uses memory affinity to make certain pages local to specific processes in order to minimize memory network/bus traffic.
Open MPI supports memory affinity on a variety of systems.
In recent versions of Open MPI, memory affinity is controlled through the Hardware Locality (hwloc) library.
Note that memory affinity support is enabled only when processor affinity is enabled. Specifically: using memory affinity does not make sense if processor affinity is not enabled because processes may allocate local memory and then move to a different processor, potentially remote from the memory that it just allocated.
11.12.14. How do I tell Open MPI to use processor and/or memory affinity?
Open MPI will, by default, enable processor and memory affinity when not running in an oversubscribed environment (i.e., when the number of MPI processes are less than or equal two the number of processors available).
The mpirun(1) man page for each version of Open MPI contains a lot of
information about the use of processor and memory affinity. You
should consult the mpirun(1) page for your version of Open MPI for
detailed information about processor/memory affinity.
Error
TODO Link to mpirun(1) …?
11.12.15. Does Open MPI support calling fork(), system(), or popen() in MPI processes?
It depends on a lot of factors, including (but not limited to):
The operating system
The underlying compute hardware
The network stack
Interactions with other middleware in the MPI process
In some cases, Open MPI will determine that it is not safe to
fork(). In these cases, Open MPI will register a
pthread_atfork() callback to print a warning when the process
forks.
This warning is helpful for legacy MPI applications where the current
maintainers are unaware that system() or popen() is being invoked from
an obscure subroutine nestled deep in millions of lines of Fortran code
(we’ve seen this kind of scenario many times).
However, this atfork handler can be dangerous because there is no way to unregister an atfork handler. Hence, packages that dynamically open Open MPI’s libraries (e.g., Python bindings for Open MPI) may fail if they finalize and unload libmpi, but later call fork. The atfork system will try to invoke Open MPI’s atfork handler; nothing good can come of that.
For such scenarios, or if you simply want to disable printing the
warning, Open MPI can be set to never register the atfork handler with
the mpi_warn_on_fork MCA parameter. For example:
shell$ mpirun --mca mpi_warn_on_fork 0 ...
Of course, systems that dlopen("libmpi.so", ...) may not use Open
MPI’s mpirun, and therefore may need to use a different
mechanism to set MCA parameters.
11.12.16. I want to run some performance benchmarks with Open MPI. How do I do that?
Running benchmarks is an extremely difficult task to do correctly. There are many, many factors to take into account; it is not as simple as just compiling and running a stock benchmark application. This documentation is by no means a definitive guide, but it does try to offer some suggestions for generating accurate, meaningful benchmarks.
Decide exactly what you are benchmarking and setup your system accordingly. For example, if you are trying to benchmark maximum performance, then many of the suggestions listed below are extremely relevant (be the only user on the systems and network in question, be the only software running, use processor affinity, etc.). If you’re trying to benchmark average performance, some of the suggestions below may be less relevant. Regardless, it is critical to know exactly what you’re trying to benchmark, and know (not guess) both your system and the benchmark application itself well enough to understand what the results mean.
To be specific, many benchmark applications are not well understood for exactly what they are testing. There have been many cases where users run a given benchmark application and wrongfully conclude that their system’s performance is bad — solely on the basis of a single benchmark that they did not understand. Read the documentation of the benchmark carefully, and possibly even look into the code itself to see exactly what it is testing.
Case in point: not all ping-pong benchmarks are created equal. Most users assume that a ping-pong benchmark is a ping-pong benchmark is a ping-pong benchmark. But this is not true; the common ping-pong benchmarks tend to test subtly different things (e.g., NetPIPE, TCP bench, IMB, OSU, etc.). Make sure you understand what your benchmark is actually testing.
Make sure that you are the only user on the systems where you are running the benchmark to eliminate contention from other processes.
Make sure that you are the only user on the entire network / interconnect to eliminate network traffic contention from other processes. This is usually somewhat difficult to do, especially in larger, shared systems. But your most accurate, repeatable results will be achieved when you are the only user on the entire network.
Disable all services and daemons that are not being used. Even “harmless” daemons consume system resources (such as RAM) and cause “jitter” by occasionally waking up, consuming CPU cycles, reading or writing to disk, etc. The optimum benchmark system has an absolute minimum number of system services running.
Ensure that processor and memory affinity are properly utilized to disallow the operating system from swapping MPI processes between processors (and causing unnecessary cache thrashing, for example).
Warning
On NUMA architectures, having the processes getting bumped from one socket to another is more expensive in terms of cache locality (with all of the cache coherency overhead that comes with the lack of it) than in terms of memory transfer routing (see below).
Be sure to understand your system’s architecture, particularly with respect to the memory, disk, and network characteristics, and test accordingly. For example, on NUMA architectures, memory accesses may be routed through a memory interconnect; remote device and/or memory accesses will be noticeably slower than local device and/or memory accesses.
Compile your benchmark with the appropriate compiler optimization flags. With some MPI implementations, the compiler wrappers (like
mpicc,mpifort, etc.) add optimization flags automatically. Open MPI does not. Add-Oor other flags explicitly.Make sure your benchmark runs for a sufficient amount of time. Short-running benchmarks are generally less accurate because they take fewer samples; longer-running jobs tend to take more samples.
If your benchmark is trying to benchmark extremely short events (such as the time required for a single ping-pong of messages):
Perform some “warmup” events first. Many MPI implementations (including Open MPI) — and other subsystems upon which the MPI uses — may use “lazy” semantics to setup and maintain streams of communications. Hence, the first event (or first few events) may well take significantly longer than subsequent events.
Use a high-resolution timer if possible —
gettimeofday()only returns millisecond precision (sometimes on the order of several microseconds).Run the event many, many times (hundreds or thousands, depending on the event and the time it takes). Not only does this provide more samples, it may also be necessary, especially when the precision of the timer you’re using may be several orders of magnitude less precise than the event you’re trying to benchmark.
Decide whether you are reporting minimum, average, or maximum numbers, and have good reasons why.
Accurately label and report all results. Reproducibility is a major goal of benchmarking; benchmark results are effectively useless if they are not precisely labeled as to exactly what they are reporting. Keep a log and detailed notes about the ‘’exact’’ system configuration that you are benchmarking. Note, for example, all hardware and software characteristics (to include hardware, firmware, and software versions as appropriate).
11.12.17. I am getting a MPI_WIN_FREE error from IMB-EXT — what do I do?
When you run IMB-EXT with Open MPI, you’ll see a message like this:
[node01.example.com:2228] *** An error occurred in MPI_Win_free
[node01.example.com:2228] *** on win
[node01.example.com:2228] *** MPI_ERR_RMA_SYNC: error while executing rma sync
[node01.example.com:2228] *** MPI_ERRORS_ARE_FATAL (your MPI job will now abort)
This is due to a bug in the Intel MPI Benchmarks, known to be in at least versions v3.1 and v3.2. Intel was notified of this bug in May of 2009. If you have a version after then, the bug should be fixed. If not, here is the fix that you can apply to the IMB-EXT source code yourself.
Here is a small patch that fixes the bug in IMB v3.2:
diff -u imb-3.2-orig/src/IMB_window.c imb-3.2-fixed/src/IMB_window.c
--- imb-3.2-orig/src/IMB_window.c 2008-10-21 04:17:31.000000000 -0400
+++ imb-3.2-fixed/src/IMB_window.c 2009-07-20 09:02:45.000000000 -0400
@@ -140,6 +140,9 @@
c_info->rank, 0, 1, c_info->r_data_type,
c_info->WIN);
MPI_ERRHAND(ierr);
}
+ /* Added a call to MPI_WIN_FENCE, per MPI-2.1 11.2.1 */
+ ierr = MPI_Win_fence(0, c_info->WIN);
+ MPI_ERRHAND(ierr);
ierr = MPI_Win_free(&c_info->WIN);
MPI_ERRHAND(ierr);
}
And here is the corresponding patch for IMB v3.1:
Index: IMB_3.1/src/IMB_window.c
===================================================================
--- IMB_3.1/src/IMB_window.c(revision 1641)
+++ IMB_3.1/src/IMB_window.c(revision 1642)
@@ -140,6 +140,10 @@
c_info->rank, 0, 1, c_info->r_data_type, c_info->WIN);
MPI_ERRHAND(ierr);
}
+ /* Added a call to MPI_WIN_FENCE here, per MPI-2.1
+ 11.2.1 */
+ ierr = MPI_Win_fence(0, c_info->WIN);
+ MPI_ERRHAND(ierr);
ierr = MPI_Win_free(&c_info->WIN);
MPI_ERRHAND(ierr);
}