SimGrid
3.14.159
Versatile Simulation of Distributed Systems
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A number of options can be given at runtime to change the default SimGrid behavior. For a complete list of all configuration options accepted by the SimGrid version used in your simulator, simply pass the –help configuration flag to your program. If some of the options are not documented on this page, this is a bug that you should please report so that we can fix it. Note that some of the options presented here may not be available in your simulators, depending on the compile-time options that you used.
There is several way to pass configuration options to the simulators. The most common way is to use the –cfg
command line argument. For example, to set the item Item
to the value Value
, simply type the following:
my_simulator --cfg=Item:Value (other arguments)
Several
command line arguments can naturally be used. If you need to include spaces in the argument, don't forget to quote the argument. You can even escape the included quotes (write \' for ' if you have your argument between ').--cfg
Another solution is to use the <config>
tag in the platform file. The only restriction is that this tag must occure before the first platform element (be it <AS>
, <cluster>
, <peer>
or whatever). The <config>
tag takes an id
attribute, but it is currently ignored so you don't really need to pass it. The important par is that within that tag, you can pass one or several <prop>
tags to specify the configuration to use. For example, setting Item
to Value
can be done by adding the following to the beginning of your platform file:
<config> <prop id="Item" value="Value"/> </config>
A last solution is to pass your configuration directly using the C interface. If you happen to use the MSG interface, this is very easy with the MSG_config() function. If you do not use MSG, that's a bit more complex, as you have to mess with the internal configuration set directly as follows. Check the relevant page for details on all the functions you can use in this context, _sg_cfg_set
being the only configuration set currently used in SimGrid.
SimGrid comes with several network, CPU and storage models built in, and you can change the used model at runtime by changing the passed configuration. The three main configuration items are given below. For each of these items, passing the special help
value gives you a short description of all possible values. Also, –help-models
should provide information about all models for all existing resources.
As of writing, the following network models are accepted. Over the time new models can be added, and some experimental models can be removed; check the values on your simulators for an uptodate information. Note that the CM02 model is described in the research report A Network Model for Simulation of Grid Application while LV08 is described in Accuracy Study and Improvement of Network Simulation in the SimGrid Framework.
If you compiled SimGrid accordingly, you can use packet-level network simulators as network models (see ns-3 as a SimGrid model). In that case, you have two extra models, described below, and some specificadditional configuration flags".
Concerning the CPU, we have only one model for now:
The host concept is the aggregation of a CPU with a network card. Three models exists, but actually, only 2 of them are interesting. The "compound" one is simply due to the way our internal code is organized, and can easily be ignored. So at the end, you have two host models: The default one allows to aggregate an existing CPU model with an existing network model, but does not allow parallel tasks because these beasts need some collaboration between the network and CPU model. That is why, ptask_07 is used by default when using SimDag.
SimGrid supports the use of plugins; currently, no known plugins can be activated but there are use-cases where you may want to write your own plugin (for instance, for logging).
Plugins can for instance define own classes that inherit from existing classes (for instance, a class "CpuEnergy" inherits from "Cpu" to assess energy consumption).
The plugin connects to the code by registering callbacks using signal.connect(callback)
(see file src/surf/plugins/energy.cpp
for details).
--cfg=plugin:Energy
The network and CPU models that are based on lmm_solve (that is, all our analytical models) accept specific optimization configurations.
It is still possible to disable the maxmin-selective-update
feature because it can reveal counter-productive in very specific scenarios where the interaction level is high. In particular, if all your communication share a given backbone link, you should disable it: without maxmin-selective-update
, every communications are updated at each step through a simple loop over them. With that feature enabled, every communications will still get updated in this case (because of the dependency induced by the backbone), but through a complicated pattern aiming at following the actual dependencies.
The analytical models handle a lot of floating point values. It is possible to change the epsilon used to update and compare them through the maxmin/precision item (default value: 0.00001). Changing it may speedup the simulation by discarding very small actions, at the price of a reduced numerical precision.
The maximum number of variables in a system can be tuned through the maxmin/concurrency_limit item (default value: 100). Setting a higher value can lift some limitations, such as the number of concurrent processes running on a single host.
The analytical models need to know the maximal TCP window size to take the TCP congestion mechanism into account. This is set to 20000 by default, but can be changed using the network/TCP-gamma item.
On linux, this value can be retrieved using the following commands. Both give a set of values, and you should use the last one, which is the maximal size.
cat /proc/sys/net/ipv4/tcp_rmem # gives the sender window cat /proc/sys/net/ipv4/tcp_wmem # gives the receiver window
SimGrid can take network irregularities such as a slow startup or changing behavior depending on the message size into account. You should not change these values unless you really know what you're doing.
The corresponding values were computed through data fitting one the timings of packet-level simulators.
See Accuracy Study and Improvement of Network Simulation in the SimGrid Framework for more information about these parameters.
If you are using the SMPI model, these correction coefficients are themselves corrected by constant values depending on the size of the exchange. Again, only hardcore experts should bother about this fact.
InfiniBand network behavior can be modeled through 3 parameters, as explained in this PhD thesis. These factors can be changed through the following option:
smpi/IB-penalty-factors:"βe;βs;γs"
By default SMPI uses factors computed on the Stampede Supercomputer at TACC, with optimal deployment of processes on nodes.
As of SimGrid v3.7, cross-traffic effects can be taken into account in analytical simulations. It means that ongoing and incoming communication flows are treated independently. In addition, the LV08 model adds 0.05 of usage on the opposite direction for each new created flow. This can be useful to simulate some important TCP phenomena such as ack compression.
For that to work, your platform must have two links for each pair of interconnected hosts. An example of usable platform is available in examples/platforms/crosstraffic.xml
.
This is activated through the network/crosstraffic item, that can be set to 0 (disable this feature) or 1 (enable it).
Note that with the default host model this option is activated by default.
(this configuration item is experimental and may change or disapear)
It is possible to specify a timing gap between consecutive emission on the same network card through the network/sender-gap item. This is still under investigation as of writting, and the default value is to wait 10 microseconds (1e-5 seconds) between emissions.
(this configuration item is experimental and may change or disapear)
It is possible to specify that messages below a certain size will be sent as soon as the call to MPI_Send is issued, without waiting for the correspondant receive. This threshold can be configured through the smpi/async-small-thresh item. The default value is 0. This behavior can also be manually set for MSG mailboxes, by setting the receiving mode of the mailbox with a call to MSG_mailbox_set_async . For MSG, all messages sent to this mailbox will have this behavior, so consider using two mailboxes if needed.
This value needs to be smaller than or equals to the threshold set at Simulating MPI detached send , because asynchronous messages are meant to be detached as well.
When using the packet-level pseudo-models, several specific configuration flags are provided to configure the associated tools. There is by far not enough such SimGrid flags to cover every aspects of the associated tools, since we only added the items that we needed ourselves. Feel free to request more items (or even better: provide patches adding more items).
When using NS3, the only existing item is ns3/TcpModel, corresponding to the ns3::TcpL4Protocol::SocketType configuration item in NS3. The only valid values (enforced on the SimGrid side) are 'NewReno' or 'Reno' or 'Tahoe'.
Each host maintains a fixed-size array of its file descriptors. You can change its size (1024 by default) through the storage/max_file_descriptors item to either enlarge it if your application requires it or to reduce it to save memory space.
To enable the SimGrid model-checking support the program should be executed using the simgrid-mc wrapper:
simgrid-mc ./my_program
Safety properties are expressed as assertions using the function
void MC_assert(int prop);
If you want to specify liveness properties (beware, that's experimental), you have to pass them on the command line, specifying the name of the file containing the property, as formatted by the ltl2ba program.
--cfg=model-check/property:<filename>
By default, the system is backtracked to its initial state to explore another path instead of backtracking to the exact step before the fork that we want to explore (this is called stateless verification). This is done this way because saving intermediate states can rapidly exhaust the available memory. If you want, you can change the value of the model-check/checkpoint
variable. For example, the following configuration will ask to take a checkpoint every step. Beware, this will certainly explode your memory. Larger values are probably better, make sure to experiment a bit to find the right setting for your specific system.
--cfg=model-check/checkpoint:1
The main issue when using the model-checking is the state space explosion. To counter that problem, several exploration reduction techniques can be used. There is unfortunately no silver bullet here, and the most efficient reduction techniques cannot be applied to any properties. In particular, the DPOR method cannot be applied on liveness properties since it may break some cycles in the exploration that are important to the property validity.
--cfg=model-check/reduction:<technique>
For now, this configuration variable can take 2 values: none: Do not apply any kind of reduction (mandatory for now for liveness properties) dpor: Apply Dynamic Partial Ordering Reduction. Only valid if you verify local safety properties.
In order to detect cycles, the model-checker needs to check if a new explored state is in fact the same state than a previous one. In order to do this, the model-checker can take a snapshot of each visited state: this snapshot is then used to compare it with subsequent states in the exploration graph.
The model-check/visited is the maximum number of states which are stored in memory. If the maximum number of snapshotted state is reached some states will be removed from the memory and some cycles might be missed.
By default, no state is snapshotted and cycles cannot be detected.
The model-check/termination configuration item can be used to report if a non-termination execution path has been found. This is a path with a cycle which means that the program might never terminate.
This only works in safety mode.
This options is disabled by default.
If set, the model-check/dot-output configuration item is the name of a file in which to write a dot file of the path leading the found property (safety or liveness violation) as well as the cycle for liveness properties. This dot file can then fed to the graphviz dot tool to generate an corresponding graphical representation.
The model-checker/max-depth can set the maximum depth of the exploration graph of the model-checker. If this limit is reached, a logging message is sent and the results might not be exact.
By default, there is not depth limit.
By default, the model-checker does not handle timeout conditions: the wait
operations never time out. With the model-check/timeout configuration item set to yes, the model-checker will explore timeouts of wait
operations.
The model-check/communications-determinism and model-check/send-determinism items can be used to select the communication determinism mode of the model-checker which checks determinism properties of the communications of an application.
When the model-checker is configured to take a snapshot of each explored state (with the model-checker/visited item), the memory consumption can rapidly reach GiB ou Tib of memory. However, for many workloads, the memory does not change much between different snapshots and taking a complete copy of each snapshot is a waste of memory.
The model-check/sparse-checkpoint option item can be set to yes in order to avoid making a complete copy of each snapshot: instead, each snapshot will be decomposed in blocks which will be stored separately. If multiple snapshots share the same block (or if the same block is used in the same snapshot), the same copy of the block will be shared leading to a reduction of the memory footprint.
For many applications, this option considerably reduces the memory consumption. In somes cases, the model-checker might be slightly slower because of the time taken to manage the metadata about the blocks. In other cases however, this snapshotting strategy will be much faster by reducing the cache consumption. When the memory consumption is important, by avoiding to hit the swap or reducing the swap usage, this option might be much faster than the basic snapshotting strategy.
This option is currently disabled by default.
The size of the stacks can have a huge impact on the memory consumption when using model-checking. By default, each snapshot will save a copy of the whole stacks and not only of the part which is really meaningful: you should expect the contribution of the memory consumption of the snapshots to be \( \mbox{number of processes} \times \mbox{stack size} \times \mbox{number of states} \).
The model-check/sparse-checkpoint can be used to reduce the memory consumption by trying to share memory between the different snapshots.
When compiled against the model checker, the stacks are not protected with guards: if the stack size is too small for your application, the stack will silently overflow on other parts of the memory.
Usually most of the time of the model-checker is spent comparing states. This process is complicated and consumes a lot of bandwidth and cache. In order to speedup the state comparison, the experimental model-checker/hash configuration item enables the computation of a hash summarizing as much information of the state as possible into a single value. This hash can be used to avoid most of the comparisons: the costly comparison is then only used when the hashes are identical.
Currently most of the state is not included in the hash because the implementation was found to be buggy and this options is not as useful as it could be. For this reason, it is currently disabled by default.
As the model-checker keeps jumping at different places in the execution graph, it is difficult to understand what happens when trying to debug an application under the model-checker. Event the output of the program is difficult to interpret. Moreover, the model-checker does not behave nicely with advanced debugging tools such as valgrind. For those reason, to identify a trajectory in the execution graph with the model-checker and replay this trajcetory and without the model-checker black-magic but with more standard tools (such as a debugger, valgrind, etc.). For this reason, Simgrid implements an experimental record/replay functionnality in order to record a trajectory with the model-checker and replay it without the model-checker.
When the model-checker finds an interesting path in the application execution graph (where a safety or liveness property is violated), it can generate an identifier for this path. In order to enable this behavious the model-check/record must be set to yes. By default, this behaviour is not enabled.
This is an example of output:
[ 0.000000] (0:@) Check a safety property [ 0.000000] (0:@) ************************** [ 0.000000] (0:@) *** PROPERTY NOT VALID *** [ 0.000000] (0:@) ************************** [ 0.000000] (0:@) Counter-example execution trace: [ 0.000000] (0:@) Path = 1/3;1/4 [ 0.000000] (0:@) [(1)Tremblay (app)] MC_RANDOM(3) [ 0.000000] (0:@) [(1)Tremblay (app)] MC_RANDOM(4) [ 0.000000] (0:@) Expanded states = 27 [ 0.000000] (0:@) Visited states = 68 [ 0.000000] (0:@) Executed transitions = 46
This path can then be replayed outside of the model-checker (and even in non-MC build of simgrid) by setting the model-check/replay item to the given path. The other options should be the same (but the model-checker should be disabled).
The format and meaning of the path may change between different releases so the same release of Simgrid should be used for the record phase and the replay phase.
In SimGrid, the user code is virtualized in a specific mechanism that allows the simulation kernel to control its execution: when a user process requires a blocking action (such as sending a message), it is interrupted, and only gets released when the simulated clock reaches the point where the blocking operation is done.
In SimGrid, the containers in which user processes are virtualized are called contexts. Several context factory are provided, and you can select the one you want to use with the contexts/factory configuration item. Some of the following may not exist on your machine because of portability issues. In any case, the default one should be the most effcient one (please report bugs if the auto-detection fails for you). They are sorted here from the slowest to the most effient:
The only reason to change this setting is when the debugging tools get fooled by the optimized context factories. Threads are the most debugging-friendly contextes, as they allow to set breakpoints anywhere with gdb and visualize backtraces for all processes, in order to debug concurrency issues. Valgrind is also more comfortable with threads, but it should be usable with all factories.
Each virtualized used process is executed using a specific system stack. The size of this stack has a huge impact on the simulation scalability, but its default value is rather large. This is because the error messages that you get when the stack size is too small are rather disturbing: this leads to stack overflow (overwriting other stacks), leading to segfaults with corrupted stack traces.
If you want to push the scalability limits of your code, you might want to reduce the contexts/stack-size item. Its default value is 8192 (in KiB), while our Chord simulation works with stacks as small as 16 KiB, for example. For the thread factory, the default value is the one of the system, if it is too large/small, it has to be set with this parameter.
The operating system should only allocate memory for the pages of the stack which are actually used and you might not need to use this in most cases. However, this setting is very important when using the model checker (see Performance considerations for the model checker).
In some cases, no stack guard page is used and the stack will silently overflow on other parts of the memory if the stack size is too small for your application. This happens :
Parallel execution of the user code is only considered stable in SimGrid v3.7 and higher. It is described in INRIA RR-7653.
If you are using the ucontext
or raw
context factories, you can request to execute the user code in parallel. Several threads are launched, each of them handling as much user contexts at each run. To actiave this, set the contexts/nthreads item to the amount of cores that you have in your computer (or lower than 1 to have the amount of cores auto-detected).
Even if you asked several worker threads using the previous option, you can request to start the parallel execution (and pay the associated synchronization costs) only if the potential parallelism is large enough. For that, set the contexts/parallel-threshold item to the minimal amount of user contexts needed to start the parallel execution. In any given simulation round, if that amount is not reached, the contexts will be run sequentially directly by the main thread (thus saving the synchronization costs). Note that this option is mainly useful when the grain of the user code is very fine, because our synchronization is now very efficient.
When parallel execution is activated, you can choose the synchronization schema used with the contexts/synchro item, which value is either:
The tracing subsystem can be configured in several different ways depending on the nature of the simulator (MSG, SimDag, SMPI) and the kind of traces that need to be obtained. See the Tracing Configuration Options subsection to get a detailed description of each configuration option.
We detail here a simple way to get the traces working for you, even if you never used the tracing API.
--cfg=tracing:yes --cfg=tracing/uncategorized:yes --cfg=triva/uncategorized:uncat.plistThe first parameter activates the tracing subsystem, the second tells it to trace host and link utilization (without any categorization) and the third creates a graph configuration file to configure Triva when analysing the resulting trace file.
--cfg=tracing:yes --cfg=tracing/categorized:yes --cfg=triva/categorized:cat.plistThe first parameter activates the tracing subsystem, the second tells it to trace host and link categorized utilization and the third creates a graph configuration file to configure Triva when analysing the resulting trace file.
smpirun -trace ...The -trace parameter for the smpirun script runs the simulation with –cfg=tracing:yes and –cfg=tracing/smpi:yes. Check the smpirun's -help parameter for additional tracing options.
Sometimes you might want to put additional information on the trace to correctly identify them later, or to provide data that can be used to reproduce an experiment. You have two ways to do that:
--cfg=tracing/comment:my_simulation_identifier
--cfg=tracing/comment-file:my_file_with_additional_information.txt
Please, use these two parameters (for comments) to make reproducible simulations. For additional details about this and all tracing options, check See the Tracing configuration Options.
Sometimes your application may try to send a task that is still being executed somewhere else, making it impossible to send this task. However, for debugging purposes, one may want to know what the other host is/was doing. This option shows a backtrace of the other process.
Enable this option by adding
--cfg=msg/debug-multiple-use:on
The SMPI interface provides several specific configuration items. These are uneasy to see since the code is usually launched through the smiprun
script directly.
In SMPI, the sequential code is automatically benchmarked, and these computations are automatically reported to the simulator. That is to say that if you have a large computation between a MPI_Recv()
and a MPI_Send()
, SMPI will automatically benchmark the duration of this code, and create an execution task within the simulator to take this into account. For that, the actual duration is measured on the host machine and then scaled to the power of the corresponding simulated machine. The variable smpi/host-speed allows to specify the computational speed of the host machine (in flop/s) to use when scaling the execution times. It defaults to 20000, but you really want to update it to get accurate simulation results.
When the code is constituted of numerous consecutive MPI calls, the previous mechanism feeds the simulation kernel with numerous tiny computations. The smpi/cpu-threshold item becomes handy when this impacts badly the simulation performance. It specifies a threshold (in seconds) below which the execution chunks are not reported to the simulation kernel (default value: 1e-6).
In some cases, however, one may wish to disable simulation of application computation. This is the case when SMPI is used not to simulate an MPI applications, but instead an MPI code that performs "live replay" of another MPI app (e.g., ScalaTrace's replay tool, various on-line simulators that run an app at scale). In this case the computation of the replay/simulation logic should not be simulated by SMPI. Instead, the replay tool or on-line simulator will issue "computation events", which correspond to the actual MPI simulation being replayed/simulated. At the moment, these computation events can be simulated using SMPI by calling internal smpi_execute*() functions.
To disable the benchmarking/simulation of computation in the simulated application, the variable smpi/simulate-computation should be set to no.
Solution | Computations actually executed? | Computations simulated ? |
---|---|---|
–cfg=smpi/simulate-computation:no | Yes | No, never |
–cfg=smpi/cpu-threshold:42 | Yes, in all cases | Only if it lasts more than 42 seconds |
SMPI_SAMPLE() macro | Only once per loop nest (see documentation) | Always |
This option allows you to pass a file that contains two columns: The first column defines the section that will be subject to a speedup; the second column is the speedup.
For instance:
"start:stop","ratio" "exchange_1.f:30:exchange_1.f:130",1.18244559422142
The first line is the header - you must include it. The following line means that the code between two consecutive MPI calls on line 30 in exchange_1.f and line 130 in exchange_1.f should receive a speedup of 1.18244559422142. The value for the second column is therefore a speedup, if it is larger than 1 and a slow-down if it is smaller than 1. Nothing will be changed if it is equal to 1.
Of course, you can set any arbitrary filenames you want (so the start and end don't have to be in the same file), but be aware that this mechanism only supports consecutive calls!
The possible throughput of network links is often dependent on the message sizes, as protocols may adapt to different message sizes. With this option, a series of message sizes and factors are given, helping the simulation to be more realistic. For instance, the current default value is
65472:0.940694;15424:0.697866;9376:0.58729;5776:1.08739;3484:0.77493;1426:0.608902;732:0.341987;257:0.338112;0:0.812084
So, messages with size 65472 and more will get a total of MAX_BANDWIDTH*0.940694, messages of size 15424 to 65471 will get MAX_BANDWIDTH*0.697866 and so on. Here, MAX_BANDWIDTH denotes the bandwidth of the link.
Default: 0 (false)
Most of the time, you run MPI code with SMPI to compute the time it would take to run it on a platform. But since the code is run through the smpirun
script, you don't have any control on the launcher code, making it difficult to report the simulated time when the simulation ends. If you set the smpi/display-timing item to 1, smpirun
will display this information when the simulation ends.
Simulation time: 1e3 seconds.
The motivation and syntax for this option is identical to the motivation/syntax of smpi/bw-factor, see smpi/bw-factor: Bandwidth factors for details.
There is an important difference, though: While smpi/bw-factor reduces the actual bandwidth (i.e., values between 0 and 1 are valid), latency factors increase the latency, i.e., values larger than or equal to 1 are valid here.
This is the default value:
65472:11.6436;15424:3.48845;9376:2.59299;5776:2.18796;3484:1.88101;1426:1.61075;732:1.9503;257:1.95341;0:2.01467
This option takes the names of PAPI counters and adds their respective values to the trace files. (See Section Tracing configuration Options.)
It is planned to make this feature available on a per-process (or per-thread?) basis. The first draft, however, just implements a "global" (i.e., for all processes) set of counters, the "default" set.
--cfg=smpi/papi-events:"default:PAPI_L3_LDM:PAPI_L2_LDM"
MPI executables are meant to be executed in separated processes, but SMPI is executed in only one process. Global variables from executables will be placed in the same memory zone and shared between processes, causing hard to find bugs. To avoid this, several options are possible :
To use this runtime automatic switching, the variable smpi/privatize-global-variables should be set to yes
This threshold specifies the size in bytes under which the send will return immediately. This is different from the threshold detailed in Simulating asyncronous send because the message is not effectively sent when the send is posted. SMPI still waits for the correspondant receive to be posted to perform the communication operation. This threshold can be set by changing the smpi/send-is-detached-thresh item. The default value is 65536.
SMPI implements more than 100 different algorithms for MPI collective communication, to accurately simulate the behavior of most of the existing MPI libraries. The smpi/coll-selector item can be used to use the decision logic of either OpenMPI or MPICH libraries (values: ompi or mpich, by default SMPI uses naive version of collective operations). Each collective operation can be manually selected with a smpi/collective_name:algo_name. Available algorithms are listed in Simulating collective operations .
Default value: 0.0001
The behavior and motivation for this configuration option is identical with smpi/test, see Section smpi/test: Inject constant times for calls to MPI_Test for details.
Default value: 0
The behavior for this configuration option is identical with smpi/test, see Section smpi/test: Inject constant times for calls to MPI_Test for details.
This configuration option works exactly as smpi/os, see Section smpi/os: Inject constant times for send operations. Of course, smpi/ois is used to account for MPI_Isend instead of MPI_Send.
In several network models such as LogP, send (MPI_Send, MPI_Isend) and receive (MPI_Recv) operations incur costs (i.e., they consume CPU time). SMPI can factor these costs in as well, but the user has to configure SMPI accordingly as these values may vary by machine. This can be done by using smpi/os for MPI_Send operations; for MPI_Isend and MPI_Recv, use smpi/ois and smpi/or, respectively. These work exactly as smpi/ois.
smpi/os can consist of multiple sections; each section takes three values, for example:
1:3:2;10:5:1
Here, the sections are divided by ";" (that is, this example contains two sections). Furthermore, each section consists of three values.
Now, SMPI always checks which section it should take for a given message; that is, if a message of size 11 is sent with the configuration of the example above, only the second section will be used, not the first, as the first value of the second section is closer to the message size. Hence, a message of size 11 incurs the following cost inside MPI_Send:
5+11*1
As 5 is the startup cost and 1 is the cost per byte.
This configuration option works exactly as smpi/os, see Section smpi/os: Inject constant times for send operations. Of course, smpi/or is used to account for MPI_Recv instead of MPI_Send.
Default value: 0.0001
By setting this option, you can control the amount of time a process sleeps when MPI_Test() is called; this is important, because SimGrid normally only advances the time while communication is happening and thus, MPI_Test will not add to the time, resulting in a deadlock if used as a break-condition.
Here is an example:
Default: 1
SMPI can use shared memory by calling shm_* functions; this might speed up the simulation. This opens or creates a new POSIX shared memory object, kept in RAM, in /dev/shm.
If you want to disable this behavior, set the value to 0.
Default value: 0
By setting this option, you can control the amount of time a process sleeps when MPI_Wtime() is called; this is important, because SimGrid normally only advances the time while communication is happening and thus, MPI_Wtime will not add to the time, resulting in a deadlock if used as a break-condition.
Here is an example:
If the time is never advanced, this loop will clearly never end as MPI_Wtime() always returns the same value. Hence, pass a (small) value to the smpi/wtime option to force a call to MPI_Wtime to advance the time as well.
The C / C++ standard contains a function called atexit. atexit registers callbacks, which are called just before the program terminates.
By setting the configuration option clean-atexit to 1 (true), a callback is registered and will clean up some variables and terminate/cleanup the tracing.
TODO: Add when this should be used.
It is possible to specify a list of directories to search into for the <include> tag in XML files by using the path configuration item. To add several directory to the path, set the configuration item several times, as in
--cfg=path:toto --cfg=path:tutu
By default, when Ctrl-C is pressed, the status of all existing simulated processes is displayed before exiting the simulation. This is very useful to debug your code, but it can reveal troublesome in some cases (such as when the amount of processes becomes really big). This behavior is disabled when verbose-exit is set to 0 (it is to 1 by default).
--cfg=exceptions/cutpath:1
This configuration option is used to remove the path from the backtrace shown when an exception is thrown. This is mainly useful for the tests: the full file path makes the tests not reproducible, and thus failing as we are currently comparing output. Clearly, the path used on different machines are almost guaranteed to be different and hence, the output would mismatch, causing the test to fail.
It can be done by using XBT. Go to Logging support for more details.
In order to achieve higher performance, you might want to use the raw context factory which avoids any system call when switching between tasks. If it is not possible you might use ucontext instead.
A stack guard page is usually used which prevents the stack from overflowing on other parts of the memory. However this might have a performance impact if a huge number of processes is created. The option contexts:guard-size is the number of stack guard pages used. By setting it to 0, no guard pages will be used: in this case, you should avoid using small stacks (stack-size) as the stack will silently overflow on other parts of the memory.
clean-atexit
: Cleanup before terminationcontexts/factory
: Selecting the virtualization factorycontexts/guard-size
: Running user code in parallelcontexts/nthreads
: Running user code in parallelcontexts/parallel_threshold
: Running user code in parallelcontexts/stack-size
: Adapting the used stack sizecontexts/synchro
: Running user code in parallelcpu/maxmin-selective-update
: Optimization level of the platform modelscpu/model
: Selecting the platform modelscpu/optim
: Optimization level of the platform modelsexception/cutpath
: Truncate local path from exception backtracehost/model
: Selecting the platform modelsmaxmin/precision
: Numerical precision of the platform modelsmsg/debug-multiple-use
: Debugging MSGmodel-check
: Configuring the Model-Checkingmodel-check/checkpoint
: Going for stateful verificationmodel-check/communications-determinism
: Communication determinismmodel-check/dot-output
: model-check/dot-output, Dot outputmodel-check/hash
: Hashing of the state (experimental)model-check/property
: Specifying a liveness propertymodel-check/max-depth
: model-check/max_depth, Depth limitmodel-check/record
: Record/replay (experimental)model-check/reduction
: Specifying the kind of reductionmodel-check/replay
: Record/replay (experimental)model-check/send-determinism
: Communication determinismmodel-check/sparse-checkpoint
: Per page checkpointsmodel-check/termination
: model-check/termination, Non termination detectionmodel-check/timeout
: Handling of timeoutmodel-check/visited
: model-check/visited, Cycle detectionnetwork/bandwidth-factor
: Correcting important network parametersnetwork/crosstraffic
: Simulating cross-trafficnetwork/latency-factor
: Correcting important network parametersnetwork/maxmin-selective-update
: Optimization level of the platform modelsnetwork/model
: Selecting the platform modelsnetwork/optim
: Optimization level of the platform modelsnetwork/sender_gap
: Simulating sender gapnetwork/TCP-gamma
: Maximal TCP window sizenetwork/weight-S
: Correcting important network parametersns3/TcpModel
: Configuring packet-level pseudo-modelspath:
XML file inclusion pathplugin:
Pluginsstorage/max_file_descriptors
: Maximum amount of file descriptors per hostsurf/precision
: Numerical precision of the platform modelsFor
collective operations of SMPI, please refer to Section Index of SMPI collective algorithms optionssmpi/async-small-thresh
: Simulating asyncronous sendsmpi/bw-factor
: smpi/bw-factor: Bandwidth factorssmpi/coll-selector
: Simulating MPI collective algorithmssmpi/comp-adjustment-file
: smpi/comp-adjustment-file: Slow-down or speed-up parts of your code.smpi/cpu-threshold
: smpi/bench: Automatic benchmarking of SMPI codesmpi/display-timing
: smpi/display-timing: Reporting simulation timesmpi/grow-injected-times
: smpi/test: Inject constant times for calls to MPI_Testsmpi/host-speed
: smpi/bench: Automatic benchmarking of SMPI codesmpi/IB-penalty-factors
: Correcting important network parameterssmpi/iprobe
: smpi/iprobe: Inject constant times for calls to MPI_Iprobesmpi/init
: smpi/init: Inject constant times for calls to MPI_Initsmpi/lat-factor
: smpi/lat-factor: Latency factorssmpi/ois
: smpi/ois: Inject constant times for asynchronous send operationssmpi/or
: smpi/or: Inject constant times for receive operationssmpi/os
: smpi/os: Inject constant times for send operationssmpi/papi-events
: smpi/papi-events: Trace hardware counters with PAPIsmpi/privatize-global-variables
: smpi/privatize-global-variables: Automatic privatization of global variablessmpi/send-is-detached-thresh
: Simulating MPI detached sendsmpi/simulate-computation
: smpi/bench: Automatic benchmarking of SMPI codesmpi/test
: smpi/test: Inject constant times for calls to MPI_Testsmpi/use-shared-malloc
: smpi/use-shared-malloc: Factorize malloc()ssmpi/wtime
: smpi/wtime: Inject constant times for calls to MPI_WtimeTracing
configuration options can be found in Section Tracing configuration Options.storage/model
: options_storage_modelverbose-exit
: Behavior on Ctrl-Cvm/model
: options_vm_modelTODO: All available collective algorithms will be made available via the smpirun --help-coll
command.