SimGrid  3.19.1
Versatile Simulation of Distributed Systems
Configure SimGrid

Table of Contents

image/svg+xml HPC Clouds P2P Scheduling Grids Application ExperimentalSetup Simulation Model Checking Property Reduction (what you test) Virtual Platform ▸ Resources ▸ Routing ▸ External Events ▸ Actors ▸ MPI Legacy Code ▸ Offline Traces ▸ Centralized Algo (C/C++/Java) + ▸ Safety ▸ Liveness ▸ Patterns ▸ DPOR ▸ State Equality (highly experimental) Models Plugins ▸ Raw Perf. ▸ Contention ▸ Collective x 2 x ≮ y y 1 send(1) send(2) Your code ▸ Signals ▸ Extensions deep inside $./my_simulator|MSG_visualization/ [0.000] [Tremblay:master]Got3workersand6taskstoprocess [0.000] [Tremblay:master]Sending’Task_0’to’Jupiter’ [0.148] [Tremblay:master]Sending’Task_1’to’Fafard’ [0.148] [Jupiter:worker]Processing’Task_0’ [0.347] [Tremblay:master]Sending’Task_2’to’Ginette’ [0.347] [Fafard:worker]Processing’Task_1’ [0.476] [Tremblay:master]Sending’Task_3’to’Jupiter’ [0.476] [Ginette:worker]Processing’Task_2’ [0.803] [Jupiter:worker]’Task_0’done [0.951] [Tremblay:master]Sending’Task_4’to’Fafard’ [0.951] [Jupiter:worker]Processing’Task_3’ [1.003] [Fafard:worker]’Task_1’done [1.202] [Tremblay:master]Sending’Task_5’to’Ginette’ [1.202] [Fafard:worker]Processing’Task_4’ [1.507] [Ginette:worker]’Task_2’done [1.606] [Jupiter:worker]’Task_3’done [1.635] [Tremblay:master]Alltasksdispatched.Let’sstopworkers. [1.635] [Ginette:worker]Processing’Task_5’ [1.637] [Jupiter:worker]I’mdone.Seeyou! [1.857] [Fafard:worker]’Task_4’done [1.859] [Fafard:worker]I’mdone.Seeyou! [2.666] [Ginette:worker]’Task_5’done [2.668] [Tremblay:master]Goodbyenow! [2.668] [Ginette:worker]I’mdone.Seeyou! [2.668][]Simulationtime2.66766 1 3 45 6 2 Root End Time, Energy (CPU, Links, Disks) 3 6 4 10G 1 13G 1.5 Config Domains operations Exhaustive test Counter example R visualizations Textual logs (paths) Calibration Workflows Fog Volunteer IoT App Deployment

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.

Passing configuration options to the simulators

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 --cfg 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 ').

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:

  <prop id="Item" value="Value"/>

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 simgrid::s4u::Engine::setConfig() or MSG_config() functions. 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.

#include <xbt/config.h>
int main(int argc, char *argv[]) {
SD_init(&argc, argv);
/* Prefer MSG_config() if you use MSG!! */
// Rest of your code

Index of all existing configuration options

The full list can be retrieved by passing "--help" and "--help-cfg" to an executable that uses SimGrid.

Index of SMPI collective algorithms options

TODO: All available collective algorithms will be made available via the smpirun --help-coll command.

Configuring the platform models

Selecting the platform models

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.

  • network/model: specify the used network model
  • cpu/model: specify the used CPU model
  • host/model: specify the used host model
  • storage/model: specify the used storage model (there is currently only one such model - this option is hence only useful for future releases)
  • vm/model: specify the model for virtual machines (there is currently only one such model - this option is hence only useful for future releases)

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.

  • LV08 (default one): Realistic network analytic model (slow-start modeled by multiplying latency by 10.4, bandwidth by .92; bottleneck sharing uses a payload of S=8775 for evaluating RTT)
  • Constant: Simplistic network model where all communication take a constant time (one second). This model provides the lowest realism, but is (marginally) faster.
  • SMPI: Realistic network model specifically tailored for HPC settings (accurate modeling of slow start with correction factors on three intervals: < 1KiB, < 64 KiB, >= 64 KiB). See also this section for more info.
  • IB: Realistic network model specifically tailored for HPC settings with InfiniBand networks (accurate modeling contention behavior, based on the model explained in See also this section for more info.
  • CM02: Legacy network analytic model (Very similar to LV08, but without corrective factors. The timings of small messages are thus poorly modeled)
  • Reno: Model from Steven H. Low using lagrange_solve instead of lmm_solve (experts only; check the code for more info).
  • Reno2: Model from Steven H. Low using lagrange_solve instead of lmm_solve (experts only; check the code for more info).
  • Vegas: Model from Steven H. Low using lagrange_solve instead of lmm_solve (experts only; check the code for more info).

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 specific additional configuration flags.

  • NS3: Network pseudo-model using the NS3 tcp model

Concerning the CPU, we have only one model for now:

  • Cas01: Simplistic CPU model (time=size/power)

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.

  • default: Default host model. Currently, CPU:Cas01 and network:LV08 (with cross traffic enabled)
  • compound: Host model that is automatically chosen if you change the network and CPU models
  • ptask_L07: Host model somehow similar to Cas01+CM02 but allowing "parallel tasks", that are intended to model the moldable tasks of the grid scheduling literature.


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).

This option is case-sensitive: Energy and energy are not the same!

Optimization level of the platform models

The network and CPU models that are based on lmm_solve (that is, all our analytical models) accept specific optimization configurations.

  • items network/optim and cpu/optim (both default to 'Lazy'):
    • Lazy: Lazy action management (partial invalidation in lmm + heap in action remaining).
    • TI: Trace integration. Highly optimized mode when using availability traces (only available for the Cas01 CPU model for now).
    • Full: Full update of remaining and variables. Slow but may be useful when debugging.
  • items network/maxmin-selective-update and cpu/maxmin-selective-update: configure whether the underlying should be lazily updated or not. It should have no impact on the computed timings, but should speed up the computation.

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.

Numerical precision of the platform models

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.

Concurrency limit

The maximum number of variables per resource can be tuned through the maxmin/concurrency-limit item. The default value is -1, meaning that there is no such limitation. You can have as many simultaneous actions per resources as you want. If your simulation presents a very high level of concurrency, it may help to use e.g. 100 as a value here. It means that at most 100 actions can consume a resource at a given time. The extraneous actions are queued and wait until the amount of concurrency of the considered resource lowers under the given boundary.

Such limitations help both to the simulation speed and simulation accuracy on highly constrained scenarios, but the simulation speed suffers of this setting on regular (less constrained) scenarios so it is off by default.

Configuring the Network model

Maximal TCP window size

The analytical models need to know the maximal TCP window size to take the TCP congestion mechanism into account. This is set to 4194304 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

Correcting important network parameters

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:


By default SMPI uses factors computed on the Stampede Supercomputer at TACC, with optimal deployment of processes on nodes.

Simulating cross-traffic

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.

Simulating asyncronous send

(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.

Configuring packet-level pseudo-models

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'.

Configuring the Storage model

Maximum amount of file descriptors per host

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.

Configuring the Model-Checking

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);

Specifying a liveness property

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.


Going for stateful verification

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.


Specifying the kind of reduction

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.


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 (default value for safety checks).

model-check/visited, Cycle detection

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. For that, 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 option 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. Small values can lead to incorrect verifications, but large value can exhaust your memory, so choose carefully.

By default, no state is snapshotted and cycles cannot be detected.

model-check/termination, Non termination detection

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.

model-check/dot-output, Dot output

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.

model-check/max-depth, Depth limit

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.

Handling of timeout

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.

Communication determinism

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.

Per page checkpoints

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.

Performance considerations for the model checker

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 (see Disabling stack guard pages).

Hashing of the state (experimental)

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.

Record/replay (experimental)

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.

Configuring the User Process Virtualization

Selecting the virtualization factory

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. This is explained graphically in the relevant tutorial, available online.

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 approximately sorted here from the slowest to the most efficient:

  • thread: very slow factory using full featured threads (either pthreads or windows native threads). They are slow but very standard. Some debuggers or profilers only work with this factory.
  • java: Java applications are virtualized onto java threads (that are regular pthreads registered to the JVM)
  • ucontext: fast factory using System V contexts (Linux and FreeBSD only)
  • boost: This uses the context implementation of the boost library for a performance that is comparable to our raw implementation.
    Install the relevant library (e.g. with the libboost-contexts-dev package on Debian/Ubuntu) and recompile SimGrid. Note that our implementation is not compatible with recent implementations of the library, and it will be hard to fix this since the library's author decided to hide an API that we were using.
  • raw: amazingly fast factory using a context switching mechanism of our own, directly implemented in assembly (only available for x86 and amd64 platforms for now) and without any unneeded system call.

The main 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 (but the callgrind tool that really don't like raw and ucontext factories).

Adapting the used stack size

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 but you can still change it 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).

Disabling stack guard pages

A stack guard page is usually used which prevents the stack of a given actor from overflowing on another stack. But the performance impact may become prohibitive when the amount of actors increases. 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.

When no stack guard page is created, stacks may then silently overflow on other parts of the memory if their size is too small for the application. This happens:

  • on Windows systems;
  • when the model checker is enabled;
  • and of course when guard pages are explicitely disabled (with contexts:guard-size=0).

Running user code in parallel

Parallel execution of the user code is only considered stable in SimGrid v3.7 and higher, and mostly for MSG simulations. SMPI simulations may well fail in parallel mode. 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:

  • futex: ultra optimized synchronisation schema, based on futexes (fast user-mode mutexes), and thus only available on Linux systems. This is the default mode when available.
  • posix: slow but portable synchronisation using only POSIX primitives.
  • busy_wait: not really a synchronisation: the worker threads constantly request new contexts to execute. It should be the most efficient synchronisation schema, but it loads all the cores of your machine for no good reason. You probably prefer the other less eager schemas.

Configuring the tracing subsystem

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.

  • Any SimGrid-based simulator (MSG, SimDag, SMPI, ...) and raw traces:
    --cfg=tracing:yes --cfg=tracing/uncategorized:yes --cfg=triva/uncategorized:uncat.plist
    The 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.
  • MSG or SimDag-based simulator and categorized traces (you need to declare categories and classify your tasks according to them)
    --cfg=tracing:yes --cfg=tracing/categorized:yes --cfg=triva/categorized:cat.plist
    The 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.
  • SMPI simulator and traces for a space/time view:
    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:

  • Add a string on top of the trace file as comment:
  • Add the contents of a textual file on top of the trace file as comment:

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.

Configuring MSG

Debugging MSG

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


Configuring SMPI

The SMPI interface provides several specific configuration items. These are uneasy to see since the code is usually launched through the smiprun script directly.

smpi/bench: Automatic benchmarking of SMPI code

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).

The option smpi/cpu-threshold ignores any computation time spent below this threshold. SMPI does not consider the amount of these computations; there is no offset for this. Hence, by using a value that is too low, you may end up with unreliable simulation results.

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.

This option just ignores the timings in your simulation; it still executes the computations itself. If you want to stop SMPI from doing that, you should check the SMPI_SAMPLE macros, documented in the section Toward faster simulations.
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

smpi/comp-adjustment-file: Slow-down or speed-up parts of your code.

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:


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!

Please note that you must pass the -trace-call-location flag to smpicc or smpiff, respectively! This flag activates some macro definitions in our mpi.h / mpi.f files that help with obtaining the call location.

smpi/bw-factor: Bandwidth factors

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


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.

The SimGrid-Team has developed a script to help you determine these values. You can find more information and the download here:

smpi/display-timing: Reporting simulation time

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.

smpi/keep-temps: not cleaning up after simulation

Default: 0 (false)

Under some conditions, SMPI generates a lot of temporary files. They usually get cleaned, but you may use this option to not erase these files. This is for example useful when debugging or profiling executions using the dlopen privatization schema, as missing binary files tend to fool the debuggers.

smpi/lat-factor: Latency factors

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:

The SimGrid-Team has developed a script to help you determine these values. You can find more information and the download here:

smpi/papi-events: Trace hardware counters with PAPI

This option is experimental and will be subject to change. This feature currently requires superuser privileges, as registers are queried. Only use this feature with code you trust! Call smpirun for instance via smpirun -wrapper "sudo " <your-parameters> or run sudo sh -c "echo 0 > /proc/sys/kernel/perf_event_paranoid" In the later case, sudo will not be required.
This option is only available when SimGrid was compiled with PAPI support.

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.


smpi/privatization: Automatic privatization of global variables

MPI executables are usually 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 intricate bugs. Several options are possible to avoid this, as described in the main SMPI publication and in the SMPI documentation. SimGrid provides two ways of automatically privatizing the globals, and this option allows to choose between them.

  • no (default when not using smpirun): Do not automatically privatize variables. Pass -no-privatize to smpirun to disable this feature.
  • dlopen or yes (default when using smpirun): Link multiple times against the binary.
  • mmap (slower, but maybe somewhat more stable): Runtime automatic switching of the data segments.
This configuration option cannot be set in your platform file. You can only pass it as an argument to smpirun.

Simulating MPI detached send

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.

Simulating MPI collective algorithms

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 .

smpi/iprobe: Inject constant times for calls to MPI_Iprobe

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.

smpi/iprobe-cpu-usage: Reduce speed for iprobe calls

Default value: 1 (no change from default behavior)

MPI_Iprobe calls can be heavily used in applications. To account correctly for the energy cores spend probing, it is necessary to reduce the load that these calls cause inside SimGrid.

For instance, we measured a max power consumption of 220 W for a particular application but only 180 W while this application was probing. Hence, the correct factor that should be passed to this option would be 180/220 = 0.81.

smpi/init: Inject constant times for calls to MPI_Init

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.

smpi/ois: Inject constant times for asynchronous send operations

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.

smpi/os: Inject constant times for send operations

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:


Here, the sections are divided by ";" (that is, this example contains two sections). Furthermore, each section consists of three values.

  1. The first value denotes the minimum size for this section to take effect; read it as "if message size is greater than this value (and other section has a larger first value that is also smaller than the message size), use this". In the first section above, this value is "1".
  2. The second value is the startup time; this is a constant value that will always be charged, no matter what the size of the message. In the first section above, this value is "3".
  3. The third value is the per-byte cost. That is, it is charged for every byte of the message (incurring cost messageSize*cost_per_byte) and hence accounts also for larger messages. In the first section of the example above, this value is "2".

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:


As 5 is the startup cost and 1 is the cost per byte.

The order of sections can be arbitrary; they will be ordered internally.

smpi/or: Inject constant times for receive operations

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.

smpi/test: Inject constant times for calls to MPI_Test

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:

while(!flag) {
MPI_Test(request, flag, status);
Internally, in order to speed up execution, we use a counter to keep track on how often we already checked if the handle is now valid or not. Hence, we actually use counter*SLEEP_TIME, that is, the time MPI_Test() causes the process to sleep increases linearly with the number of previously failed tests. This behavior can be disabled by setting smpi/grow-injected-times to no. This will also disable this behavior for MPI_Iprobe.

smpi/shared-malloc: Factorize malloc()s

Default: global

If your simulation consumes too much memory, you may want to modify your code so that the working areas are shared by all MPI ranks. For example, in a bloc-cyclic matrix multiplication, you will only allocate one set of blocs, and every processes will share them. Naturally, this will lead to very wrong results, but this will save a lot of memory so this is still desirable for some studies. For more on the motivation for that feature, please refer to the relevant section of the SMPI CourseWare (see Activity #2.2 of the pointed assignment). In practice, change the call to malloc() and free() into SMPI_SHARED_MALLOC() and SMPI_SHARED_FREE().

SMPI provides 2 algorithms for this feature. The first one, called local, allocates one bloc per call to SMPI_SHARED_MALLOC() in your code (each call location gets its own bloc) and this bloc is shared amongst all MPI ranks. This is implemented with the shm_* functions to create a new POSIX shared memory object (kept in RAM, in /dev/shm) for each shared bloc.

With the global algorithm, each call to SMPI_SHARED_MALLOC() returns a new adress, but it only points to a shadow bloc: its memory area is mapped on a 1MiB file on disk. If the returned bloc is of size N MiB, then the same file is mapped N times to cover the whole bloc. At the end, no matter how many SMPI_SHARED_MALLOC you do, this will only consume 1 MiB in memory.

You can disable this behavior and come back to regular mallocs (for example for debugging purposes) using "no" as a value.

If you want to keep private some parts of the buffer, for instance if these parts are used by the application logic and should not be corrupted, you can use SMPI_PARTIAL_SHARED_MALLOC(size, offsets, offsets_count).

As an example,

mem = SMPI_PARTIAL_SHARED_MALLOC(500, {27,42 , 100,200}, 2);

will allocate 500 bytes to mem, such that mem[27..41] and mem[100..199] are shared and other area remain private.

Then, it can be deallocated by calling SMPI_SHARED_FREE(mem).

When smpi/shared-malloc:global is used, the memory consumption problem is solved, but it may induce too much load on the kernel's pages table. In this case, you should use huge pages so that we create only one entry per Mb of malloced data instead of one entry per 4k. To activate this, you must mount a hugetlbfs on your system and allocate at least one huge page:

mkdir /home/huge
sudo mount none /home/huge -t hugetlbfs -o rw,mode=0777
sudo sh -c 'echo 1 > /proc/sys/vm/nr_hugepages' # echo more if you need more

Then, you can pass the option –cfg=smpi/shared-malloc-hugepage:/home/huge to smpirun to actually activate the huge page support in shared mallocs.

smpi/wtime: Inject constant times for calls to MPI_Wtime

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:

while(MPI_Wtime() < some_time_bound) {

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.

Configuring other aspects of SimGrid

Cleanup before termination

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.

Profile files' search path

It is possible to specify a list of directories to search into for the trace files (see trace and trace_connect) 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

Behavior on Ctrl-C

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).

Truncate local path from exception backtrace


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.

Logging Configuration

It can be done by using XBT. Go to Logging support for more details.