SimGrid’s Design Goals

Any SimGrid simulation comes down to a set of actors using some resources through activities. SimGrid provides several kinds of resources (link, CPU, disk, and synchronization objects such as mutex or semaphores) along with the corresponding activity kinds (communication, execution, I/O, lock). SimGrid users provide a platform instantiation (list of interconnected resources) and an application (the code executed by actors) along with the actors’ placement on the platform.

The actors (ie, the user code) can only interact with the platform through activities, that are somewhat similar to synchronizations. Some are very natural (locking a mutex is a synchronization with the other actors using the same mutex) while others activities constitute more original synchronization: execution, communication, and I/O have a quantitative component that depends on the resources. But still, writing some data to disk is seen as a synchronization with the other actors using the same disk. When you lock a mutex, you can proceed only when that mutex gets unlocked by someone else. Similarly, when you do an I/O, you can proceed once the disk delivered enough performance to fulfill your demand (along with the concurrent demands of the other actors occurring at the same time). Communication activities have both a qualitative component (the actual communication starts only when both the sender and receiver are ready to proceed) and a quantitative component (consuming the communication power of the link resources).

The design of SimGrid is shaped by several design goals:

  • reproducibility: re-executing the same simulation must lead to the exact same outcome, even if it runs on another computer or operating system. When possible, this should also be true when you use another version of SimGrid.

  • speed: running a given simulation should be as fast as possible

  • versatility: ability to simulate many kinds of distributed systems and resource models. But the simulation should be parsimonious too, to not hinder the tool’s usability. SimGrid tries to provide sane default settings along with the possibility to augment and modify the provided models and their default settings.

  • scalability: ability to deal with very large simulations. In the number of actors, in the size of the platform, in the number of events, or all together.

Actors and activities

The first crux of the SimGrid design lays in the interaction between each actor and the activities. For the sake of reproducibility, the actors cannot interact directly with their environment: every modification request is serialized through a central component that processes them in a reproducible order. For the sake of speed, SimGrid is designed as an operating system: the actors issue simcalls to a simulation kernel that is called maestro (because it decides which actors can proceed and which ones must wait).

In practice, a SimGrid simulation is a suite of so-called scheduling rounds, during which all actors that are not currently blocked on a simcall get executed. For that, maestro passes the control flow to the code of each actor, that are written in either C++, C, Fortran, Python, or Java. The control flow then returns to the maestro when the actor blocks on its next blocking simcall. Note that the time it takes to execute the actor code has to be reported to the simulator using execution activities. SMPI programs are automatically benchmarked while these executions must be manually reported in S4U. The simulated time is discrete in SimGrid and only progresses between scheduling rounds, so all events occurring during a given scheduling round occur at the exact same simulated timestamp, even if the actors are usually executed sequentially on the real platform.

To modify their environment, the actors issue either immediate simcalls that take no time in the simulation (e.g.: spawning another actor), or blocking simcalls that must wait for future events (e.g.: mutex locks require the mutex to be unlocked by its owner; communications wait for the network to provide enough communication performance to fulfill the demand). A given scheduling round is usually composed of several sub-scheduling rounds during which immediate simcalls are resolved. This ends when all actors are either terminated or within a blocking simcall. The simulation models are then used to compute the time at which the first next simcall terminates. The time is advanced to that point, and a new scheduling round begins with all actors that got unblocked at that timestamp.

Context switching between the actors and maestro is highly optimized for the sake of simulation performance. SimGrid provides several implementations of this mechanism, called context factories. These implementations fall into two categories: Preemptive contexts are based on full-fledged system threads such as pthread on Linux or Java threads in the JVM. They are usually better supported by external debuggers and profiling tools, but less efficient. The most efficient factories use non-preemptive mechanisms, such as SysV’s ucontexts, boost’s context, or our own hand-tuned implementation, that is written in assembly language. This is possible because a given actor is never interrupted between consecutive simcalls in SimGrid.

For the sake of performance, actors can be executed in parallel using several system threads for non-preemptive contexts. But in our experience, this rarely leads to any performance improvement because most applications simulated on top of SimGrid are fine-grained: when the simulation performance really matters, the users tend to abstract away any large computations to efficiently simulate the control flow of their application. In addition, parallel simulation puts unpleasant restrictions on the user code, that must be correctly isolated. To be honest, most of the existing SMPI implementation cannot be used in parallel yet.

Parsimonious model versatility

Another orthogonal crux of the SimGrid design is the parsimonious versatility in modeling. For that, we tend to unify all resource and activity kinds. As you have seen, we parallel the classical notion of computational power with the more original communication power and I/O power. Asynchronous executions are less common than the asynchronous communications that proliferate in MPI but they are still provided for sake of symmetry: they even prove useful to efficiently simulate thread pools. Note that asynchronous mutex locking is still to be added to SimGrid atm. The notion of pstate was introduced to model the stepwise variation of computational speed depending on the DVFS, and was reused to model the bootup and shutdown phases of a CPU: the computational speed is 0 at these specific pstates. This pstate notion was extended to represent the fact that the bandwidth provided by a wifi link to a given station depends on its signal-noise ratio (SNR).

Further on this line, all provided resource models are very comparable internally. They rely on linear inequation systems, stating for example that the sum of the computational power received by all computation activities located on a given CPU cannot overpass the computational power provided by this resource. This extends nicely to multi-resources activities such as communications using several links, and also to the so-called parallel tasks (abstract activities representing a parallel execution kernel consuming both the communication and computational power of a set of machines). Specific coefficients are added to the linear system to reflect how the real resources are shared between concurrent usages. The resulting system is then solved using a max-min objective function that maximizes the minimum of all shares allocated to activities. Our experience shows that this approach can successfully be used for fast yet accurate simulations of complex phenomena, provided that the model’s coefficients and constants are carefully tailored and instantiated to that phenomenon.


Even if it was not in the original goals of SimGrid, the framework now integrates a full-featured model-checker (dubbed MC or Mc SimGrid) that can exhaustively explore all execution paths that the application could experience. Conceptually, Mc SimGrid is built upon the ideas presented previously. Instead of using the resource models to compute the order simcall terminations, it explores every order that is causally possible. In a simulation entailing only three concurrent events (i.e., simcalls) A, B, and C, it will first explore the scenario where the activities order is ABC, then the ACB order, then BAC, then BCA, then CAB and finally CBA. Of course, the number of scenarios to explore grows exponentially with the number of simcalls in the simulation. Mc SimGrid leverages reduction techniques to avoid re-exploring equivalent traces.

In practice, Mc SimGrid can be used to verify classical safety and liveness properties, but also communication determinism, a property that allows more efficient solutions toward fault-tolerance. It can alleviate the state space explosion problem through Dynamic Partial Ordering Reduction (DPOR) and state equality.

Mc SimGrid is far more experimental than other parts of the framework, such as SMPI that can now be used to run many full-featured MPI codes out of the box.