aivika-distributed: Parallel distributed discrete event simulation module for the Aivika library

This package extends the aivika-transformers [1] package and allows running parallel distributed simulations. It uses an optimistic strategy known as the Time Warp method. To synchronize the global virtual time, it uses Samadi's algorithm.

Moreover, this package uses the author's modification that allows recovering the distributed simulation after temporary connection errors whenever possible. For that, you have to enable explicitly the recovering mode and enable monitoring all logical processes including the specialized Time Server process as it is shown in one of the test examples included in the distribution.

With the recovering mode enabled, you can try to build a distributed simulation using ordinary computers connected via the ordinary net. For example, such a distributed model could even consist of computers located in different continents of the Earth, where the computers could be connected through the Internet. Here the most exciting thing is that this is the optimistic distributed simulation with possible rollbacks. It is assumed that optimistic methods tend to better support the parallelism inherited in the models.

You can test the distributed simulation using your own laptop only, although the package is still destined to be used with a multi-core computer, or computers connected in the distributed cluster.

There are additional packages that allow you to run the distributed simulation experiments by the Monte-Carlo method. They allow you to save the simulation results in SQL databases and then generate a report or a set of reports consisting of HTML pages with charts, histograms, links to CSV tables, summary statistics etc. Please consult the AivikaSoft [3] website for more details.

Regarding the speed of simulation, the rough estimation is as follows. The distributed simulation module is slower up to 12-30 times in comparison with the sequential aivika [2] simulation library using the equivalent sequential models. The estimation has dramatically changed after started using another more fast pseudo-random number generator by default, which made the sequential module even more fast. The lower estimation in 12 times is likely to correspond to complex models. The upper estimation in 30 times will probably correspond to quite simple event-oriented and process-oriented models, where the sequential module can be exceptionally fast.

Note that you can run up to 7 parallel logical processes on a single 8-core processor computer and run the Time Server process too. On a 36-core processor, you can launch up to 35 logical processes simultaneously.

At the same time, the message passing between the logical processes can dramatically decrease the speed of distributed simulation, especially if they cause rollbacks. Thus, much depends on the distributed model itself.

Finally, you can use the following test model [4] as an example.

[1] http://hackage.haskell.org/package/aivika-transformers

[2] http://hackage.haskell.org/package/aivika

[3] http://www.aivikasoft.com

[4] https://github.com/dsorokin/aivika-distributed-test

• Simulation
  • Aivika
    • Simulation.Aivika.Distributed
      • Simulation.Aivika.Distributed.Optimistic
        • Simulation.Aivika.Distributed.Optimistic.DIO
        • Simulation.Aivika.Distributed.Optimistic.Generator
        • Simulation.Aivika.Distributed.Optimistic.Guard
        • Simulation.Aivika.Distributed.Optimistic.Message
        • Simulation.Aivika.Distributed.Optimistic.Priority
        • Simulation.Aivika.Distributed.Optimistic.QueueStrategy
        • Ref
          • Simulation.Aivika.Distributed.Optimistic.Ref.Base
            • Simulation.Aivika.Distributed.Optimistic.Ref.Base.Lazy
            • Simulation.Aivika.Distributed.Optimistic.Ref.Base.Strict
        • Simulation.Aivika.Distributed.Optimistic.State
        • Simulation.Aivika.Distributed.Optimistic.TimeServer