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Ensemble propagations

Ensemble propagations#

Added in version 0.17.0.

Starting with version 0.17.0, heyoka offers support for ensemble propagations. In ensemble mode, multiple distinct instances of the same ODE system are integrated in parallel, typically using different sets of initial conditions and/or runtime parameters. Monte Carlo simulations and parameter searches are two typical examples of tasks in which ensemble mode is particularly useful.

The ensemble mode API mirrors the time-limited propagation functions available in the adaptive integrator class. Specifically, three functions are available:

  • ensemble_propagate_until(), for ensemble propagations up to a specified epoch,

  • ensemble_propagate_for(), for ensemble propagations for a time interval,

  • ensemble_propagate_grid(), for ensemble propagations over a time grid.

In this tutorial, we will be focusing on the ensemble_propagate_until() function, but adapting the code to the other two functions should be straightforward.

Note that, at this time, ensemble propagations are limited to using multiple threads of execution to achieve parallelisation. In the future, additional parallelisation modes (e.g., multiprocessing, distributed) may be added.

A simple example#

As usual, for this illustrative tutorial we will be using the ODEs of the simple pendulum. Thus, let us begin with the definition of the symbolic variables and of an integrator object:

    // Create the symbolic variables x and v.
    auto [x, v] = make_vars("x", "v");

    // Create the integrator object
    // in double precision.
    auto ta = taylor_adaptive<double>{// Definition of the ODE system:
                                      // x' = v
                                      // v' = -9.8 * sin(x)
                                      {prime(x) = v, prime(v) = -9.8 * sin(x)},
                                      // Initial conditions
                                      // for x and v.
                                      {0., 0.}};

Note how, differently from the other tutorials, here we have set the initial conditions to zeroes. This is because, in ensemble mode, we will never use directly the ta object to perform a numerical integration. Rather, ta acts as a template from which other integrator objects will be constructed, and thus its initial conditions are inconsequential.

The ensemble_propagate_until() function takes in input at least 4 arguments:

  • the template integrator ta,

  • the final epoch t for the propagations (this argument would be a time interval delta_t for ensemble_propagate_for() and a time grid for ensemble_propagate_grid()),

  • the number of iterations n_iter in the ensemble,

  • a function object gen, known as the generator.

The signature of the generator gen reads:

taylor_adaptive<double> gen(taylor_adaptive<double> ta, std::size_t idx);

That is, the generator takes in input a copy of the template integrator ta and an iteration index idx in the [0, n_iter) range. gen is then expected to modify ta (e.g., by setting its initial conditions to specific values) and return it.

The ensemble_propagate_until() function iterates over the [0, n_iter) range. At each iteration, the generator gen is invoked, with the template integrator as the first argument and the current iteration number as the second argument. The propagate_until() member function is then called on the integrator returned by gen, and the result of the propagation is appended to a list of results which is finally returned by ensemble_propagate_until() once all the propagations have finished.

Let us see a concrete example of ensemble_propagate_until() in action. First, we begin by creating 10 sets of different initial conditions to be used in the ensemble propagations:

    // Create 10 sets of different initial conditions,
    // one for each element of the ensemble.
    std::vector<std::vector<double>> ensemble_ics;
    for (auto i = 0; i < 10; ++i) {
        ensemble_ics.push_back({0.05 + i / 100., 0.025 + i / 100.});
    }

Next, we define a generator that will pick a set of initial conditions from ensemble_ics, depending on the iteration index:

    // The generator.
    auto gen = [&ensemble_ics](taylor_adaptive<double> ta_copy, std::size_t i) {
        ta_copy.get_state_data()[0] = ensemble_ics[i][0];
        ta_copy.get_state_data()[1] = ensemble_ics[i][1];

        return ta_copy;
    };

We are now ready to invoke the ensemble_propagate_until() function:

    // Run the ensemble propagation up to t = 20.
    auto ret = ensemble_propagate_until<double>(ta, 20., 10, gen);

Note that the ensemble_propagate_until() function can be invoked with additional keyword arguments (beside the mandatory initial 4 arguments). Any additional argument will be forwarded to each propagate_until() invocation.

The value returned by ensemble_propagate_until() is a vector of tuples constructed by concatenating the integrator object used for each integration and the tuple returned by each propagate_until() invocation. This way, at the end of an ensemble propagation it is possible to inspect both the state of each integrator object and the output of each invocation of propagate_until() (including, e.g., the continuous output, if requested).

For instance, we can print to screen the integrator used for the last iteration of the ensemble. This is the first object of the ninth tuple in the return value:

    // Print to screen the integrator that was used
    // for the last iteration of the ensemble.
    std::cout << std::get<0>(ret[9]) << '\n';

Tolerance               : 2.2204460492503131e-16
High accuracy           : false
Compact mode            : false
Taylor order            : 20
Dimension               : 2
Time                    : 20.000000000000000
State                   : [0.12257736827306077, 0.24068377640981869]

The other values in the tuple are those returned by the propagate_until() invocation:

    // Print the values returned by the propagate_until()
    // invocation.
    std::cout << "Integration outcome: " << std::get<1>(ret[9]) << '\n';
    std::cout << "Min/max timesteps  : " << std::get<2>(ret[9]) << "/" << std::get<3>(ret[9]) << '\n';
    std::cout << "N of timesteps     : " << std::get<4>(ret[9]) << '\n';
    std::cout << "Continuous output  : " << static_cast<bool>(std::get<5>(ret[9])) << '\n';

Integration outcome: taylor_outcome::time_limit
Min/max timesteps  : 0.158147/0.167025
N of timesteps     : 124
Continuous output  : 0

Thread safety considerations#

Because currently ensemble propagations always use thread-based parallelism, it is important to ensure that all the objects involved in an ensemble propagation provide a certain degree of thread safety. This concerns especially the user-provided function objects such as the generator and the callbacks (if present).

In an ensemble propagation, it is thus important to keep in mind that the following actions may be performed concurrently by separate threads of execution:

  • invocation of the generator’s call operator (that is, the generator is shared among multiple threads and must support concurrent invocations of its call operator);

  • copy construction of the step callbacks and invocation of the call operator on the copies (that is, step callbacks are not shared among multiple threads of execution, and a new copy is created for each invocation of the propagate_*() functions);

  • copy construction of the events callbacks and invocation of the call operator on the copies (that is, each thread of execution gets its own copy of the event callbacks thanks to the creation of a new integrator object via the generator).

For instance, an event callback which performs write operations on a global variable without using some form of synchronisation will result in undefined behaviour when used in an ensemble propagation.

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