Implementing your algorithm

To run your own Monte Carlo algorithm with Carlo, you need to implement the AbstractMC interface documented in this file. For an example implementation showcasing all the features, take a look at the Ising example implementation.

Carlo.AbstractMC — Type

This type is an interface for implementing your own Monte Carlo algorithm that will be run by Carlo.

source

The following methods all need to be defined for your Monte Carlo algoritm type (here referred to as YourMC <: AbstractMC). See Parallel run mode for a slightly different interface that allows inner MPI parallelization of your algorithm.

Carlo.init! — Function

Carlo.init!(mc::YourMC, ctx::MCContext, params::AbstractDict)

Executed when a simulation is started from scratch.

source

Carlo.sweep! — Function

Carlo.sweep!(mc::YourMC, ctx::MCContext)

Perform one Monte Carlo sweep or update to the configuration.

Note

Doing measurements is supported during this step as some algorithms require doing so for efficiency. Remember to check for is_thermalized in that case.

source

Carlo.measure! — Function

Carlo.measure!(mc::YourMC, ctx::MCContext)

Perform one Monte Carlo measurement.

source

Carlo.write_checkpoint — Function

Carlo.write_checkpoint(mc::YourMC, out::HDF5.Group)

Save the complete state of the simulation to out.

source

Carlo.read_checkpoint! — Function

Carlo.read_checkpoint!(mc::YourMC, in::HDF5.Group)

Read the state of the simulation from in.

source

Carlo.register_evaluables — Function

Carlo.register_evaluables(mc::Type{YourMC}, eval::AbstractEvaluator, params::AbstractDict)

This function is used to calculate postprocessed quantities from quantities that were measured during the simulation. Common examples are variances or ratios of observables.

See evaluables for more details.

source

Interfacing with Carlo features

The MCContext type, passed to your code by some of the functions above enables to use some features provided by Carlo.

Carlo.MCContext — Type

Holds the Carlo-internal state of the simulation and provides an interface to

Random numbers: the public field MCContext.rng is a random number generator (see rng)
Measurements: see measure!(::MCContext, ::Symbol, ::Any)
Simulation state: see is_thermalized

source

Carlo.is_thermalized — Function

is_thermalized(ctx::MCContext)::Bool

Returns true if the simulation is thermalized.

source

Carlo.measure! — Method

measure!(ctx::MCContext, name::Symbol, value)

Measure a sample for the observable named name. The sample value may be either a scalar or vector of a float type.

source