pydcop.distribution.ilp_fgdp

distribute(computation_graph: ComputationGraph, agentsdef: Iterable[AgentDef], hints: DistributionHints | None = None, computation_memory=None, communication_load=None, timeout=None)

Generate a distribution for the dcop.

Parameters:

  • computation_graph – a ComputationGraph

  • agentsdef – the agents definitions

  • hints – a DistributionHints

  • computation_memory – a function that takes a computation node as an argument and return the memory footprint for this

  • link_communication – a function that takes a Link as an argument and return the communication cost of this edge

  • timeout – ignored

distribution_cost(distribution: Distribution, computation_graph: ComputationGraph, agentsdef: Iterable[AgentDef], computation_memory: Callable[[ComputationNode], float], communication_load: Callable[[ComputationNode, str], float]) → float

Compute the cost for a distribution.

In this model, the cost only includes the communication costs based on message size.

Parameters:

  • distribution –

  • computation_graph –

  • agentsdef –

  • computation_memory –

  • communication_load –

factor_graph_lp_model(cg: ComputationsFactorGraph, agents: List[AgentDef], must_host: Dict[str, List], computation_memory=None, communication_load=None)

To distribute we need: * com : the communication cost of an edge between a var and a fact * mem_var : the memory footprint of a variable computation * mem_fac : the memory footprint of a factor computation

These function depends on the algorithm.

Here * mem_var and mem_fac are given by the computation_memory method. * com is given by computation_memory

Returns: