Multiple Task Implementations

As in Java COMPSs applications, it is possible to define multiple implementations for each task. In particular, a programmer can define a task for a particular purpose, and multiple implementations for that task with the same objective, but with different constraints (e.g. specific libraries, hardware, etc). To this end, the @implement (or @Implement) decorator followed with the specific implementations constraints (with the @constraint decorator, see Section [subsubsec:constraints]) needs to be placed ON TOP of the @task decorator. Although the user only calls the task that is not decorated with the @implement decorator, when the application is executed in a heterogeneous distributed environment, the runtime will take into account the constraints on each implementation and will try to invoke the implementation that fulfills the constraints within each resource, keeping this management invisible to the user (Code 109).

Code 109 Multiple task implementations example

from pycompss.api.implement import implement

@implement(source_class="sourcemodule", method="main_func")
@constraint(app_software="numpy")
@task(returns=list)
def myfunctionWithNumpy(list1, list2):
    # Operate with the lists using numpy
    return resultList

@task(returns=list)
def main_func(list1, list2):
    # Operate with the lists using built-int functions
    return resultList

Please, note that if the implementation is used to define a binary, OmpSs, MPI, COMPSs, multinode or reduction task invocation (see Other task types), the @implement decorator must be always on top of the decorators stack, followed by the @constraint decorator, then the @binary/@ompss/@mpi/@compss/@multinode decorator, and finally, the @task decorator in the lowest level.