Task Constraints

It is possible to define constraints for each task. To this end, the @constraint (or @Constraint) decorator followed by the desired constraints needs to be placed ON TOP of the @task decorator (Code 99).

Important

Please note the the order of @constraint and @task decorators is important.

Code 99 Constrained task example

from pycompss.api.task import task
from pycompss.api.constraint import constraint
from pycompss.api.parameter import INOUT

@constraint(computing_units="4")
@task(c=INOUT)
def func(a, b, c):
     c += a * b
     ...

This decorator enables the user to set the particular constraints for each task, such as the amount of Cores required explicitly. Alternatively, it is also possible to indicate that the value of a constraint is specified in a environment variable (Code 100).

For example:

Code 100 Constrained task with environment variable example

from pycompss.api.task import task
from pycompss.api.constraint import constraint
from pycompss.api.parameter import INOUT

@constraint(computing_units="4",
            app_software="numpy,scipy,gnuplot",
            memory_size="$MIN_MEM_REQ")
@task(c=INOUT)
def func(a, b, c):
     c += a * b
     ...

Or another example requesting a CPU core and a GPU (Code 101).

Code 101 CPU and GPU constrained task example

from pycompss.api.task import task
from pycompss.api.constraint import constraint

@constraint(processors=[{'processorType':'CPU', 'computingUnits':'1'},
                        {'processorType':'GPU', 'computingUnits':'1'}])
@task(returns=1)
def func(a, b, c):
     ...
     return result

When the task requests a GPU, COMPSs provides the information about the assigned GPU through the COMPSS_BINDED_GPUS, CUDA_VISIBLE_DEVICES and GPU_DEVICE_ORDINAL environment variables. This information can be gathered from the task code in order to use the GPU.

Please, take into account that in order to respect the constraints, the peculiarities of the infrastructure must be defined in the resources.xml file.

Supported constraints

A full description of the supported constraints can be found in Table 14.

Dynamic constraints

The dynamic constraints are supported for computing units, memory size and disk size. In order to define a constraint as dynamic, instead of setting a static value or environment variable, the user needs to set the name of the global variable desired for the constraint.

Code 102 dynamic_constraint task example

from pycompss.api.task import task
from pycompss.api.constraint import constraint
MS = 1

@constraint(memory_size="MS")
@task()
def func(a, b, c):
     c += a * b
     ...

Having the dynamic constraint defined with a global variable, the user now has the ability to change the global variable value between task calls, in order to have different constraint values for each call.

Code 103 dynamic_constraint task call example

def main():
    global MS
    {...}
    func(a, b, c)
    MS = 2
    func(a, b, c)
    MS = a + b * c
    func(a, b, c)

It is possible to define dynamic constraints using alternative syntax (Alternative ways to define dynamic constraints).

Special constraints

There is a special constraint when considering the COMPSs agents deployment (Agents Deployments) to specify that the task MUST be executed in the node that received the task. This constraint is indicated in the @constraint decorator with the is_local argument equal a boolean (True or False) (Code 108) in addition to other constraints.

Code 108 is_local task example

from pycompss.api.task import task
from pycompss.api.constraint import constraint

@constraint(is_local=True)
@task(c=INOUT)
def func(a, b, c):
     c += a * b

Important

The is_local constraint has NO effect with the default COMPSs deployment (master-workers) (Master-Worker Deployments).