Enhanced performance using hybrid programming models of task-based workflows and MPI
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/331014
Tipus de documentText en actes de congrés
Data publicació2020-05
EditorBarcelona Supercomputing Center
Condicions d'accésAccés obert
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Abstract
While MPI [1] + X (where X is another parallel programming
model) has been proposed and used by the community,
we propose a hybrid programming model that combines taskbased
model + MPI. Task-based workflows offer the necessary
abstraction to simplify the application development for large
scale execution, and supporting tasks that launch MPI executions
enables to exploit the performance capabilities of manycore
systems. Hence, application programmers can get the
maximum performance out of the underlying systems without
compromising the programmability of the application.
We present an extension to PyCOMPSs framework [2],
a task-based parallel programming model for the execution
of Python applications. Throughout this paper, we name the
tasks that natively execute MPI code as Native MPI Tasks,
as opposed to tasks that call external MPI binaries. Having
Native MPI tasks as part of the programming model means
that in the same source file users can have two types of task:
tasks that execute MPI code and other tasks that execute non-
MPI code. PyCOMPSs organizes the tasks in Directed Acyclic
Graph (DAG) and manages their scheduling and execution,
hence users can focus only on the logic of the task.
CitacióElshazly, H.M.A.E.; Badia Sala, R.M. Enhanced performance using hybrid programming models of task-based workflows and MPI. A: . Barcelona Supercomputing Center, 2020, p. 41-42.
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