EASYPAP: a Framework for Learning Parallel Programming

Alice Lasserre (1) , Raymond Namyst (1) , Pierre-André Wacrenier (2, 1)

1 UB - Université de Bordeaux
2 STORM - STatic Optimizations, Runtime Methods

This paper presents EASYPAP, an easy-to-use programming environment designed to help students to learn parallel programming. EASYPAP features a wide range of 2D computation kernels that the students are invited to parallelize using Pthreads, OpenMP, OpenCL or MPI. Execution of kernels can be interactively visualized, and powerful monitoring tools allow students to observe both the scheduling of computations and the assignment of 2D tiles to threads/processes. By focusing on algorithms and data distribution, students can experiment with diverse code variants and tune multiple parameters, resulting in richer problem exploration and faster progress towards efficient solutions. We present selected lab assignments which illustrate how EASYPAP improves the way students explore parallel programming.

Mots clés: Parallel programming; Visualization; Monitoring; Education; OpenMP; MPI

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Alice Lasserre, Raymond Namyst, Pierre-André Wacrenier. EASYPAP: a Framework for Learning Parallel Programming. 2020. ⟨hal-02469919⟩