HPC for Idealists with Deadlines: Pragmatic Abstractions for High Performance

Durée : 01:18:02 -Réalisation : 16 novembre 2022 -Mise en ligne : 19 novembre 2022
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Development of high performance code often necessitates compromise. Lowered computational cost comes at the expense of other worthy goals, including intelligibility and maintainability of programs. We discuss a "ladder" of tools ranging from low- to high-level, aiming to reduce these sacrifices in machine independence, readability, and separation of concerns, all while enabling "near-handwritten" performance to be attained. These tools include PyOpenCL, offering access to the OpenCL compute abstraction and open-source implementations thereof, including pocl. They include loopy, a polyhedrally-based code transformation tool for CPU and GPU code. And they include pytato, which captures data flow graphs of numpy-compatible array computations for transformation and processing via loopy. Moderate-to-large-scale examples illustrate successful uses of these tools.

Lieu de réalisation
En ligne
Langue :
Andreas Klöckner (Intervenant)
Conditions d'utilisation
Droit commun de la propriété intellectuelle
Citer cette ressource :
Andreas Klöckner. GroupeCalcul. (2022, 16 novembre). HPC for Idealists with Deadlines: Pragmatic Abstractions for High Performance. [Vidéo]. Canal-U. (Consultée le 26 mars 2023)