PRUDA: An API for Time and Space Predictible Programming in NVDIA GPUs using CUDA

Tekin, Reyyan; Zahaf, Houssam-Eddine; Lipari, Giuseppe

Document type :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

Title :

PRUDA: An API for Time and Space Predictible Programming in NVDIA GPUs using CUDA

Author(s) :

Tekin, Reyyan [Auteur]
Zahaf, Houssam-Eddine [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Lipari, Giuseppe [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Conference title :

Junior Workshop: JRWRTC - Real-Time Networks and Systems 2019

City :

Toulouse

Country :

France

Start date of the conference :

2019-11-07

HAL domain(s) :

Informatique [cs]/Systèmes embarqués
Informatique [cs]/Système d'exploitation [cs.OS]

English abstract : [en]

Recent computing platforms combine CPUs with different types of accelerators such as Graphical Processing Units (GPUs) to cope with the increasing computation power needed by complex real-time applications. NVIDIA GPUs are ...
Show more >Recent computing platforms combine CPUs with different types of accelerators such as Graphical Processing Units (GPUs) to cope with the increasing computation power needed by complex real-time applications. NVIDIA GPUs are compound of hundreds of computing elements called CUDA cores, to achieve fast computations for parallel applications. However, GPUs are not designed to support real-time execution , as their main goal is to achieve maximum through-put for their resources. Supporting real-time execution on NVIDIA GPUs involves not only achieving timely predictable calculations but also to optimize the CUDA cores usage. In this work, we present the design and the implementation of PRUDA (Predictable Real-time CUDA), a programming platform to manage the GPU resources, therefore decide when and where a real-time task is executed. PRUDA is written in C and provides different mechanisms to manage the task priorities and allocation on the GPU. It provides tools to help a designer to properly implement real-time schedulers on the top of CUDA.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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