Fundamentals of Accelerated Computing with CUDA C/C++ (FACCC)

 

Résumé du cours

This workshop teaches the fundamental tools and techniques for accelerating C/C++ applications to run on massively parallel GPUs with CUDA®. You’ll learn how to write code, configure code parallelization with CUDA, optimize memory migration between the CPU and GPU accelerator, and implement the workflow that you’ve learned on a new task—accelerating a fully functional, but CPU-only, particle simulator for observable massive performance gains. At the end of the workshop, you’ll have access to additional resources to create new GPU-accelerated applications on your own.

Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.

Moyens Pédagogiques :
  • Quiz pré-formation de vérification des connaissances (si applicable)
  • Réalisation de la formation par un formateur agréé par l’éditeur
  • Formation réalisable en présentiel ou en distanciel
  • Mise à disposition de labs distants/plateforme de lab pour chacun des participants (si applicable à la formation)
  • Distribution de supports de cours officiels en langue anglaise pour chacun des participants
    • Il est nécessaire d'avoir une connaissance de l'anglais technique écrit pour la compréhension des supports de cours
Moyens d'évaluation :
  • Quiz pré-formation de vérification des connaissances (si applicable)
  • Évaluations formatives pendant la formation, à travers les travaux pratiques réalisés sur les labs à l’issue de chaque module, QCM, mises en situation…
  • Complétion par chaque participant d’un questionnaire et/ou questionnaire de positionnement en amont et à l’issue de la formation pour validation de l’acquisition des compétences

Objectifs

At the conclusion of the workshop, you’ll have an understanding of the fundamental tools and techniques for GPU-accelerating C/C++ applications with CUDA and be able to:

  • Write code to be executed by a GPU accelerator
  • Expose and express data and instruction-level parallelism in C/C++ applications using CUDA
  • Utilize CUDA-managed memory and optimize memory migration using asynchronous prefetching
  • Leverage command-line and visual profilers to guide your work
  • Utilize concurrent streams for instruction-level parallelism
  • Write GPU-accelerated CUDA C/C++ applications, or refactor existing CPU-only applications, using a profile-driven approach

Suite de parcours

Contenu

Introduction

  • Meet the instructor.
  • Create an account at courses.nvidia.com/join

Accelerating Applications with CUDA C/C++

  • Learn the essential syntax and concepts to be able to write GPU-enabled C/C++ applications with CUDA:
  • Write, compile, and run GPU code.
  • Control parallel thread hierarchy.
  • Allocate and free memory for the GPU.

Managing Accelerated Application Memory with CUDA C/C++

  • Learn the command-line profiler and CUDA-managed memory, focusing on observation-driven application improvements and a deep understanding of managed memory behavior:
  • Profile CUDA code with the command-line profiler.
  • Go deep on unified memory.
  • Optimize unified memory management.

Asynchronous Streaming and Visual Profiling for Accelerated Applications with CUDA C/C++

  • Identify opportunities for improved memory management and instruction-level parallelism:
  • Profile CUDA code with NVIDIA Nsight Systems.
  • Use concurrent CUDA streams.

Final Review

  • Review key learnings and wrap up questions.
  • Complete the assessment to earn a certificate.
  • Take the workshop survey.

Prix & Delivery methods

Formation en ligne

Durée
1 jour

Prix
  • US $ 500,–
Dates et Inscription
Demande de date

Actuellement aucune session planifiée