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 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.
Anglais
Moyens Pédagogiques :