his 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.

Learning Objectives

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

Detailed course description: https://www.nvidia.com/en-us/training/instructor-led-workshops/fundamentals-of-accelerated-computing-with-cuda/