Fundamentals Of Accelerated Computing With Cuda C/C++ (NFACWC)

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.

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


  • Basic C/C++ competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations
  • No previous knowledge of CUDA programming is assumed


NVIDIA® Nsight™, nsys


Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.

Hardware Requirements:

Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud.

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Workshop Outline


  • Meet the instructor.
  • Create an account at

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.