Fundamentals of Accelerated Computing with CUDA C/C++ Training

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.
Course Details


1 day


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

Skills Gained

  • 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
Course Outline
  • Introduction
  • 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