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Fundamentals of Accelerated Computing with CUDA C/C++

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...

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$500 USD
Course Code NV-ACC-CUDA-C
Duration 1 day
Available Formats Classroom, Virtual

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.

Skills Gained

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

Prerequisites

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

Course Details

Workshop 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

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