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Fundamentals of Accelerated Computing with CUDA C/C++

Nvidia and Nvidia Deep Learning Institute via Independent

Found in Computer Science, C++

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Overview

Learn to use CUDA C/C++ tools and techniques to accelerate CPU-only applications to run on massively parallel GPUs.

What You'll Learn

  • How to GPU-accelerate CPU-only applications with CUDA C/C++
  • An iterative, profiler driven approach to accelerating applications


About This Course

The CUDA computing platform enables the acceleration of CPU-only applications to run on the world's fastest massively parallel GPUs. Experience accelerating C/C++ applications by:

  • Accelerating CPU-only applications to run their latent parallelism on GPUs
  • Utilizing essential CUDA memory management techniques to optimize accelerated applications
  • Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
  • Leveraging command line and visual profiling to guide and check your work

Upon completion of this workshop, you'll be able to accelerate and optimize existing C/C++ CPU-only applications using a number of the most essential CUDA tools and techniques. You will have a keen sense for an iterative style of CUDA development that will allow you to ship accelerated applications fast.
Prerequisites

To successfully complete this course, you should have some basic C/C++ competency.

Syllabus

  • Accelerating Applications with CUDA C/C++
  • Managing Accelerated Application Memory with CUDA C/C++ Unified Memory and nvprof
  • Asynchronous Streaming, and Visual Profiling for Accelerated Applications with CUDA C/C++
  • Next Steps
  • Course Survey
  • Environment Quick Start

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