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

Course Overview

In this one-day, instructor-led CUDA course in Washington, DC Metro, Tysons Corner, VA, Columbia, MD or Live Online, students will learn the fundamental tools and techniques for accelerating C/C++ applications to run on massively parallel GPUs with CUDA®. Participants will learn how to write code, configure code parallelization with CUDA, optimize memory migration between the CPU and GPU accelerator, and implement the workflow that they’ve learned on a new task—accelerating a fully functional, but CPU-only, particle simulator for observable massive performance gains. After taking this course, learners will 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 their 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

Schedule

Currently, there are no public classes scheduled. Please contact a LEXX LIVETraining Consultant to discuss hosting a private class at 301-258-8200.

Program Level

Beginner

Prerequisites

All learners are expected to have:

  • 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 Outline

Module 1:  Introduction

Module 2: 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.

Module 3: 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.

Module 4: 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.

Module 5: Final Review

 

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LEXX Live is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints re-garding registered sponsors may be submitted to the National Registry of CPE Sponsors through its web site: www.nasbaregistry.org

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Starting at $500

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