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

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

Technologies:

NVIDIA® Nsight™, nsys

Certificate:

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.

Workshop Outline

Introduction

• Meet the instructor.
• Create an account at courses.nvidia.com/join

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.