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Course Overview
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.
Who Should Attend
Developers
Course Objectives
- 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
- Course Introduction
- Accelerating Applications with CUDA C/C++
- Managing Accelerated Application Memory with CUDA C/C++
- Asynchronous Streaming and Visual Profiling for Accelerated Applications with CUDA C/C++
- Course Summary