Fundamentals Of Accelerated Computing With Cuda C/C++ (NFACWC)
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.
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
- Basic C/C++ competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations
- No previous knowledge of CUDA programming is assumed
NVIDIA® Nsight™, nsys
Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.
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.
- 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.
- Review key learnings and wrap up questions.
- Complete the assessment to earn a certificate.
- Take the workshop survey.