Nvidia Fundamentals of Accelerated Computing with CUDA C/C++ (FACCC)

Ziele der Schulung

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.

Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.

Zielgruppe Seminar

This course is designed for developers, engineers, and researchers who want to accelerate their applications using GPU programming with CUDA C/C++. It is ideal for professionals working in high-performance computing (HPC), scientific simulations, deep learning, and data analytics. Participants should have a basic understanding of C/C++ programming and an interest in learning how to leverage GPUs for performance optimization.

Voraussetzungen

• Basic knowledge of C or C++ programming.
• Familiarity with parallel computing concepts is helpful but not required.
• No prior experience with CUDA programming is necessary.

Seminarinhalt

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.

Hinweise

Partner

Dieses Seminar bieten wir in Kooperation mit unserem Nvidia Learning Partner Fast Lane Institute for Knowledge Transfer GmbH an.

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