To fully harness the capabilities of NVIDIA GPUs, optimizing NVIDIA CUDA performance is essential, particularly for developers new to GPU programming. This talk is specifically designed for those stepping into the world of CUDA, providing a solid foundation in GPU architecture principles and optimization techniques.
Athena Elafrou, a developer technology engineer at NVIDIA, leads a foundational session that dives into the basics of writing high-performance CUDA kernels tailored for NVIDIA GPUs. You’ll gain insights into critical aspects of GPU architecture, focusing on the NVIDIA H200 Tensor Core GPU, and learn how to use its features to enhance performance.
Follow along with a PDF of the session, which emphasizes fundamental memory access optimization techniques, where you’ll discover how to boost memory throughput by aligning and coalescing memory accesses. It also explores strategies to increase parallelism in your applications by improving instruction-level parallelism (ILP) and thread-level parallelism (TLP), key techniques for hiding latencies, and maximizing the overall throughput of your CUDA programs.
Additionally, you’ll learn how to manage atomic operations efficiently through practical examples and tested optimization techniques.
You’ll walk through real-world examples and performance analyses to provide you with actionable knowledge that you can directly apply to your CUDA development work. Whether you’re just starting with CUDA or looking to refine your skills, this session will equip you with the tools needed to unlock the power of NVIDIA GPUs.
Watch the talk Introduction to CUDA Programming and Performance Optimization, explore more videos on NVIDIA On-Demand, and gain valuable skills and insights from industry experts by joining the NVIDIA Developer Program.
This content was partially crafted with the assistance of generative AI and LLMs. It underwent careful review and was edited by the NVIDIA Technical Blog team to ensure precision, accuracy, and quality.
