CUDA Ampere Tuning Guide
Type: Guide Tags: NVIDIA, CUDA, Ampere, performance, tuning, async copy, barriers, Tensor Cores, NVLink Related: CUDA-Ampere-Compatibility-Guide, NVIDIA-Ampere-Architecture, CUDA-Best-Practices-Guide, CUDA-Programming-Guide, Nsight-Compute, NVLink, CUDA-Streams Sources: https://docs.nvidia.com/cuda/ampere-tuning-guide/index.html Last Updated: 2026-04-29
Summary
The CUDA Ampere Tuning Guide is NVIDIA’s architecture-specific performance guide for CUDA applications on Ampere GPUs. It covers Ampere tuning topics such as asynchronous global-to-shared memory copy, split arrive/wait barriers, warp-level reductions, Tensor Core improvements, memory hierarchy, and third-generation NVLink.
Detail
Purpose
Ampere retained the CUDA programming model while adding features that changed how high-performance kernels should be written. The tuning guide helps developers move beyond generic CUDA best practices and take advantage of Ampere-specific features.
Key tuning areas
- Asynchronous copy from global memory to shared memory.
- Hardware acceleration for split arrive/wait barriers.
- Warp-level reduction operation support.
- Improved Tensor Core and FP32 throughput.
- Larger memory capacity, HBM bandwidth, L2 capacity, and L2 residency controls.
- Unified shared memory/L1/texture cache behavior.
- Third-generation NVLink.
NVIDIA context
Ampere remains an important installed base for A100-class AI infrastructure. The guide is useful for understanding the performance baseline that Hopper and Blackwell evolved from.
Connections
- CUDA-Ampere-Compatibility-Guide - compatibility guide for Ampere migration.
- NVIDIA-Ampere-Architecture - architecture-level page.
- CUDA-Best-Practices-Guide - general CUDA performance guidance.
- CUDA-Programming-Guide - programming concepts behind async copy, barriers, and memory hierarchy.
- Nsight-Compute - kernel profiler for validating Ampere tuning.
- NVLink - Ampere tuning includes third-generation NVLink behavior.
Source Excerpts
- NVIDIA’s Ampere tuning guide highlights asynchronous copy, split barriers, warp-level reductions, Tensor Core improvements, memory hierarchy changes, and NVLink 3.