CUDA Turing Tuning Guide
Type: Guide Tags: NVIDIA, CUDA, Turing, performance, tuning, independent thread scheduling, memory, Tensor Cores Related: CUDA-Turing-Compatibility-Guide, NVIDIA-Turing-Architecture, CUDA-Best-Practices-Guide, CUDA-Programming-Guide, Nsight-Compute, NVIDIA-RTX Sources: https://docs.nvidia.com/cuda/turing-tuning-guide/index.html Last Updated: 2026-04-29
Summary
The CUDA Turing Tuning Guide is NVIDIA’s architecture-specific performance guide for CUDA applications on Turing GPUs. It captures the CUDA tuning guidance for the generation that introduced RTX-era hardware alongside CUDA compute capability 7.5.
Detail
Purpose
Turing tuning matters for long-lived CUDA applications deployed on RTX, workstation, embedded, and inference environments. The guide explains what developers should account for after applying general CUDA best practices.
Key tuning areas
- Streaming multiprocessor occupancy and execution behavior.
- Independent thread scheduling considerations inherited from Volta-era changes.
- Memory hierarchy and cache behavior.
- Tensor Core behavior available on Turing GPUs.
- Profiling and validation through Nsight-Compute.
NVIDIA context
Turing is older than Ampere, Hopper, and Blackwell, but it is still represented in current CUDA docs. This page keeps the wiki complete for multi-generation CUDA support questions without making old toolkit releases canonical.
Connections
- CUDA-Turing-Compatibility-Guide - compatibility companion for Turing migration.
- NVIDIA-Turing-Architecture - architecture-level Turing page.
- CUDA-Best-Practices-Guide - general CUDA performance guidance.
- CUDA-Programming-Guide - execution, memory, and synchronization reference.
- Nsight-Compute - kernel profiler for tuning validation.
- NVIDIA-RTX - RTX-era GPUs include Turing generation hardware.
Source Excerpts
- NVIDIA’s Turing tuning guide is the current architecture-specific CUDA tuning reference for Turing GPUs.