CUDA编译设置

CUDA版本和GPU卡的对应关系，以及编译设置：

• Fermi (CUDA 3.2 until CUDA 8) (deprecated from CUDA 9):

SM20 or SM_20, compute_30 – Older cards such as GeForce 400, 500, 600, GT-630

Kepler (CUDA 5 and later):

SM30 or SM_30, compute_30 – Kepler architecture (generic – Tesla K40/K80, GeForce 700, GT-730)
Adds support for unified memory programming

SM35 or SM_35, compute_35 – More specific Tesla K40
Adds support for dynamic parallelism. Shows no real benefit over SM30 in my experience.

SM37 or SM_37, compute_37 – More specific Tesla K80
Adds a few more registers. Shows no real benefit over SM30 in my experience

• Maxwell (CUDA 6 and later):

SM50 or SM_50, compute_50 – Tesla/Quadro M series

SM52 or SM_52, compute_52 – Quadro M6000 , GeForce 900, GTX-970, GTX-980, GTX Titan X

SM53 or SM_53, compute_53 – Tegra (Jetson) TX1 / Tegra X1

• Pascal (CUDA 8 and later)

SM60 or SM_60, compute_60 – Quadro GP100, Tesla P100, DGX-1 (Generic Pascal)

SM61 or SM_61, compute_61 – GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4, Discrete GPU on the NVIDIA Drive PX2

SM62 or SM_62, compute_62 – Integrated GPU on the NVIDIA Drive PX2, Tegra (Jetson) TX2

• Volta (CUDA 9 and later)

SM70 or SM_70, compute_70 – DGX-1 with Volta, Tesla V100, GTX 1180 (GV104), Titan V, Quadro GV100

SM72 or SM_72, compute_72 – Jetson AGX Xavier

• Turing (CUDA 10 and later)

SM75 or SM_75, compute_75 – GTX Turing – GTX 1660 Ti, RTX 2060, RTX 2070, RTX 2080, Titan RTX, Quadro RTX 4000, Quadro RTX 5000, Quadro RTX 6000, Quadro RTX 8000,Quadro T1000/T2000, Tesla T4

• Ampere(CUDA 11.1 and later)

SM80 or SM_80, compute_80 – A100, DGX-A100

SM86 or SM_86, compute_86 – Tesla GA10x cards, RTX Ampere – RTX 3080, GA102 – RTX 3090, RTX A2000, A3000, A4000, A5000, A6000, NVIDIA A40, GA106 – RTX 3060, GA104 – RTX 3070, GA107 – RTX 3050, Quadro A10, Quadro A16, Quadro A40, A2 Tensor Core GPU