I’ve got a Geforce GTX 470 on a linux system, and after building the examples from /opt/cuda/samples in my home folder, I can get the “deviceQuery” example to run just fine, but the “vectorAdd” example fails with the following output:
[Vector addition of 50000 elements] Copy input data from the host memory to the CUDA device CUDA kernel launch with 196 blocks of 256 threads Failed to launch vectorAdd kernel (error code no kernel image is available for execution on the device)!
Here is the output of the deviceQuery example:
CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "GeForce GTX 470" CUDA Driver Version / Runtime Version 9.1 / 9.1 CUDA Capability Major/Minor version number: 2.0 Total amount of global memory: 1217 MBytes (1276575744 bytes) MapSMtoCores for SM 2.0 is undefined. Default to use 64 Cores/SM MapSMtoCores for SM 2.0 is undefined. Default to use 64 Cores/SM (14) Multiprocessors, ( 64) CUDA Cores/MP: 896 CUDA Cores GPU Max Clock rate: 1215 MHz (1.22 GHz) Memory Clock rate: 1674 Mhz Memory Bus Width: 320-bit L2 Cache Size: 655360 bytes Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65535), 3D=(2048, 2048, 2048) Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 32768 Warp size: 32 Maximum number of threads per multiprocessor: 1536 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (65535, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 1 copy engine(s) Run time limit on kernels: Yes Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Disabled Device supports Unified Addressing (UVA): Yes Supports Cooperative Kernel Launch: No Supports MultiDevice Co-op Kernel Launch: No Device PCI Domain ID / Bus ID / location ID: 0 / 3 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.1, CUDA Runtime Version = 9.1, NumDevs = 1 Result = PASS
My best guess is that I need to tell CUDA to compile for version 2.0 (as per CUDA capability), but I can’t find anything online to this effect. What am I doing wrong, or how can I further diagnose the problem?