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?