CUDA failed to launch kernel : no kernel image available for execution - cuda

Question

i am trying to run CUDA on a rather old GPU. I tried the CUDA Samples vectorAdd which gives me the following error:
Failed to launch vectorAdd kernel (error code no kernel image is available for execution on the device)!
These are the outputs from
deviceQuery:
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX 580"
CUDA Driver Version / Runtime Version 9.1 / 9.0
CUDA Capability Major/Minor version number: 2.0
Total amount of global memory: 1467 MBytes (1538392064 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
(16) Multiprocessors, ( 64) CUDA Cores/MP: 1024 CUDA Cores
GPU Max Clock rate: 1630 MHz (1.63 GHz)
Memory Clock rate: 2050 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 786432 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 = 9.0, NumDevs = 1
Result = PASS
nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.147 Driver Version: 390.147 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 580 Off | 00000000:03:00.0 N/A | N/A |
| 42% 48C P12 N/A / N/A | 257MiB / 1467MiB | N/A Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 Not Supported |
+-----------------------------------------------------------------------------+
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176
Now according to the CUDA compatibility PDF
https://docs.nvidia.com/pdf/CUDA_Compatibility.pdf
I assume I have Binary Compatibility from CUDA 9.0.176 to the GPU Driver.
For Compute Capability Support, the table does not list the 390 Driver.
Is it even possible to program CUDA on this GPU or should I get a newer one? If it is possible, what combination of driver and CUDA toolkit version do I need?

Answer 1

The GPU you are using is a Fermi class (compute capability 2.0) device. Support was officially removed from the CUDA toolkit when CUDA 9.0 was released in September 2017. The last release of the CUDA toolkit with Fermi support was CUDA 8.0. You will have to use that (or something even older) if you wish to use that GPU with CUDA.
[Answer assembled from comments an added as a community wiki entry to get this question off the unanswered list for the CUDA tag]