Hello!
I have one machine on which I will be develop applications by using CUDA Toolkit.
Currently I have next environment:
$ lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation GF108 [GeForce GT 430] (rev a1)
01:00.1 Audio device: NVIDIA Corporation GF108 High Definition Audio Controller (rev a1)
$ cat /proc/driver/nvidia/version
NVRM version: NVIDIA UNIX x86_64 Kernel Module 390.48 Thu Mar 22 00:42:57 PDT 2018
GCC version: gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.9)
$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Tue_Jan_10_13:22:03_CST_2017
Cuda compilation tools, release 8.0, V8.0.61
To verify my environment after installation I built deviceQuery example and checked it:
$ ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GT 430"
CUDA Driver Version / Runtime Version 9.1 / 8.0
CUDA Capability Major/Minor version number: 2.1
Total amount of global memory: 1980 MBytes (2076508160 bytes)
( 2) Multiprocessors, ( 48) CUDA Cores/MP: 96 CUDA Cores
GPU Max Clock rate: 1460 MHz (1.46 GHz)
Memory Clock rate: 667 Mhz
Memory Bus Width: 128-bit
L2 Cache Size: 131072 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
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 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 = 8.0, NumDevs = 1, Device0 = GeForce GT 430
Result = PASS
Then I move the deviceQuery app to another machine with next environment:
$ lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation Device 1b06 (rev a1)
01:00.1 Audio device: NVIDIA Corporation Device 10ef (rev a1)
02:00.0 VGA compatible controller: NVIDIA Corporation Device 1b06 (rev a1)
02:00.1 Audio device: NVIDIA Corporation Device 10ef (rev a1)
$ cat /proc/driver/nvidia/version
NVRM version: NVIDIA UNIX x86_64 Kernel Module 375.82 Wed Jul 19 21:16:49 PDT 2017
GCC version: gcc version 6.3.0 20170516 (Debian 6.3.0-18+deb9u1)
and run the deviceQuery app but got this error:
$ ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
cudaGetDeviceCount returned 35
-> CUDA driver version is insufficient for CUDA runtime version
Result = FAIL
I also copy lib folder from /usr/local/cuda-8.0/ alongside the deviceQuery app and run next command:
LD_LIBRARY_PATH=lib64:$LD_LIBRARY_PATH ./deviceQuery
but I still get the same error.
So, what I need to do for running CUDA applications on other machines, not only on development environment?