我可以调用什么 utility/binary 来确定 nVIDIA GPU 计算能力?
What utility/binary can I call to determine an nVIDIA GPU's Compute Capability?
假设我有一个安装了单个 GPU 的系统,并且假设我还安装了最新版本的 CUDA。
我想确定我的 GPU 的计算能力是多少。如果我可以编译代码,那会很容易:
#include <stdio.h>
int main() {
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, 0);
printf("%d", prop.major * 10 + prop.minor);
}
但是 - 假设我想 无需 编译。我可以吗?我认为 nvidia-smi
可能对我有帮助,因为它可以让您查询有关设备的各种信息,但它似乎不会让您获得计算能力。也许我还能做些什么?也许可以通过 /proc
或系统日志看到某些内容?
编辑: 这旨在 运行 在构建之前,在我无法控制的系统上。因此它必须具有最小的依赖性,运行 在命令行上并且不需要 root 权限。
不幸的是,目前看来答案是“否”,需要编译程序或使用在别处编译的二进制文件。
编辑: 我已经针对这个问题采用了一种解决方法 - 一个自包含的 bash script,它编译一个小型内置 C 程序以确定计算能力。 (用 CMake 调用特别有用,但只能 运行 独立。)
此外,我已经就此事提交了 feature-requesting bug report at nVIDIA。
这是脚本,假设 nvcc
在您的路径上:
//usr/bin/env nvcc --run "[=10=]" ${1:+--run-args "${@:1}"} ; exit $?
#include <cstdio>
#include <cstdlib>
#include <cuda_runtime_api.h>
int main(int argc, char *argv[])
{
cudaDeviceProp prop;
cudaError_t status;
int device_count;
int device_index = 0;
if (argc > 1) {
device_index = atoi(argv[1]);
}
status = cudaGetDeviceCount(&device_count);
if (status != cudaSuccess) {
fprintf(stderr,"cudaGetDeviceCount() failed: %s\n", cudaGetErrorString(status));
return -1;
}
if (device_index >= device_count) {
fprintf(stderr, "Specified device index %d exceeds the maximum (the device count on this system is %d)\n", device_index, device_count);
return -1;
}
status = cudaGetDeviceProperties(&prop, device_index);
if (status != cudaSuccess) {
fprintf(stderr,"cudaGetDeviceProperties() for device device_index failed: %s\n", cudaGetErrorString(status));
return -1;
}
int v = prop.major * 10 + prop.minor;
printf("%d\n", v);
}
您可以使用 cuda 安装中包含的 deviceQuery
实用程序
# change cwd into utility source directoy
$ cd /usr/local/cuda/samples/1_Utilities/deviceQuery
# build deviceQuery utility with make as root
$ sudo make
# run deviceQuery
$ ./deviceQuery | grep Capability
CUDA Capability Major/Minor version number: 7.5
# optionally copy deviceQuery in ~/bin for future use
$ cp ./deviceQuery ~/bin
使用 RTX 2080 Ti 的 deviceQuery 的完整输出如下:
$ ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce RTX 2080 Ti"
CUDA Driver Version / Runtime Version 11.2 / 10.2
CUDA Capability Major/Minor version number: 7.5
Total amount of global memory: 11016 MBytes (11551440896 bytes)
(68) Multiprocessors, ( 64) CUDA Cores/MP: 4352 CUDA Cores
GPU Max Clock rate: 1770 MHz (1.77 GHz)
Memory Clock rate: 7000 Mhz
Memory Bus Width: 352-bit
L2 Cache Size: 5767168 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 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: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1024
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): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 3 copy engine(s)
Run time limit on kernels: No
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 supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: 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 = 11.2, CUDA Runtime Version = 10.2, NumDevs = 1
Result = PASS
谢谢。
我们可以使用nvidia-smi --query-gpu=compute_cap --format=csv
来获取计算能力。
示例输出:
compute_cap
8.6
可用于 cuda 工具包 11.6。
假设我有一个安装了单个 GPU 的系统,并且假设我还安装了最新版本的 CUDA。
我想确定我的 GPU 的计算能力是多少。如果我可以编译代码,那会很容易:
#include <stdio.h>
int main() {
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, 0);
printf("%d", prop.major * 10 + prop.minor);
}
但是 - 假设我想 无需 编译。我可以吗?我认为 nvidia-smi
可能对我有帮助,因为它可以让您查询有关设备的各种信息,但它似乎不会让您获得计算能力。也许我还能做些什么?也许可以通过 /proc
或系统日志看到某些内容?
编辑: 这旨在 运行 在构建之前,在我无法控制的系统上。因此它必须具有最小的依赖性,运行 在命令行上并且不需要 root 权限。
不幸的是,目前看来答案是“否”,需要编译程序或使用在别处编译的二进制文件。
编辑: 我已经针对这个问题采用了一种解决方法 - 一个自包含的 bash script,它编译一个小型内置 C 程序以确定计算能力。 (用 CMake 调用特别有用,但只能 运行 独立。)
此外,我已经就此事提交了 feature-requesting bug report at nVIDIA。
这是脚本,假设 nvcc
在您的路径上:
//usr/bin/env nvcc --run "[=10=]" ${1:+--run-args "${@:1}"} ; exit $?
#include <cstdio>
#include <cstdlib>
#include <cuda_runtime_api.h>
int main(int argc, char *argv[])
{
cudaDeviceProp prop;
cudaError_t status;
int device_count;
int device_index = 0;
if (argc > 1) {
device_index = atoi(argv[1]);
}
status = cudaGetDeviceCount(&device_count);
if (status != cudaSuccess) {
fprintf(stderr,"cudaGetDeviceCount() failed: %s\n", cudaGetErrorString(status));
return -1;
}
if (device_index >= device_count) {
fprintf(stderr, "Specified device index %d exceeds the maximum (the device count on this system is %d)\n", device_index, device_count);
return -1;
}
status = cudaGetDeviceProperties(&prop, device_index);
if (status != cudaSuccess) {
fprintf(stderr,"cudaGetDeviceProperties() for device device_index failed: %s\n", cudaGetErrorString(status));
return -1;
}
int v = prop.major * 10 + prop.minor;
printf("%d\n", v);
}
您可以使用 cuda 安装中包含的 deviceQuery
实用程序
# change cwd into utility source directoy
$ cd /usr/local/cuda/samples/1_Utilities/deviceQuery
# build deviceQuery utility with make as root
$ sudo make
# run deviceQuery
$ ./deviceQuery | grep Capability
CUDA Capability Major/Minor version number: 7.5
# optionally copy deviceQuery in ~/bin for future use
$ cp ./deviceQuery ~/bin
使用 RTX 2080 Ti 的 deviceQuery 的完整输出如下:
$ ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce RTX 2080 Ti"
CUDA Driver Version / Runtime Version 11.2 / 10.2
CUDA Capability Major/Minor version number: 7.5
Total amount of global memory: 11016 MBytes (11551440896 bytes)
(68) Multiprocessors, ( 64) CUDA Cores/MP: 4352 CUDA Cores
GPU Max Clock rate: 1770 MHz (1.77 GHz)
Memory Clock rate: 7000 Mhz
Memory Bus Width: 352-bit
L2 Cache Size: 5767168 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 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: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1024
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): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 3 copy engine(s)
Run time limit on kernels: No
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 supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: 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 = 11.2, CUDA Runtime Version = 10.2, NumDevs = 1
Result = PASS
谢谢。
我们可以使用nvidia-smi --query-gpu=compute_cap --format=csv
来获取计算能力。
示例输出:
compute_cap
8.6
可用于 cuda 工具包 11.6。