Nvidia CUDA 错误：没有可在设备上执行的内核映像

Posted 2023-04-15

【中文标题】Nvidia CUDA 错误：没有可在设备上执行的内核映像

【英文标题】：Nvidia CUDA Error: no kernel image is available for execution on the device

【发布时间】：2021-08-21 20:15:36

【问题描述】：

我有一台 NVidia GeForce GTX 770，并希望将其 CUDA 功能用于我正在进行的项目。我的机器运行的是 windows 10 64bit。

我已按照提供的 CUDA Toolkit 安装指南进行操作：https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/。

安装驱动程序后，我打开了示例解决方案（使用 Visual Studio 2019）并构建了 deviceQuery 和 bandwidthTest 示例。这是输出：

设备查询：

C:\ProgramData\NVIDIA Corporation\CUDA Samples\v11.3\bin\win64\Debug\deviceQuery.exe Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "NVIDIA GeForce GTX 770"
  CUDA Driver Version / Runtime Version          11.3 / 11.3
  CUDA Capability Major/Minor version number:    3.0
  Total amount of global memory:                 2048 MBytes (2147483648 bytes)
  (008) Multiprocessors, (192) CUDA Cores/MP:    1536 CUDA Cores
  GPU Max Clock rate:                            1137 MHz (1.14 GHz)
  Memory Clock rate:                             3505 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 524288 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  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 shared memory per multiprocessor:        49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  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 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
  CUDA Device Driver Mode (TCC or WDDM):         WDDM (Windows Display Driver Model)
  Device supports Unified Addressing (UVA):      Yes
  Device supports Managed Memory:                Yes
  Device supports Compute Preemption:            No
  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 = 11.3, CUDA Runtime Version = 11.3, NumDevs = 1
Result = PASS

带宽：

[CUDA Bandwidth Test] - Starting...
Running on...

 Device 0: NVIDIA GeForce GTX 770
 Quick Mode

 Host to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)    Bandwidth(GB/s)
   32000000         3.1

 Device to Host Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)    Bandwidth(GB/s)
   32000000         3.4

 Device to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)    Bandwidth(GB/s)
   32000000         161.7

Result = PASS

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

但是，当我尝试运行任何其他示例时，例如随 CUDA 11.3 运行时模板提供的起始代码：

#include "cuda_runtime.h"
#include "device_launch_parameters.h"

#include <stdio.h>

cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size);

__global__ void addKernel(int* c, const int* a, const int* b) 
    int i = threadIdx.x;
    c[i] = a[i] + b[i];


int main() 
    const int arraySize = 5;
    const int a[arraySize] =  1, 2, 3, 4, 5 ;
    const int b[arraySize] =  10, 20, 30, 40, 50 ;
    int c[arraySize] =  0 ;

    // Add vectors in parallel.
    cudaError_t cudaStatus = addWithCuda(c, a, b, arraySize);
    if (cudaStatus != cudaSuccess) 
        fprintf(stderr, "addWithCuda failed!");
        return 1;
    

    printf("1,2,3,4,5 + 10,20,30,40,50 = %d,%d,%d,%d,%d\n", c[0], c[1], c[2], c[3], c[4]);

    // cudaDeviceReset must be called before exiting in order for profiling and
    // tracing tools such as Nsight and Visual Profiler to show complete traces.
    cudaStatus = cudaDeviceReset();
    if (cudaStatus != cudaSuccess) 
        fprintf(stderr, "cudaDeviceReset failed!");
        return 1;
    

    return 0;


// Helper function for using CUDA to add vectors in parallel.
cudaError_t addWithCuda(int* c, const int* a, const int* b, unsigned int size) 
    int* dev_a = 0;
    int* dev_b = 0;
    int* dev_c = 0;
    cudaError_t cudaStatus;

    // Choose which GPU to run on, change this on a multi-GPU system.
    cudaStatus = cudaSetDevice(0);
    if (cudaStatus != cudaSuccess) 
        fprintf(stderr, "cudaSetDevice failed!  Do you have a CUDA-capable GPU installed?");
        goto Error;
    

    // Allocate GPU buffers for three vectors (two input, one output)    .
    cudaStatus = cudaMalloc((void**)&dev_c, size * sizeof(int));
    if (cudaStatus != cudaSuccess) 
        fprintf(stderr, "cudaMalloc failed!");
        goto Error;
    

    cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(int));
    if (cudaStatus != cudaSuccess) 
        fprintf(stderr, "cudaMalloc failed!");
        goto Error;
    

    cudaStatus = cudaMalloc((void**)&dev_b, size * sizeof(int));
    if (cudaStatus != cudaSuccess) 
        fprintf(stderr, "cudaMalloc failed!");
        goto Error;
    

    // Copy input vectors from host memory to GPU buffers.
    cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice);
    if (cudaStatus != cudaSuccess) 
        fprintf(stderr, "cudaMemcpy failed!");
        goto Error;
    

    cudaStatus = cudaMemcpy(dev_b, b, size * sizeof(int), cudaMemcpyHostToDevice);
    if (cudaStatus != cudaSuccess) 
        fprintf(stderr, "cudaMemcpy failed!");
        goto Error;
    

    // Launch a kernel on the GPU with one thread for each element.
    addKernel << <1, size >> > (dev_c, dev_a, dev_b);

    // Check for any errors launching the kernel
    cudaStatus = cudaGetLastError();
    if (cudaStatus != cudaSuccess) 
        fprintf(stderr, "addKernel launch failed: %s\n", cudaGetErrorString(cudaStatus));
        goto Error;
    

    // cudaDeviceSynchronize waits for the kernel to finish, and returns
    // any errors encountered during the launch.
    cudaStatus = cudaDeviceSynchronize();
    if (cudaStatus != cudaSuccess) 
        fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!\n", cudaStatus);
        goto Error;
    

    // Copy output vector from GPU buffer to host memory.
    cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost);
    if (cudaStatus != cudaSuccess) 
        fprintf(stderr, "cudaMemcpy failed!");
        goto Error;
    

Error:
    cudaFree(dev_c);
    cudaFree(dev_a);
    cudaFree(dev_b);

    return cudaStatus;

我收到以下错误：

addKernel launch failed: no kernel image is available for execution on the device
addWithCuda failed!

从这张表：https://docs.nvidia.com/deploy/cuda-compatibility/index.html#support-hardware__table-hardware-support你可以看到我GPU的计算能力版本（3.0）其实和安装的驱动（465.19.01+）是兼容的，那为什么我不能运行除了查询以外的任何代码和带宽测试？

【问题讨论】：

@RobertCrovella 我刚刚安装了 CUDA Toolkit 10.2，现在一切正常。非常感谢。

@RobertCrovella 如果您将评论作为答案发表，我很乐意将其标记为已接受。

【参考方案1】：

您的 GTX770 GPU 是“Kepler”架构计算能力 3.0 设备。这些设备在 CUDA 10 发布周期中被弃用，并且从 CUDA 11.0 开始不再支持它们

CUDA 10.2 版本是带有support for compute 3.0 devices 的最后一个工具包。您将无法使 CUDA 11.0 或更高版本与您的 GPU 一起使用。查询和带宽测试使用的 API 不会尝试在您的 GPU 上运行代码，这就是为什么它们可以在任何其他示例都不起作用的地方工作。