CUDA 启动内核失败：没有可执行的内核映像

Question

我正在尝试运行在相当旧的 GPU 上使用 CUDA。我尝试了 CUDA Samples vectorAdd，它给了我以下错误：

Failed to launch vectorAdd kernel (error code no kernel image is available for execution on the device)!

这些是

的输出

设备查询：

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

现根据CUDA兼容性PDF https://docs.nvidia.com/pdf/CUDA_Compatibility.pdf 我假设我具有从 CUDA 9.0.176 到 GPU 驱动程序的二进制兼容性。对于计算能力支持，table 没有列出 390 驱动程序。甚至可以在这个 GPU 上对 CUDA 进行编程，还是我应该买一个更新的？如果可能，我需要什么驱动程序和 CUDA 工具包版本的组合？

Answer 1

您使用的 GPU 是 Fermi class（计算能力 2.0）设备。 CUDA 9.0 于 2017 年 9 月发布时，CUDA 工具包提供了 officially removed 支持。具有 Fermi 支持的 CUDA 工具包的最后一个版本是 CUDA 8.0。如果您想将该 GPU 与 CUDA 一起使用，则必须使用它（或更旧的东西）。

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CUDA 启动内核失败：没有可执行的内核映像

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

cuda

gpu

driver

nvidia