簡介

前幾天搗鼓了一下Ubuntu，正是想用一下我舊電腦上的N卡，可以用GPU來跑程式碼，體驗一下多核的快樂。

還好我這破電腦也是支援Cuda的：

$ sudo lshw -C display
  *-display                 
       description: 3D controller
       product: GK208M [GeForce GT 740M]
       vendor: NVIDIA Corporation
       physical id: 0
       bus info: pci@0000:01:00.0
       version: a1
       width: 64 bits
       clock: 33MHz
       capabilities: pm msi pciexpress bus_master cap_list rom
       configuration: driver=nouveau latency=0
       resources: irq:35 memory:f0000000-f0ffffff memory:c0000000-cfffffff memory:d0000000-d1ffffff ioport:6000(size=128)

安裝相關工具

首先安裝一下Cuda的開發工具，命令如下：

$ sudo apt install nvidia-cuda-toolkit

檢視一下相關資訊：

$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Thu_Nov_18_09:45:30_PST_2021
Cuda compilation tools, release 11.5, V11.5.119
Build cuda_11.5.r11.5/compiler.30672275_0

通過Conda安裝相關的依賴包：

conda install numba & conda install cudatoolkit

通過pip安裝也可以，一樣的。

測試與驅動安裝

簡單測試了一下，發覺報錯了：

$ /home/larry/anaconda3/bin/python /home/larry/code/pkslow-samples/python/src/main/python/cuda/test1.py
Traceback (most recent call last):
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 246, in ensure_initialized
    self.cuInit(0)
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 319, in safe_cuda_api_call
    self._check_ctypes_error(fname, retcode)
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 387, in _check_ctypes_error
    raise CudaAPIError(retcode, msg)
numba.cuda.cudadrv.driver.CudaAPIError: [100] Call to cuInit results in CUDA_ERROR_NO_DEVICE

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/larry/code/pkslow-samples/python/src/main/python/cuda/test1.py", line 15, in <module>
    gpu_print[1, 2]()
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/compiler.py", line 862, in __getitem__
    return self.configure(*args)
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/compiler.py", line 857, in configure
    return _KernelConfiguration(self, griddim, blockdim, stream, sharedmem)
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/compiler.py", line 718, in __init__
    ctx = get_context()
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/devices.py", line 220, in get_context
    return _runtime.get_or_create_context(devnum)
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/devices.py", line 138, in get_or_create_context
    return self._get_or_create_context_uncached(devnum)
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/devices.py", line 153, in _get_or_create_context_uncached
    with driver.get_active_context() as ac:
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 487, in __enter__
    driver.cuCtxGetCurrent(byref(hctx))
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 284, in __getattr__
    self.ensure_initialized()
  File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 250, in ensure_initialized
    raise CudaSupportError(f"Error at driver init: {description}")
numba.cuda.cudadrv.error.CudaSupportError: Error at driver init: Call to cuInit results in CUDA_ERROR_NO_DEVICE (100)

網上搜了一下，發現是驅動問題。通過Ubuntu自帶的工具安裝顯示卡驅動：

還是失敗：

$ nvidia-smi
NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.

最後，通過命令列安裝驅動，成功解決這個問題：

$ sudo apt install nvidia-driver-470

檢查後發現正常了：

$ nvidia-smi 
Wed Dec  7 22:13:49 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.161.03   Driver Version: 470.161.03   CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0 N/A |                  N/A |
| N/A   51C    P8    N/A /  N/A |      4MiB /  2004MiB |     N/A      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

測試程式碼也可以跑了。

測試Python程式碼

列印ID

準備以下程式碼：

from numba import cuda
import os

def cpu_print():
    print('cpu print')


@cuda.jit
def gpu_print():
    dataIndex = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x
    print('gpu print ', cuda.threadIdx.x, cuda.blockIdx.x, cuda.blockDim.x, dataIndex)


if __name__ == '__main__':
    gpu_print[4, 4]()
    cuda.synchronize()
    cpu_print()

這個程式碼主要有兩個函數，一個是用CPU執行，一個是用GPU執行，執行列印操作。關鍵在於@cuda.jit這個註解，讓程式碼在GPU上執行。執行結果如下：

$ /home/larry/anaconda3/bin/python /home/larry/code/pkslow-samples/python/src/main/python/cuda/print_test.py
gpu print  0 3 4 12
gpu print  1 3 4 13
gpu print  2 3 4 14
gpu print  3 3 4 15
gpu print  0 2 4 8
gpu print  1 2 4 9
gpu print  2 2 4 10
gpu print  3 2 4 11
gpu print  0 1 4 4
gpu print  1 1 4 5
gpu print  2 1 4 6
gpu print  3 1 4 7
gpu print  0 0 4 0
gpu print  1 0 4 1
gpu print  2 0 4 2
gpu print  3 0 4 3
cpu print

可以看到GPU總共列印了16次，使用了不同的Thread來執行。這次每次列印的結果都可能不同，因為提交GPU是非同步執行的，無法確保哪個單元先執行。同時也需要呼叫同步函數cuda.synchronize()，確保GPU執行完再繼續往下跑。

檢視時間

我們通過這個函數來看GPU並行的力量：

from numba import jit, cuda
import numpy as np
# to measure exec time
from timeit import default_timer as timer


# normal function to run on cpu
def func(a):
    for i in range(10000000):
        a[i] += 1


# function optimized to run on gpu
@jit(target_backend='cuda')
def func2(a):
    for i in range(10000000):
        a[i] += 1


if __name__ == "__main__":
    n = 10000000
    a = np.ones(n, dtype=np.float64)

    start = timer()
    func(a)
    print("without GPU:", timer() - start)

    start = timer()
    func2(a)
    print("with GPU:", timer() - start)

結果如下：

$ /home/larry/anaconda3/bin/python /home/larry/code/pkslow-samples/python/src/main/python/cuda/time_test.py
without GPU: 3.7136273959999926
with GPU: 0.4040513340000871

可以看到使用CPU需要3.7秒，而GPU則只要0.4秒，還是能快不少的。當然這裡不是說GPU一定比CPU快，具體要看任務的型別。

程式碼

程式碼請看GitHub: https://github.com/LarryDpk/pkslow-samples

用GPU來執行Python程式碼

簡介

安裝相關工具

測試與驅動安裝

測試Python程式碼

列印ID

檢視時間

程式碼