简介

前几天捣鼓了一下Ubuntu,正是想用一下我旧电脑上的N卡,可以用GPU来跑代码,体验一下多核的快乐。

还好我这破电脑也是支持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)

安装相关工具

首先安装一下Cuda的开发工具,命令如下:

$ sudo apt install nvidia-cuda-toolkit

查看一下相关信息:

$ nvcc --versionnvcc: NVIDIA (R) Cuda compiler driverCopyright (c) 2005-2021 NVIDIA CorporationBuilt on Thu_Nov_18_09:45:30_PST_2021Cuda compilation tools, release 11.5, V11.5.119Build cuda_11.5.r11.5/compiler.30672275_0

通过Conda安装相关的依赖包:

conda install numba & conda install cudatoolkit

通过pip安装也可以,一样的。

测试与驱动安装

简单测试了一下,发觉报错了:

$ /home/larry/anaconda3/bin/python /home/larry/code/pkslow-samples/python/src/main/python/cuda/test1.pyTraceback (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_DEVICEDuring 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     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)

网上搜了一下,发现是驱动问题。通过Ubuntu自带的工具安装显卡驱动:

还是失败:

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

最后,通过命令行安装驱动,成功解决这个问题:

$ sudo apt install nvidia-driver-470

检查后发现正常了:

$ 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                                                 |+-----------------------------------------------------------------------------+

测试代码也可以跑了。

测试Python代码

打印ID

准备以下代码:

from numba import cudaimport osdef cpu_print():    print('cpu print')@cuda.jitdef 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()

这个代码主要有两个函数,一个是用CPU执行,一个是用GPU执行,执行打印操作。关键在于@cuda.jit这个注解,让代码在GPU上执行。运行结果如下:

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

可以看到GPU总共打印了16次,使用了不同的Thread来执行。这次每次打印的结果都可能不同,因为提交GPU是异步执行的,无法确保哪个单元先执行。同时也需要调用同步函数cuda.synchronize(),确保GPU执行完再继续往下跑。

查看时间

我们通过这个函数来看GPU并行的力量:

from numba import jit, cudaimport numpy as np# to measure exec timefrom timeit import default_timer as timer# normal function to run on cpudef 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] += 1if __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)

结果如下:

$ /home/larry/anaconda3/bin/python /home/larry/code/pkslow-samples/python/src/main/python/cuda/time_test.pywithout GPU: 3.7136273959999926with GPU: 0.4040513340000871

可以看到使用CPU需要3.7秒,而GPU则只要0.4秒,还是能快不少的。当然这里不是说GPU一定比CPU快,具体要看任务的类型。

代码

代码请看GitHub: https://github.com/LarryDpk/pkslow-samples