[python] [GPU]GPU 사용 Python 코드 실행

Running Python script on GPU.

GPU’s have more cores than CPU and hence when it comes to parallel computing of data, GPUs performs exceptionally better than CPU even though GPU has lower clock speed and it lacks several core managements features as compared to the CPU.
Thus, running a python script on GPU can prove out to be comparatively faster than CPU, however it must be noted that for processing a data set with GPU, the data will first be transferred to the GPU’s memory which may require additional time so if data set is small then cpu may perform better than gpu.
Getting started:
Only NVIDIA GPU’s are supported for now and the ones which are listed in this page. If your graphics card has CUDA cores, them u can proceed further with setting up things.
Installation:
First make sure that Nvidia drivers are upto date also you can install cudatoolkit explicitly from here.then install Anaconda add anaconda to environment while installing.
After completion of all the installations run the following commands in the command prompt.

conda install numba & conda install cudatoolkit

NOTE: If Anaconda is not added to the environment then navigate to anaconda installation and locate the Scripts directory and open command prompt there.

CODE :

We will use the numba.jit decorator for the function we want to compute over the GPU. The decorator has several parameters but we will work with only the target parameter. Target tells the jit to compile codes for which source(“CPU” or “Cuda”). “Cuda” corresponds to GPU. However, if CPU is passed as an argument then the jit tries to optimize the code run faster on CPU and improves the speed too.

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 ="cuda")						 
def func2(a): 
	for i in range(10000000): 
		a[i]+= 1
if __name__=="__main__": 
	n = 10000000							
	a = np.ones(n, dtype = np.float64) 
	b = np.ones(n, dtype = np.float32) 
	
	start = timer() 
	func(a) 
	print("without GPU:", timer()-start)	 
	
	start = timer() 
	func2(a) 
	print("with GPU:", timer()-start) 

Output: based on CPU = i3 6006u, GPU = 920M

without GPU: 8.985259440999926

with GPU: 1.4247172560001218

However, it must be noted that the array is first copied from ram to the GPU for processing and if the function returns anything then the returned values will be copied from GPU to CPU back. Therefore for small data sets the speed of CPU is comparatively faster but the speed can be further improved even for small data sets by passing target as “CPU”. Special care should be taken when the function which is written under the jit attempts to call any other function then that function should also be optimized with jit else the jit may produce even more slower codes.

램에서 GPU로 처리할 데이터가 복사되고, 처리 후 결과가 GPU에서 CPU로 돌아가는 점을 주의해야 합니다.

이러한 메커니즘으로 작은크기의 데이터 처리는 CPU에서 처리하는게 상대적으로 빠를 수 있습니다.

특별한 경우로 jit로 쓰여진 함수가 다른 함수를 호출할때 호출되는 함수가 jit 적용이 안된 경우는 처리속도가 느려지게 됩니다.

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runfile('D:/TheMyWork/2020/2020-07/2020-07-26/untitled0.py', wdir='D:/TheMyWork/2020/2020-07/2020-07-26')

without GPU: 5.46546520000004

with GPU: 1.0288339999999607

runfile('D:/TheMyWork/2020/2020-07/2020-07-26/untitled0.py', wdir='D:/TheMyWork/2020/2020-07/2020-07-26')

without GPU: 6.352249200000017

with GPU: 0.06906279999998333

runfile('D:/TheMyWork/2020/2020-07/2020-07-26/untitled0.py', wdir='D:/TheMyWork/2020/2020-07/2020-07-26')

without GPU: 6.171576800000025

with GPU: 0.0799111000000039

runfile('D:/TheMyWork/2021/2021-04/2021-04-21/pythonGPU.py', wdir='D:/TheMyWork/2021/2021-04/2021-04-21')

without GPU: 6.24125069999991

with GPU: 0.08168079999995825