運行了一個玩具性能範例後，我們現在將稍微偏離主題並將性能與
進行對比
一些 Python 實作。首先讓我們設定計算階段，並提供命令列
Python 腳本的功能。
import argparse
import time
import math
import numpy as np
import os
from numba import njit
from joblib import Parallel, delayed
parser = argparse.ArgumentParser()
parser.add_argument("--workers", type=int, default=8)
parser.add_argument("--arraysize", type=int, default=100_000_000)
args = parser.parse_args()
# Set the number of threads to 1 for different libraries
print("=" * 80)
print(
f"\nStarting the benchmark for {args.arraysize} elements "
f"using {args.workers} threads/workers\n"
)
# Generate the data structures for the benchmark
array0 = [np.random.rand() for _ in range(args.arraysize)]
array1 = array0.copy()
array2 = array0.copy()
array_in_np = np.array(array1)
array_in_np_copy = array_in_np.copy()
這是我們的參賽者：
for i in range(len(array0)):
array0[i] = math.cos(math.sin(math.sqrt(array0[i])))
np.sqrt(array_in_np, out=array_in_np) np.sin(array_in_np, out=array_in_np) np.cos(array_in_np, out=array_in_np)
def compute_inplace_with_joblib(chunk):
return np.cos(np.sin(np.sqrt(chunk))) #parallel function for joblib
chunks = np.array_split(array1, args.workers) # Split the array into chunks
numresults = Parallel(n_jobs=args.workers)(
delayed(compute_inplace_with_joblib)(chunk) for chunk in chunks
)# Process each chunk in a separate thread
array1 = np.concatenate(numresults) # Concatenate the results
@njit
def compute_inplace_with_numba(array):
np.sqrt(array,array)
np.sin(array,array)
np.cos(array,array)
## njit will compile this function to machine code
compute_inplace_with_numba(array_in_np_copy)
以下是計時結果：
In place in ( base Python): 11.42 seconds In place in (Python Joblib): 4.59 seconds In place in ( Python Numba): 2.62 seconds In place in ( Python Numpy): 0.92 seconds
numba 出奇的慢！ ？難道是由於 mohawk2 在 IRC 交流中關於此問題指出的編譯開銷造成的嗎？
為了測試這一點，我們應該在執行基準測試之前呼叫一次compute_inplace_with_numba。這樣做表明 Numba 現在比 Numpy 更快。
In place in ( base Python): 11.89 seconds In place in (Python Joblib): 4.42 seconds In place in ( Python Numpy): 0.93 seconds In place in ( Python Numba): 0.49 seconds
n<-50000000
x<-runif(n)
start_time <- Sys.time()
result <- cos(sin(sqrt(x)))
end_time <- Sys.time()
# Calculate the time taken
time_taken <- end_time - start_time
# Print the time taken
print(sprintf("Time in base R: %.2f seconds", time_taken))
Time in base R: 1.30 seconds
以上是效能追求第二部分：Perl 與 Python的詳細內容。更多資訊請關注PHP中文網其他相關文章！
