パフォーマンスの探求パート II : Perl と Python

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()

  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)

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

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