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numba用户手册
1.numba基础
2.@jit
3.使用自动并行化@jit
4.性能提升
5.创建ufunc
6.@jitclass
7.@cfunc
8.提前编译代码AOT
9.numba线程
10.调试
1.19。例子
1.19.1。曼德尔布罗
#! /usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function, division, absolute_importfrom timeit import default_timer as timer
from matplotlib.pylab import imshow, jet, show, ion
import numpy as npfrom numba import jit@jit
def mandel(x, y, max_iters):"""Given the real and imaginary parts of a complex number,determine if it is a candidate for membership in the Mandelbrotset given a fixed number of iterations."""i = 0c = complex(x,y)z = 0.0jfor i in range(max_iters):z = z*z + cif (z.real*z.real + z.imag*z.imag) >= 4:return ireturn 255@jit
def create_fractal(min_x, max_x, min_y, max_y, image, iters):height = image.shape[0]width = image.shape[1]pixel_size_x = (max_x - min_x) / widthpixel_size_y = (max_y - min_y) / heightfor x in range(width):real = min_x + x * pixel_size_xfor y in range(height):imag = min_y + y * pixel_size_ycolor = mandel(real, imag, iters)image[y, x] = colorreturn imageimage = np.zeros((500 * 2, 750 * 2), dtype=np.uint8)
s = timer()
create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20)
e = timer()
print(e - s)
imshow(image)
#jet()
#ion()
show()
1.19.2。移动平均
#!/usr/bin/env python
"""
A moving average function using @guvectorize.
"""import numpy as npfrom numba import guvectorize@guvectorize(['void(float64[:], intp[:], float64[:])'], '(n),()->(n)')
def move_mean(a, window_arr, out):window_width = window_arr[0]asum = 0.0count = 0for i in range(window_width):asum += a[i]count += 1out[i] = asum / countfor i in range(window_width, len(a)):asum += a[i] - a[i - window_width]out[i] = asum / countarr = np.arange(20, dtype=np.float64).reshape(2, 10)
print(arr)
print(move_mean(arr, 3))
1.19.3。多线程
下面的代码展示了使用nogil功能时潜在的性能改进。例如，在四核计算机上，我得到以下结果打印出来：
numpy (1 thread) 145 ms
numba (1 thread) 128 ms
numba (4 threads) 35 ms
注意
在Python 3中，您可以使用标准的current.futures模块，而不是手动生成线程并分派任务。
#!/usr/bin/env python
from __future__ import print_function, division, absolute_importimport math
import threading
from timeit import repeatimport numpy as np
from numba import jitnthreads = 4
size = 10**6def func_np(a, b):"""Control function using Numpy."""return np.exp(2.1 * a + 3.2 * b)@jit('void(double[:], double[:], double[:])', nopython=True, nogil=True)
def inner_func_nb(result, a, b):"""Function under test."""for i in range(len(result)):result[i] = math.exp(2.1 * a[i] + 3.2 * b[i])def timefunc(correct, s, func, *args, **kwargs):"""Benchmark *func* and print out its runtime."""print(s.ljust(20), end=" ")# Make sure the function is compiled before we start the benchmarkres = func(*args, **kwargs)if correct is not None:assert np.allclose(res, correct), (res, correct)# time itprint('{:>5.0f} ms'.format(min(repeat(lambda: func(*args, **kwargs),number=5, repeat=2)) * 1000))return resdef make_singlethread(inner_func):"""Run the given function inside a single thread."""def func(*args):length = len(args[0])result = np.empty(length, dtype=np.float64)inner_func(result, *args)return resultreturn funcdef make_multithread(inner_func, numthreads):"""Run the given function inside *numthreads* threads, splitting itsarguments into equal-sized chunks."""def func_mt(*args):length = len(args[0])result = np.empty(length, dtype=np.float64)args = (result,) + argschunklen = (length + numthreads - 1) // numthreads# Create argument tuples for each input chunkchunks = [[arg[i * chunklen:(i + 1) * chunklen] for arg in args]for i in range(numthreads)]# Spawn one thread per chunkthreads = [threading.Thread(target=inner_func, args=chunk)for chunk in chunks]for thread in threads:thread.start()for thread in threads:thread.join()return resultreturn func_mtfunc_nb = make_singlethread(inner_func_nb)
func_nb_mt = make_multithread(inner_func_nb, nthreads)a = np.random.rand(size)
b = np.random.rand(size)correct = timefunc(None, "numpy (1 thread)", func_np, a, b)
timefunc(correct, "numba (1 thread)", func_nb, a, b)
timefunc(correct, "numba (%d threads)" % nthreads, func_nb_mt, a, 这篇关于numba用户手册-11 实例的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
