Numba加速python代码

Numba加速python代码

Numba 读取装饰函数的 Python 字节码，并将其与有关函数输入参数类型的信息结合起来，分析和优化代码，最后使用编译器库（LLVM）针对你的 CPU 生成量身定制的机器代码。每次调用函数时，都会使用此编译版本，来达到加速的目的。

​​原始代码

import mathimport timedef is_prime(num): if num == 2: return True if num <= 1 or not num % 2: return False for div in range(3, int(math.sqrt(num) + 1), 2): if not num % div: return False return Truedef run_program(N): total = 0 for i in range(N): if is_prime(i): total += 1 return totalif __name__ == "__main__": N = 10000000 start = time.time() total = run_program(N) end = time.time() print(f"total prime num is {total}") print(f"cost {end - start}s")

运行结果：

total prime num is 664579 cost 53.910285234451294s

2 导入 Numba 的 njit，再在函数上方放个装饰器 @njit

import mathimport timefrom numba import njit# @njit 相当于 @jit(nopython=True)@njitdef is_prime(num): if num == 2: return True if num <= 1 or not num % 2: return False for div in range(3, int(math.sqrt(num) + 1), 2): if not num % div: return False return True# @njit 相当于 @jit(nopython=True)@njit(parallel = True)def run_program(N): total = 0 for i in range(N): if is_prime(i): total += 1 return totalif __name__ == "__main__": N = 10000000 start = time.time() total = run_program(N) end = time.time() print(f"total prime num is {total}") print(f"cost {end - start}s")

运行结果：

total prime num is 664579 cost 3.5231616497039795s

3 加入prange 参数来并行计算

import mathimport timefrom numba import njit, prange# @njit 相当于 @jit(nopython=True)@njitdef is_prime(num): if num == 2: return True if num <= 1 or not num % 2: return False for div in range(3, int(math.sqrt(num) + 1), 2): if not num % div: return False return True# @njit 相当于 @jit(nopython=True)@njit(parallel = True)def run_program(N): total = 0 for i in prange(N): if is_prime(i): total += 1 return totalif __name__ == "__main__": N = 10000000 start = time.time() total = run_program(N) end = time.time() print(f"total prime num is {total}") print(f"cost {end - start}s")

运行结果：

total prime num is 664579 cost 1.087864875793457s

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