numpy 的基本操作是否矢量化，即它们是否使用 SIMD 操作? [英] Are numpy's basic operations vectorized, i.e. do they use SIMD operations?

问题描述

我正在做一些性能分析，我想知道 numpy 是否在数据类型已知(双精度)时对其标准数组操作进行矢量化.

I am doing some performance analysis, and i wonder, whether numpy vectorizes its standard array operations, when the datatype is known (double).

a, b = (some numpy arrays)
c = a + b #Is this vectorized?

此操作是否向量化，即计算是否包含 SIMD 操作?

Is this operation vectorized, i.e. will the computation consist of SIMD operations?

推荐答案

是的，他们是.

/*
 * This file is for the definitions of simd vectorized operations.
 *
 * Currently contains sse2 functions that are built on amd64, x32 or
 * non-generic builds (CFLAGS=-march=...)
 * In future it may contain other instruction sets like AVX or NEON     detected
 * at runtime in which case it needs to be included indirectly via a file
 * compiled with special options (or use gcc target attributes) so the binary
 * stays portable.
 */

链接:Numpy simd.inc.src 在 github 上.

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