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[ 源代码: simde  ]

软件包:libsimde-dev(0.8.2~rc1-1)

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Implementations of SIMD instructions for all systems

SIMDe provides fast, portable implementations of SIMD intrinsics on hardware which doesn't natively support them, such as calling SSE functions on ARM. There is no performance penalty if the hardware supports the native implementation (e.g., SSE/AVX runs at full speed on x86, NEON on ARM, etc.).

This makes porting code to other architectures much easier in a few key ways:

First, instead of forcing you to rewrite everything for each architecture, SIMDe lets you get a port up and running almost effortlessly. You can then start working on switching the most performance-critical sections to native intrinsics, improving performance gradually. SIMDe lets (for example) SSE/AVX and NEON code exist side-by-side, in the same implementation.

Second, SIMDe makes it easier to write code targeting ISA extensions you don't have convenient access to. You can run NEON code on your x86 machine without an emulator. Obviously you'll eventually want to test on the actual hardware you're targeting, but for most development, SIMDe can provide a much easier path.

SIMDe takes a very different approach from most other SIMD abstraction layers in that it aims to expose the entire functionality of the underlying instruction set. Instead of limiting functionality to the lowest common denominator, SIMDe tries to minimize the amount of effort required to port while still allowing you the space to optimize as needed.

The current focus is on writing complete portable implementations, though a large number of functions already have accelerated implementations using one (or more) of the following:

    SIMD intrinsics from other ISA extensions (e.g., using NEON to implement
SSE).
    Compiler-specific vector extensions and built-ins such as
__builtin_shufflevector and __builtin_convertvector
    Compiler auto-vectorization hints, using:
       OpenMP 4 SIMD
       Cilk Plus
       GCC loop-specific pragmas
       clang pragma loop hint directives

标签: 软件开发: 函数库, 角色: 开发用函数库

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硬件架构 软件包大小 安装后大小 文件
amd64 454.0 kB8,351.0 kB [文件列表]