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Vc: A C++ library for explicit vectorization

机译:Vc:用于显式矢量化的C ++库

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摘要

It is an established trend that CPU development takes advantage of Moore's Law to improve in parallelism much more than in scalar execution speed. This results in higher hardware thread counts (MIMD) and improved vector units (SIMD), of which the MIMD developments have received the focus of library research and development in recent years. To make use of the latest hardware improvements, SIMD must receive a stronger focus of API research and development because the computational power can no longer be neglected and often auto-vectorizing compilers cannot generate the necessary SIMD code, as will be shown in this paper. Nowadays, the SIMD capabilities are sufficiently significant to warrant vectorization of algorithms requiring more conditional execution than was originally expected for Streaming SIMD Extension to handle. The Vc library was designed to support developers in the creation of portable vectorized code. Its capabilities and performance have been thoroughly tested. Vc provides portability of the source code, allowing full utilization of the hardware's SIMD capabilities, without introducing any overhead.
机译:CPU开发利用摩尔定律来提高并行性的数量远远超过了标量执行速度,这是一个既定的趋势。这导致更高的硬件线程数(MIMD)和改进的向量单元(SIMD),其中MIMD的开发近年来受到图书馆研究和开发的关注。为了利用最新的硬件改进,SIMD必须更加重视API的研究和开发,因为计算能力不再被忽略,并且自动矢量化编译器通常无法生成必要的SIMD代码,如本文所示。如今,SIMD功能已经足够重要,可以保证对算法进行矢量化处理,从而需要比Streaming SIMD Extension最初要处理的条件执行更多的条件执行。 Vc库旨在支持开发人员创建可移植矢量化代码。其功能和性能已经过全面测试。 Vc提供源代码的可移植性,从而允许在不引入任何开销的情况下充分利用硬件的SIMD功能。

著录项

  • 来源
    《Software》 |2012年第11期|p.1409-1430|共22页
  • 作者单位

    Frankfurt Institute for Advanced Studies, Goethe University, Ruth-Moufang-Str. 1, 60438 Frankfurt am Main, Germany;

    Frankfurt Institute for Advanced Studies and Institute for Computer Science, Goethe University Frankfurt, Germany;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    simd; C++; data-parallel; AVX; LRBni; SSE; optimization; V_c; vectorization;

    机译:simd;C ++;数据并行AVX;LRBni;上证所;优化;V_c;向量化;

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