首页> 外文学位 >A study in acceleration of selected artificial intelligence computations using thread-level parallelism.
【24h】

A study in acceleration of selected artificial intelligence computations using thread-level parallelism.

机译:使用线程级并行性加速选定的人工智能计算的研究。

获取原文
获取原文并翻译 | 示例

摘要

This study demonstrates a practical implementation of selected Artificial Intelligence computations using thread-level parallelism with C++11 on a four-core processor, with a primary goal of reducing execution times. These programs spend a large percentage of the execution time searching and learning, both of which can benefit from the speed advantages offered by thread-level parallelism. As computer hardware architectures have moved from serial execution to concurrent multithreaded execution, new software programming techniques are needed to take advantage of concurrent hardware. C++11 is a new C++ standard with many new features and this study will focus on applying the new multithreading libraries including the new atomic memory model available in C++11 to solve these problems. Serial and multithreaded programs are compared in terms of execution time and programming effort to help determine when thread-level parallel designs should be considered.
机译:这项研究演示了在四核处理器上使用线程级并行和C ++ 11进行选定人工智能计算的实际实现,其主要目的是减少执行时间。这些程序花费了大量的执行时间进行搜索和学习,这两者都可以从线程级并行性提供的速度优势中受益。随着计算机硬件体系结构从串行执行转移到并发多线程执行,需要新的软件编程技术来利用并发硬件。 C ++ 11是具有许多新功能的新C ++标准,本研究将致力于应用新的多线程库,包括C ++ 11中可用的新原子存储模型来解决这些问题。比较串行和多线程程序的执行时间和编程工作量,以帮助确定何时应考虑线程级并行设计。

著录项

  • 作者

    Niles, Kisron.;

  • 作者单位

    University of Idaho.;

  • 授予单位 University of Idaho.;
  • 学科 Computer Science.;Artificial Intelligence.
  • 学位 M.Engr.
  • 年度 2014
  • 页码 80 p.
  • 总页数 80
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号