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Efficient Parallelization

Compiler Methodology for Intel® MIC Architecture

Efficient Parallelization

Overview

This article is part of the Intel® Modern Code Developer Community documentation which supports developers in leveraging application performance in code through a systematic step-by-step optimization framework methodology. This article addresses: Thread level parallelization.

 

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  • 高效并行化

    Efficient Parallelization Document

    高效并行化文档

    面向英特尔® 集成众核架构的编译器方法

    高效并行化

    概述

    本章介绍并行化。其中有各种并行化方法与资源的链接以及如何获取最佳并行化性能的技巧。

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  • Otimização
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  • Intel® Parallel Debugger Extension

    This whitepaper provides tips and tricks on how to best take advantage of the additional insight into parallel data constructs the Intel® Parallel Debugger Extension brings to the Microsoft Visual Studio* debug environment. In doing so it provides a high-level overview of the Intel® Parallel Debugger Extension for Microsoft Visual Studio* and the key features that enhance the debug experience.
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  • SIGCSE2012 Workshop 23 Parallelism and Concurrency for Data-Structures & Algorithms courses

    This workshop is inspired by Dan Grossman’s SIGCSE 2011 workshop on Data Abstractions. We review C/C++ conversions of the original Java-based materials and will include material from the Parallel Algorithms course at Kent State. The workshop will appeal to data-structure and algorithms course instructors. Workshop topics will include divide and conquer approaches, work sharing concepts, and a scoped locking scheme in OpenMP for C++ classes.

    Superscalar programming 101 (Matrix Multiply) Part 5 of 5

    In part 4 we saw the effects of the QuickThread Parallel Tag Team Transpose method of Matrix Multiplication performed on a Dual Xeon 5570 systems with 2 sockets and two L3 caches, each shared by four cores (8 threads). and each processor with four L2 and four L1 caches each shared by one core and 2 threads, we find:

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  • Superscalar programming 101 (Matrix Multiply) Part 3 of 5

    By Jim Dempsey

    In the previous article (part 2) we have seen that by reorganizing the loops and with use of temporary array we can observe a performance gain with SSE small vector optimizations (compiler does this) but a larger gain came from better cache utilization due to the layout change and array access order. The improvements pushed us into a memory bandwidth limitation whereby the Serial method now outperforms the Parallel method (of the Serial method).

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