英特尔® Cilk™ Plus

How can I parallelize implicit loop ?

I have the loop, inside its body running the function with array member (dependent on loop index) as an argument, and returning one value.
I can parallelized this loop by using cilk_for() operator instead of regular for() - and it is simple and works well.  This is explicit parallelization.  
Instead of explicit loop instruction I can use Array Notation contruction (as shown below) - it is implicit loop.
My routine is relatively long and complecs, and has Array Notation constructions inside, so it cannot be declared as a vector (elemental) one.

Patches or configure options to build the trunk on arm

Hello, 

I want to build the trunk on an embedded system supporting armv7 instructions. The build was accomplished without errors but cilk/cilk.h and libcilkrts weren't built. I checked out the patches available on the internet they do support non x86 architectures but I think just i386 not arm.

Are there other patches or config options to add while building so that I get those libraries along with the build 

Regards   

Trusted Tools in the New Android* World: Optimization Techniques - from Intel® SSE Intrinsics to Intel® Cilk™ Plus

Author: Zvi Danovich, Senior SW Application Engineer, Intel

Introduction

Most Android applications, even those based only on scripting and managed languages (Java*, HTML5,…), eventually use middleware features that would benefit from optimization.

This paper will discuss optimization needs and approaches on Android and walk through a case study of how to optimize a multimedia and augmented reality application.

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  • Parallel Computation of Sparse Rulers

    This article explains the sparse ruler problem, two parallel codes for computing sparse rulers, and some new results that reveal a surprising "gap" behavior for solutions to the sparse ruler problem. The code and results are included in the attached zip file.

    Background

    A complete sparse ruler is a ruler with M marks than can measure any integer distance between 0 and L units. For example, the following ruler has 6 marks (including the ends) and can measure integer distance from 0 to 13:

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  • Program Optimization through Loop Vectorization

    Download Article

    Download Program Optimization through Loop Vectorization [PDF 617KB]

    Overview

    In this white paper, we will use a very simplified finite difference stencil computation of the following form:

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  • Graph Algorithms: Shortest Path

    Dijkstra algorithm is a graph search algorithm that solves the single-source shortest path problem for a graph with non-negative edge path costs, producing a shortest path tree. The algorithm requires repeated searching for the vertex having the smallest distance and accumulating shortest distance from the source vertex. This example calculates the shortest path between each pair of vertexes in a complete graph having 2000 vertexes using Dijkstra algorithm.

    Sorting Algorithms: Merge Sort

    Merge sort algorithm is a comparison-based sorting algorithm. In this sample, we use top-down implementation, which recursively splits list into two halves (called sublists) until size of list is 1. Then merge these two sublists and produce a sorted list. This sample could run in serial, or in parallel with Intel® Cilk™ Plus keywords cilk_spawn and cilk_sync. For more details about merge sort algorithm and top-down implementation, please refer to http://en.wikipedia.org/wiki/Merge_sort.

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