Basic OMP Parallelized Program Not Scaling As Expected

#include <iostream>
#include <vector>
#include <stdexcept>
#include <sstream>
#include <omp.h>

std::vector<int> col_sums(std::vector<std::vector<short>>& data) {
    unsigned int height = data.size(), width = data[0].size();
    std::vector<int> totalSums(width, 0), threadSums(width, 0);

    #pragma omp parallel firstprivate(threadSums)
        #pragma omp parallel for
        for (unsigned int i = 0; i < height; i++) {
            threadSums.data()[0:width] += data[i].data()[0:width];

使用分层为英特尔® Galileo开发板创建Yocto镜像

本文介绍了如何从源代码为英特尔® Galileo 开发板(英特尔® 物联网开发人员套件的一部分)创建映像。 首先,需要获取编译映像需要使用的多个层。 您需要有足够大的磁盘空间 (~20GB),并且需要运行 最新的 64 位版 Linux* 操作系统。 我们在 Debian 7 和 openSUSE 12 上进行了尝试,希望其他系统上也能够运行。

该映像基于 poky 的 'daisy' 分支:
$ git clone --branch daisy git://git.yoctoproject.org/poky iotdk
$ cd iotdk

添加几个层 :
$ git clone git://git.yoctoproject.org/meta-intel-quark
$ git clone --branch daisy git://git.yoctoproject.org/meta-intel-iot- middleware
$ git clone git://git.yoctoproject.org/meta-intel- galileo

Promlems with Intel MPI

I have trouble with running Intel MPI on cluster with different different numbers of processors on nodes (12 and 32).

I use Intel MPI 4.0.3 and it works correctly on 20 nodes with 12 processors (Intel(Xeon(R)CPU X5650 @2.67)) at each, and all processors works correctly, then I try to run Intel MPI on other 3 nodes with 32 processors (Intel(Xeon(R)CPU E5-4620 v2@2.00) at each and they work correctly too.

Rapid Makers

For some while I keep finding around me things related to Makers, Quadcopters, and algorithms. At first I thought that it is just by chance... That IoT is nice, and Makers are having fun, and algorithms are just another way of saying parallel programming and so on... Apparently there is something very unique that connects all these seemingly unrelated areas. You know, it takes a while to realize it, but: if everyone at work speaks Martian, and your barman speaks Martian, and you go back home and your wife speaks Martian, then you probably live on Mars!

Resin.io releases experimental support for the Intel® Edison

Managing a fleet of IoT devices and deploying code is no easy task. Resin.io changes the workflow by leveraging Git and Docker technology!

How It Works

When you have new code for your end devices, all you need to do is simply perform a "git push". Resin.io builds your code into a Docker container and deploys it onto the device if/when it's online! Below is an image describing the process, found on Resin.io's website:

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