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import networkx
import sys
G = networkx.Graph()
argvs = sys.argv

if( len(argvs) != 4 ):
  print 'Usage: python %s <d> <n> <seed>' % argvs[0]
  quit()
d = int( argvs[1] )
n = int( argvs[2] )

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require './config/environment'

psq_id = ARGV[0]
psq = ParameterSetQuery.find(psq_id)
$stderr.puts "Number of found PS: #{psq.parameter_sets.count}"

def replace_runs(run)
  run_attr = { submitted_to: run.submitted_to,
               mpi_procs: run.mpi_procs,
               omp_threads: run.omp_threads,

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sudo yum update
echo "[MongoDB]
name=MongoDB Repository
baseurl=http://downloads-distro.mongodb.org/repo/redhat/os/x86_64
gpgcheck=0
enabled=1" | sudo tee -a /etc/yum.repos.d/mongodb.repo
sudo yum install -y mongodb-org-server mongodb-org-shell mongodb-org-tools 
sudo service mongod start

sudo chkconfig mongod on

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var fileref=document.createElement('script')
fileref.setAttribute("type","text/javascript")
fileref.setAttribute("src", "http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML")
document.getElementsByTagName("head")[0].appendChild(fileref)

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#include <stdio.h>
#include <stdlib.h>
#include <pthread.h>
#include <omp.h>

#define NTHREADS 4

void *myFun(void *x)
{
  int tid;

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#include <iostream>
#define _USE_MATH_DEFINES
#include <cmath>
#include <boost/math/special_functions/beta.hpp>

double Q_k0( double f0, double k0, double k ) {
  double c = 1.0 / ( f0 * (k0+1.0) );

  double x = f0 / (1.0-f0);
  double a = k + 1.0;

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import x10.io.Console;
import x10.util.Timer;

class ParallelHelloWorld {
  public static def main(args:Rail[String]):void {
    val timer = new Timer();
    val begin = timer.milliTime();
    finish for (p in Place.places()) {
      async at (p) {
        Console.OUT.println("Hello, I'm " + here);

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from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 784])

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])

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