sglyon

using Base.LinAlg: BlasChar, BlasInt, blas_int, chkstride1, chksquare
using Base: blasfunc
using Base.LinAlg.LAPACK: liblapack, @lapackerror

A = [1. 2 3
      4 5 6
      7 8 9]
B = diagm(randn(3))

args = ('V', 'V', copy(A), copy(B))

sglyon

##### Install a lot of stuff first #####

$sudo apt-get update

##install python 
$ wget http://09c8d0b2229f813c1b93-c95ac804525aac4b6dba79b00b39d1d3.r79.cf1.rackcdn.com/Anaconda-2.0.1-Linux-x86_64.sh
$ sudo bash anaconda........sh
 
##install necessary libs
$ sudo apt-get install -y python-matplotlib python-tornado ipython ipython-notebook python-setuptools python-pip

sglyon

#Set up data partition
sudo mkdir /data
sudo chmod 777 /data
sudo "echo /dev/xvdb   /data	ext4	rw,user,exec,comment=cloudconfig	0	2 >> /etc/fstab"
sudo mount /data

#Install build environment
sudo sed -i "s/enabled=0/enabled=1" /etc/yum.repos.d/epel.epo
sudo yum -y update
sudo yum -y upgrade

sglyon

# file:  QuantMacro.jl

module QuantMacro

export Models #, other tools

include("models.jl")

end  # module

sglyon

#!/usr/bin/env python
import os
import urllib
from threading import Thread
from Queue import Queue
import pandas as pd


def hist_data_threaded(syms, max_threads=100, source='yahoo',
                       start='1/1/2010', end='12/31/2013'):

sglyon

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [

sglyon

Notice that when running it on a Linux VM on OSX I get about a 4x speedup relative to running it natively on OSX.

Also notice from the profile that the Linux version spends about 90% of its time in the myexp call and only about 3% in the line @inbounds d_i2_j += ((points[k, i] - data[k, j])^2). (I know these aren't actually times, but rather times when the profiler registered that line. But it gives an approximation to time spent).

The OSX version spends about 57% of its time (so about 5.3 seconds on average) in myexp and about 33% of its time (about 3 seconds on average) in the line @inbounds d_i2_j += ((points[k, i] - data[k, j])^2).

I can't explain why, on the same hardware, the OSX version is so much slower here.

sglyon

sglyon

using Roots
using ODE

@doc doc"""
ode_events{TY}(f::Function, y0::TY, tspan, odeint::Function; kwargs...)

### Arguments

* `f`: function such that dy/dt = f(t, y)
* `y0`: initial condition for y

sglyon