joezuntz

Fixing problems when compiling programs

Most of us in science learn to write and build computer program in an ad-hoc way, copying bits of Makefile from other people, and using trial and error to change things.

That's fine when everything works, but when stuff starts to go wrong it's useful to have an idea of what different steps are happening during compilation, so you can figure out what's going wrong. In this post I'll talk at a a fairly high level about what happens when you compile code, and in a bit more detail about fixing problems you might see when you doing so.

Interpreted vs Compiled

	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# PSFEx Im3shape Example"
	]
	},
	{

	import glob
	import re
	import pyparsing as pp
	import collections


	def parse_projects(text):
	#the list which will contain the located projects
	projects = []

	{
	"metadata": {
	"kernelspec": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 2
	},
	"display_name": "IPython (Python 2)",
	"language": "python",
	"name": "python2"

	import pylab as pl
	import numpy as np
	Z=-1jnp.array([np.exp(1.j2**i) for i in xrange(100)])
	pl.plot(0.5*Z.real, Z.imag, '-', color='#FF69B4')
	pl.show()

	#Suppose you have a collection of tasks, which in this example I'll assume is just running a function f.
	#If these tasks are completely separate and independent the most then you can parallelize them easily.
	#In this gist I'll show the simplest possible way to do this using mpi4py.
	#There are better ways to do this, in particular if the tasks vary significantly in time taken to run.

	import mpi4py.MPI

	def f(i):
	"A fake task - in this case let just open a file and write a number to it"
	#open file with name based on task number