PyHyp experiments.
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Makefile
README.md
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README.md

PyHyP Experiments

This repository contains benchmarks for our composed Python/PHP VM as well as other VMs.

Building

If you are using the VirtualBox image, you can skip this stage, as the VMs come pre-built.

To build yourself, first ensure you have the following installed on a Linux/amd64 system:

  • bunzip2
  • git
  • hg
  • python
  • svn
  • unzip
  • wget
  • GCC (and gcc-multilib).
  • virtualenv
  • Dependencies of HHVM, PyPy, and HippyVM.

Then run:

$ sh build.sh

Running the Benchmarks

First inspect the generated config.py. Here you can adjust (for example) the number of iterations at each Kalibera level and the benchmark parameters.

  • In each VM dict the parameter n_iterations sets the number of in-process iterations on a per-VM basis..

  • The top-level N_EXECUTIONS parameter sets how many process executions to repeat with.

The defaults you see in the config file are the parameters we used.

To run the benchmarks:

$ make bench

Results are written to config_results.json.

The source code for the benchmarks is in the 'benchmarks/' directory.

On our circa 2014 benchmark machines, running the benchmarks took several days.

Generating Tables

The tables from the paper can generated by running make latex-tables.

To make tables you need a config_results.json file to be present in this directory. To generate this file, run the experiment as shown above. Alternatively, if you are using the VirtualBox image, then the data from our run can be found in ~/raw_results. You can copy the config_results.json file here and generate tables from this.

Note that the bootstrapping method we use to compute confidence intervals [1] is non-deterministic, so even if you use our data, expect small differences with the tables you see in the paper.

Generating the tables requires a working TeX Live setup.

Case studies

The two case studies from the paper are also included.

If you are using the VirtualBox image, then the case studies come ready to run. Please see the post-login message for more information.

To set up the case studies manually, run:

$ cd casestudies & make

Prepare PyHyp

PyHyP needs to have the environment variable PYPY_PREFIX set to the pypy-hippy-bridge folder. If you don't want to set this variable globally, you can just patch the hippyvm/hippy-c-cgi executable by adding the appropriate export:

#!/bin/sh
export PYPY_PREFIX=PATH_TO_EXPERIMENTS/pypy-hippy-bridge/
HIPPY=`dirname $0`/hippy-c
exec $HIPPY --cgi "$@"

SquirrelMail

To run the case studies you need to setup a web-server, e.g. Apache, Lighttpd, nginx. You'll then have to replace the PHP interpreter with PyHyp. Here's an example configuration for lighttpd:

# /etc/lighttpd/conf.d/cgi.conf
cgi.assign = ( ".pl"  => "/usr/bin/perl",
               ".cgi" => "/usr/bin/perl",
               ".rb"  => "/usr/bin/ruby",
               ".erb" => "/usr/bin/eruby",
               ".php" => "PATH_TO_EXPERIMENTS/hippyvm/hippy-c-cgi",
               ".py"  => "/usr/bin/python2.7" )

Setup

Run configure to configure Squirrelmail. At the very least you will need to change the following things:

  • Enter your IMAP Server details in 2. Server-Settings -> Update IMAP Settings.
  • Change the data folder under 4. General Options -> Data Directory to ../data. Then change the ownership of squirrelmail-4.22.1/data/ to be accessible by your webserver user, e.g. chown -R http:http data/.

Finally, point the web server to the SquirrelMail folder (casestudies/squirrelmail-webmail-1.4.22/). Here's a minimal configuration file for lighttpd:

# This is a minimal example config
# See /usr/share/doc/lighttpd
# and http://redmine.lighttpd.net/projects/lighttpd/wiki/Docs:ConfigurationOptions

server.modules = (
	"mod_access",
)

include "conf.d/cgi.conf"

server.port		= 80
server.username		= "http"
server.groupname	= "http"
server.document-root	= "PATH_TO_EXPERIMENTS/pyhyp_experiments/casestudies/"
server.errorlog		= "/var/log/lighttpd/error.log"
dir-listing.activate	= "enable"
index-file.names	= ( "index.html", "index.php" )
mimetype.assign		= (
				".html" => "text/html",
				".txt" => "text/plain",
				".css" => "text/css",
				".js" => "application/x-javascript",
				".jpg" => "image/jpeg",
				".jpeg" => "image/jpeg",
				".gif" => "image/gif",
				".png" => "image/png",
				"" => "application/octet-stream"
			)

Activating the plugin

Run Squirrelmails configure once more:

cd squirrelmail-webmail-1.4.22/
./configure

Select: 8. Plugins, then select nltk and sympy.

Activate HTML emails

To be able to see rendered formulae within Squirrelmail we need to activate HTML viewing of emails which is deactived by default. To do that, go to the local Squirrelmail website, then navigate to Options->Display Preferences and check the box Show HTML Version by default and Submit.

Troubleshooting

Sympy is missing

To be able to use Sympy we need to install it into PyHyp. The easiest way to do this is to download the current Sympy version from https://github.com/sympy/sympy/archive/sympy-0.7.6.1.tar.gz. Extract the sympy folder from the archive into pypy-hippy-bridge/site-packages/.

CFFI

This example can be run just using the PyHyp executable. Simply run:

PYPY_PREFIX=../work/pyhyp/pypy-hippy-bridge ../work/pyhyp/hippyvm/pyhyp cffi.php

References

[1] Quantifying Performance Changes with Effect Size Confidence Intervals, Tomas Kalibera and Richard Jones, 2012.