Note 2: This tutorial is outdated. Please see this updated version.
These instructions should get you up and running with a minimal environment for compiling 64-bit C++ projects with OpenMP (e.g., BioFVM and PhysiCell) using gcc. These instructions were tested with OSX 10.11 (El Capitan), but they should work on any reasonably recent version of OSX.
In the end result, you'll have a compiler and key makefile capabilities. The entire toolchain is free and open source.
Of course, you can use other compilers and more sophisticated integrated desktop environments, but these instructions will get you a good baseline system with support for 64-bit binaries and OpenMP parallelization.
Note 4: Yesterday in this post, we showed how to set up gcc using the popular MacPorts package manager. Because MacPorts builds gcc (and all its dependencies!) from source, it takes a very, very long time. On my 2012 Macbook Air, this step took 16 hours. This tutorial uses Homebrew to dramatically speed up the process!
What you'll need:
- XCode: This includes command line development tools. Evidently, it is required for both Homebrew and its competitors (e.g., MacPorts). Download the latest version in the App Store. (Search for xcode.) As of January 15, 2016, the App Store will install Version 7.2. Please note that this is a 4.41 GB download!
- Homebrew: This is a package manager for OSX, which will let you easily download and install many linux utilities without building them from source. You'll particularly need it for getting gcc. Installation is a simple command-line script, as detailed below. As of January 17, 2016, this will download Version 0.9.5.
- gcc5 (from Homebrew): This will be an up-to-date 64-bit version of gcc, with support for OpenMP. As of January 17, 2016, this will download Version 5.2.0.
Main steps:
1) Download, install, and prepare XCode
As mentioned above, open the App Store, search for Xcode, and start the download / install. Go ahead and grab a coffee while it's downloading and installing 4+ GB. Once it has installed, open Xcode, agree to the license, and let it install whatever components it needs.
Now, you need to get the command line tools. Go to the Xcode menu, select "Open Developer Tool", and choose "More Developer Tools ...". This will open up a site in Safari and prompt you to log in.
Sign on with your AppleID, agree to yet more licensing terms, and then search for "command line tools" for your version of Xcode and OSX. (In my case, this is OSX 10.11 with Xcode 7.2) Click the + next to the correct version, and then the link for the dmg file. (Command_Line_Tools_OS_X_10.11_for_Xcode_7.2.dmg).
Double-click the dmg file. Double-click pkg file it contains. Click "continue", "OK", and "agree" as much as it takes to install. Once done, go ahead and exit the installer and close the dmg file.
2) Install Homebrew
Open a terminal window (Open Launchpad, then "Other", then "Terminal"), and run:
> ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
> ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Let the script run, and answer "y" whenever asked. This will not take very long.
3) Get, install, and prepare gcc
Open a terminal window (see above), and search for gcc, version 5.x or above
> brew search gcc5
You should see a list of packages, including gcc5. Take a note of what is found. (In my case, it found homebrew/versions/gcc5
Then, download and install gcc5:
> brew install homebrew/versions/gcc5
This will download whatever dependencies are needed, generally already pre-compiled. The whole process should only take five or ten minutes.
Lastly, you need to get the exact name of your compiler. In your terminal window, type g++, and then hit tab twice to see a list. On my system, I see this:
> brew search gcc5
You should see a list of packages, including gcc5. Take a note of what is found. (In my case, it found homebrew/versions/gcc5
Then, download and install gcc5:
> brew install homebrew/versions/gcc5
This will download whatever dependencies are needed, generally already pre-compiled. The whole process should only take five or ten minutes.
Lastly, you need to get the exact name of your compiler. In your terminal window, type g++, and then hit tab twice to see a list. On my system, I see this:
Pauls-MBA:~ pmacklin$ g++
g++ g++-5 g++-mp-5
Look for the version of g++ without an "mp" in its name. In my case, it's g++-5. Double-check that you have the right one by checking its version. It should look something like this:
Pauls-MBA:~ pmacklin$ g++-5 --version
g++-5 (Homebrew gcc5 5.2.0) 5.2.0
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
Notice that Homebrew shows up in the information. The correct compiler is g++-5.
5) Test the compiler
Write a basic parallelized program:
Open Terminal (see above). You should be in your user profile's root directory. Make a new subdirectory, enter it, and create a new file:
> mkdir omp_test
> cd omp_test
> nano omp_test.cpp
Then, write your basic OpenMP test:
#include <iostream>
#include <cmath>
#include <vector>
#include <omp.h>
int main( int argc, char* argv[] )
{
omp_set_num_threads( 8 );
double pi = acos( -1.0 );
std::cout << "Allocating memory ..." << std::endl;
std::vector<double> my_vector( 128000000, 0.0 );
std::cout << "Done!" << std::endl << std::endl;
std::cout << "Entering main loop ... " << std::endl;
#pragma omp parallel for
for( int i=0; i < my_vector.size(); i++ )
{
my_vector[i] = exp( -sin( i*i + pi*log(i+1) ) );
}
std::cout << "Done!" << std::endl;
return 0;
}
Save the file (as omp_test.cpp). (In nano, use [Control]-X, Y, and then confirm the choice of filename.)
In the omp_set_num_threads() line above, replace 8 with the maximum number of virtual processors on your CPU. (For a quad-core CPU with hyperthreading, this number is 8. On a hex-core CPU without hyperthreading, this number is 6.) If in doubt, leave it alone for now.
Write a makefile:
Next, create a Makefile to start editing:
> nano Makefile
Add the following contents:
> nano Makefile
Add the following contents:
CC := g++-5
# replace this with your correct compiler as identified above
# replace this with your correct compiler as identified above
ARCH := core2 # Replace this with your CPU architecture.
# core2 is pretty safe for most modern machines.
CFLAGS := -march=$(ARCH) -O3 -fopenmp -m64 -std=c++11
COMPILE_COMMAND := $(CC) $(CFLAGS)
OUTPUT := my_test
all: omp_test.cpp
$(COMPILE_COMMAND) -o $(OUTPUT) omp_test.cpp
clean:
rm -f *.o $(OUTPUT).*
Go ahead and save this (as Makefile). ([Control]-X, Y, confirm the filename.)
Compile and run the test:
Go back to your (still open) command prompt. Compile and run the program:
> make
> ./my_test
The output should look something like this:
Allocating memory ...
Done!
Entering main loop ...
Done!
Note 1: If the make command gives errors like "**** missing separator", then you need to replace the white space (e.g., one or more spaces) at the start of the "$(COMPILE_COMMAND)" and "rm -f" lines with a single tab character.
Note 2: If the compiler gives an error like "fatal error: 'omg.h' not found", you probably used Apple's build of clang, which does not include OpenMP support. You'll need to make sure that you specify your compiler on the CC line of your makefile.
Now, let's verify that the code is using OpenMP.
Open another Terminal window. While the code is running, run top. Take a look at the performance, particularly CPU usage. While your program is running, you should see CPU usage fairly close to '100% user'. (This is a good indication that your code is running the OpenMP parallelization as expected.)
Note 2: If the compiler gives an error like "fatal error: 'omg.h' not found", you probably used Apple's build of clang, which does not include OpenMP support. You'll need to make sure that you specify your compiler on the CC line of your makefile.
Now, let's verify that the code is using OpenMP.
Open another Terminal window. While the code is running, run top. Take a look at the performance, particularly CPU usage. While your program is running, you should see CPU usage fairly close to '100% user'. (This is a good indication that your code is running the OpenMP parallelization as expected.)
What's next?
Download a copy of BioFVM and try out the included examples!- BioFVM announcement on Blogspot: [click here]
- BioFVM on MathCancer.org: http://BioFVM.MathCancer.org
- BioFVM on SourceForge: http://BioFVM.sf.net
- BioFVM Method Paper in BioInformatics: http://dx.doi.org/10.1093/bioinformatics/btv730
