![]() ![]() Mac OSX is almost the same as Linux and both GNU make and ninja can be used by following the instructions in the Linux section. Here is an example: # assume your source code is in a directory called src and you are one directory up from there Once you have ninja built and installed in your PATH, you are ready to run cmake. For more information run ninja –help with the ninja you have built. It does however accept a –j flag with the same syntax as GNU make, -j N where N is the number of jobs run in parallel. It defaults to building cores +2 jobs at once (thanks to Matthew Woehlke for pointing out that it is not simply 10 as I had originally stated.). Ninja does not require a –j flag like GNU make to perform a parallel build. Once it is built, you will need to put ninja in your PATH so CMake can find it. Basically, you just run python bootstrap.py. There is a README in the top of the ninja source tree that explains how to build it. You will need python and a c++ compiler to build ninja. The source for ninja can be found here: git:///martine/ninja.git or if you use Fortran here: /Kitware/ninja. To use ninja you will need to first build ninja from source. The ninja tool is very quick to figure out that it has nothing to do which is important for incremental builds of large projects. Kitware maintains a branch of ninja with the required features on /Kitware/ninja. As of July 2017, the needed features have not been integrated into upstream ninja. Fortunately, CMake now has a ninja generator so that your project can take advantage of this new tool. The Ninja generator conditionally supports Fortran when the ninja tool has the required features. ninja was created to run faster than make and of course run parallel builds very well. This is a replacement for the GNU make tool. Some developers at Google recently created a new build tool called ninja. # assume your source code is in a directory called src and you are one directory up from there mkdir build So, if you have a quad core Linux machine, you would run make –j5. For minimum build times, you want to use a value of N that is one more than the number of cores on the machine. The N argument is used to specify how many jobs are run in parallel during the build. The flag tells make to build in parallel. To have parallel builds with gmake, you need to run gmake with the –jN command line option. It is used by CMake’s “Unix Makefiles” generator. The traditional gmake tool which is usually installed as “make” on Linux systems can run parallel builds. I will start with Linux, followed by Apple OSX, and finish up with Windows. It is specified either in the cmake-gui or with the –G command line option to cmake. CMake generator – A CMake generator is a target build tool for CMake.Basically, it invokes the compiler command line for independent objects at the same time. Object Level Parallelism – This is when a build system builds individual object files at the same time.High level targets are things like libraries and executables. ![]() Target Level Parallelism – This is when a build system builds high level targets at the same time.In this blog, I will cover the approaches for parallel builds on the major platforms and tool chains supported by CMake. Since CMake is a Meta build tool that does not directly build software, but rather generates build files for other tools, the approaches to parallel building differ from generator to generator and platform to platform. The CMake build system and its developers have always been aware of how important parallel builds are, and have made sure that CMake could take advantage of them when possible. OK, I admit this is a bit of a lame analogy, but I don’t think you would find too many developers that are not interested in building software as fast as possible using all of the horse power available on the hardware they are using. Software developers are equally motivated to use as much of their “cores” as possible to build software. This allows for the most efficient means of moving towards my running goals. ![]() As a distance runner, it is important to run using a fully engaged core. ![]()
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