# Make Process¶

Building the code is achieved simply by invoking the Makefile in the main directory. The Makefile contains the set of instructions that build the executable, normally called “fargo3d” according to a specific setup and with proper instructions for generating a CPU, GPU, sequential or parallel built. This Makefile is not standalone. A set of scripts was developed in order to simplify the building process. These scripts do all the hard work. Most users will not need to know the different stages of a build, but we give here the detail of what happens when you issue make [options] in the main directory.

A schematic view of the make process is:

We can see that the make process involves three main steps. When we type make in the main directory, we spawn the first step, that is we execute the Makefile. All the shortcut rules, cleaning rules, and testing rules are defined within. The second step is spawned by the Makefile itself, and amounts to running the scripts/make.py file, that works as a link between the main Makefile and the src/makefile file. The third and last step corresponds to the execution of the src/makefile . All the scripts are managed at this stage (automatic conversion of C to CUDA, analysis of boundary conditions and scaling rules, etc.)

## Makefile (Stage1)¶

As we have just seen the first file related with the make process is “Makefile” in the main directory. Inside, there is a set of instructions that manages the build variables (such as GPU, PARALLEL, etc.) This makefile works as a wrapper that further invokes scripts/param.py. There are two ways to define a build variable from the command line: using shortcut rules, or defining the value of the variables manually. The set of variables allowed are:

• PROFILING: [0,1] The profiler flags and a set of timers will be activated. Useful for benchmarks and development.

• RESCALE: [0,1] Activates a rescaling of the (real) parameters of the parameter file to certain units. The numerical values used for this rescaling depends on the value of the build variable UNITS. The output are all done with the new units.

• SETUP: Selects the proper directory where your setup is. A list will be shown if you type make list.

• MPICUDA: [0,1] Activates the peer2peer/uva MPI-Communications between different devices. Only compatible with MVAPICH2 2.0 and OpenMPI 1.7. (Strongly recommended)

• FARGO_DISPLAY: [NONE,MATPLOTLIB] Run time visualization of your fields using python-numpy-matplotlib packages.

• DEBUG: [0,1] The code will be compiled in debug mode. The main change is the flag “-g” at compilation time. No optimization is performed in this mode.

• FULLDEBUG: [0,1] The code runs in full debug mode. Similar to debug mode, but the merge flag (-m) is not allowed. All the fields are dumped with their ghosts cells (buffers).

• UNITS: [0,MKS,CGS] How the units are interpreted by the code. See the unit section for further details.

• GPU: [0,1] Activates a GPU compilation.

• PARALLEL: [0,1] Activates the use of the MPI Libraries.

• BIGMEM: [0,1] Activates the use of the global GPU memory to store light (1D) arrays (which are otherwise stored in the so-called constant memory on board the GPU). Normally, it is needed when your simulation exceeds ~750 cells in some direction. This feature is device-dependent. Typically, a 3D run with mesh size 500*500*500 does not require BIGMEM=1, but a 2D run with mesh size 50*1000 does. However, when BIGMEM=1 is not required, you may activate it. Perform some benchmarking to check which choice yields faster results. This is problem and platform dependent.

• jobs/JOBS: [N] where N is the number of processes that you want to spawn for building the code. By default N=8. This option is much needed when working with CUDA. The building process of CUDA object files is by far the most expensive stage of building a GPU instance of FARGO3D, as you will soon realize.

All these variables must be defined by: make VARIABLE=VALUE.

The shortcut rules are invoked with the command make option, where option can be:

• cuda/nocuda, gpu/nogpu –> GPU=1/0

• bigmem/nobigmem –> BIGMEM=1/0

• seq/para –> PARALLEL=0/1

• debug/nodebug –> DEBUG=1/0

• fulldebug/nofulldebug –> FULLDEBUG=1/0

• prof/noprof –> PROFILING=1/0

• mpicuda/nompicuda –> MPICUDA=1/0

• view/noview –> FARGO_DISPLAY=MATPLOTLIB/NONE

• cgs/mks/scalefree –> UNITS=CGS/MKS/0

• rescale/norescale –> RESCALE=1/0

• testlist –> A list of the tests implemented.

• testname –> the test called name.py, found in test_suite, will be executed.

• blocks –> special syntax: make blocks setup=SETUPNAME. Performs a detailed study of the performance of your graphics card with respect to the size of the CUDA blocks. This test will be done for each GPU function. The result is stored in setups/SETUPNAME/SETUPNAME.blocks (go to the section “Increasing the GPU performance”). A build is performed with a default block size if this file does not exist, so you do not have to worry about this feature at this stage. However, remember that it may increase the performance up to ~20 %.

• clean –> Cleans the bin/ directory. Recommended when you switch to another SETUP.

• mrproper –> Removes all the data related to some specific make configuration. All the code is restored to its default. The outputs directory will not be touched.

## scripts/make.py (Stage2)¶

The second step in the building process is to call make.py. This file does not need a manual invocation, and is launched from the main Makefile (stage1). In general, the compilation process is expensive when you are working with CUDA files. For this reason, a parallel make process is highly desirable. Normally, a general Makefile can handle parallel compilation, but in the FARGO3D case (that uses a lot of scripting) some race conditions could appear in parallel Makefiles. Since the GNU make utility does not have a proper way to avoid such problem, we developed an interface between Makefile and src/makefile to do all the building process in the right order: first invoking the scripts to build all the headers and variable declarations and then a parallel execution of src/makefile. Also, make.py keeps track of the last flags used in the last built (sticky options). All this information is stored in the hidden file std/.lastflags.

## src/makefile (Stage3)¶

The third and last step in the building process is to call src/makefile, which is done by scripts/make.py. The role of this makefile is to build the executable, normally called fargo3d in the main directory. All the calls to scripts are done here. Inside there are a set of rules for making the executable in the correct sequence. You may have a look through these different rules, which are self-documented by their names.

There is a set of system-configuration blocks, that allows building FARGO3D on different platforms with the same makefile. This configuration blocks are selected by using the environment variable called FARGO_ARCH (the same as for the FARGO code). Also, inside this makefile are defined a lot of useful variables. Here is where the structure of the code is defined, and where the variable VPATH is specified. This variable is extremely powerful, and if you want to extend the FARGO3D directory structure, you should learn about the use of this variable (GNU Make Reference Guide).

Another important set of variables are:

• MAINOBJ: The name of all the CPU objects that will be linked with the final executable. All new source file in the code must be included in this variable (with the .o extension, instead of .c)
• GPU_OBJ: The name of the static kernels used in the code. In practice, you will never need to touch it. By static we mean that these few kernels are not generated automatically from the C code by a Python script.
• GPU_OBJBLOCKS: The name of the objects that will be generated by the script c2cuda.py. Note all of them must have the suffix _gpu.o, with a prefix that is the one of the corresponding C file. This is very important, because the rule that generates CUDA-files from C-files uses the suffix of the object name. In the tutorial on how to develop a GPU-Routine (function) this will be presented in more detail. All the functions that must be generated automatically from C code at build time must appear as a list in this variable.

## FARGO_ARCH environment variable¶

FARGO3D is a multi-platform code, and can run on a modern cluster of GPUs but also on your personal computer, even without a GPU. For the ease of use, we adopt a computer-dependent makefile scheme, managed by the environment variable FARGO_ARCH.

You can see in src/makefile a group of lines similar to:

#LINUX PLATFORM (GENERIC)
#FARGO_ARCH must be set to LINUX
CC_LINUX      = gcc
SEQOPT_LINUX  = -O3 -ffast-math
PARAOPT_LINUX = ${SEQOPT_LINUX} PARACC_LINUX = mpicc LIBS_LINUX = -lm INC_LINUX = NVCC_LINUX = nvcc PARAINC_LINUX = PARALIB_LINUX =  These lines are telling the makefile where the libraries are and which compilers will be used. In the LINUX case (default case), we are not including any parallel library to PARALIB and any header to PARAINC because we are assuming they are in your LD_LIBRARY_PATH, or they are installed in the default places. In general, in your cluster, you should have something similar to: #FARGO_ARCH must be set to MYCLUSTER CC_MYCLUSTER = /bin/gcc SEQOPT_MYCLUSTER = -O3 -ffast-math PARAOPT_MYCLUSTER =${SEQOPT_LINUX}
PARACC_MYCLUSTER  = /bin/mpicc
LIBS_MYCLUSTER    = -lm
INC_MYCLUSTER     =
NVCC_MYCLUSTER    = ${CUDA}/bin/nvcc PARAINC_MYCLUSTER = -I/${MPIDIR}/include
PARALIB_MYCLUSTER = -L/${MPIDIR}/lib64  Where MPIDIR and CUDA are variables pointing to the place where MPI and Cuda are installed. To use the FARGO_ARCH variable, you have two options: • define FARGO_ARCH before compiling the code. • define FARGO_ARCH in your personal .bashrc or .tcshrc file (depending on your shell). If you do not have a standard Linux distribution, do not forget to export the variable FARGO_ARCH in your ~/.bashrc file: $: vi USER_DIR/.bashrc


export FARGO_ARCH=MYCLUSTER

where MYCLUSTER is only an example name. You should modify it to match the name that you defined in the src/makefile. This file is provided as is with a few examples that you may adapt to your own needs.