Available Compilers

We are currently supporting two Compiler Suites on the Linux platform:

• The Intel Compiler Suite is located in the /opt/ics directory. The version is 12.1. The compilers ifort and icc are in the /opt/ics/composer_xe_2011_sp1.6.233/bin/intel64 directory. Various libraries are in the /opt/ics/lib/intel64 directory.
• As part of the CentOS distribution, we also have the Gnu C/C++ and Fortran Compilers called gcc, g++ and gfortran, respectively. They are installed in /usr/bin.

Setup

• For setting up the Intel Compiler Suite you need to issue the command

  use icsmpi

  . This replaces the issuing of lengthy setting for environment variables by a simple command use. It also adds the proper directories to the PATH variable.

• The public-domain compilers "gcc" and "gfortran" are available by default, i.e. they require no set-up. The current version for these is 4.4.7-4. Sometimes these compilers are required when compiling public-domain programs. We recommend the use of these compilers unless Intel is required to improve performance.

Compiling and Linking

• The Intel compilers are called ifort and icc for Fortran (all versions) and C/C++, respectively.
• The public-domain gnu compilers are called gcc, g++, and gfortran for C, C++, and Fortran (all versions), respectively.

Compiling and linking is best done with a makefile. Here are a few common flags. Consult man pages for specific details (for instance "man gcc").

Compiling

compiler -c [options] name.ext

where "compiler" stands for the compiler name, for instance "gfortran" for the GNU Fortran compiler. The file extension "ext" determines what source code is being compiled, for instance "f" means "fixed format" Fortran, f90 means "free format" Fortran (90), or "c" stands for C. "[options]" denotes additional compiler flags that usually start with a '-'.

Linking

compiler -o name [options] [libraries] list

"compiler" see above. "name" is the name of the executable (if not specified, the default is "a.out". [options] see above. [libraries] is a list of libraries that need to be linked in, usually as a list of file names with full path, or as '-L' and '-l' combinations [see below]. "list" means a list of object files, usually with ".o" extension.

Using the compilers and the linker in the above manner requires the proper setting of the PATH environment variable, i.e. prior set-up. There are hundreds of compiler flags, and many of them are not required most of the time. A few that are in more frequent use are:

• -On optimizes your code. "n" is a number from 1 to 5 with increasing severity of alterations made to the code, but also increasing gain. Up to -xO3 is generally rather safe to use. But you should, of course, always check results against an un-optimized version: they might differ.
• '-fast is a combination of optimization flags that is reasonably safe to use and often improves performance a lot. However, the resulting code is optimized specifically for the current machine architecture and may not work well (or at all) on platforms that differ from the one compiled on. Note that this usually overrides the "-On" option. If you use this flag for compiling, you also need to include it at the linking stage.
• -g produces code that can be debugged. -g and -On are not necessarily mutually exclusive, but optimization may make debugging difficult, because it alters the relationship between source code and executable. This is a good flag to have in the development stage of a program, but is usually dropped later.
• -V (or -v) produces the version of the compiler.
• -lname is used to bind in a library called libname.a (static) or libname.so (dynamic). This flag is used to link only.
• -L dirname is used in conjunction with -lname and lets the linker know where to look for libraries. "dirname" is a directory name such as /opt/studio12/SUNWspro/prod/lib.
• -Rdirname is used to tell the program where to get dynamic libraries at runtime.

There are many more flags. They are documented in the man pages (e.g. "man f90" for the Studio Fortran compiler), as well in the documentation for the compiler. Some compiler flags are only useful for parallel programs and will be discussed later. Sometimes there is a considerable performance gain from using specific options (such as "-fast"), but the code becomes less general.

An Example

The working principle of MPI is perhaps best illustrated on the grounds of a programming example. The following program, written in Fortran 90 computes the sum of all square-roots of integers from 0 up to a specific limit m:

 module mpi 
  include 'mpif.h' 
 end module mpi 

 module cpuids 
  integer::myid,totps, ierr 
 end module cpuids 

 program example02 
  use mpi 
  use cpuids 
  call mpiinit 
  call demo02 
  call mpi_finalize(ierr) 
  stop 
 end 

 subroutine mpiinit 
  use mpi
  use cpuids 
  call mpi_init( ierr ) 
  call mpi_comm_rank(mpi_comm_world,myid,ierr) 
  call mpi_comm_size(mpi_comm_world,totps,ierr) 
  return 
 end 

 subroutine demo02 
  use mpi 
  use cpuids 
  integer:: m, i 
  real*8 :: s, mys 
  if(myid.eq.0) then 
   write(*,*)'how many terms?' 
   read(*,*) m 
  end if 
  call mpi_bcast(m,1,mpi_integer,0,mpi_comm_world,ierr) 
  mys=0.0d0 
  do i=myid,m,totps 
   mys=mys+dsqrt(dfloat(i)) 
  end do 
  write(*,*)'rank:', myid,'mys=',mys, ' m:',m 
  s=0.0d0 
  call mpi_reduce(mys,s,1,mpi_real8,mpi_sum,0,mpi_comm_world,ierr) 
  if(myid.eq.0) then 
   write(*,*)'total sum: ', s 
  end if 
  return 
 end 

Some of the common tasks that need to be performed in every MPI program are done in the subroutine mpiinit in this program. Namely, we need to call the routine mpi_init to prepare the usage of MPI. This has to be done before any other MPI routine is called. The two routine calls to mpi_comm_size and call mpi_comm_rank determine how many processes are running and what is the unique ID number of the present, i.e. the calling process. Both pieces of information are essential. The results are stored in the variables totps and myid, respectively. Note that these variables appear in a module cpuids so that they may be accessed from all routines that "use" that module.

The main work in the example is done in the subroutine demo02. Note that this routine does use the module cpuids. The first operation is to determine the maximum integer m in the sum by requesting input from the user. The if-clause if(myid.eq.0) then serves to restrict this I/O operation to only one process, the so-called "root process", usually chosen to be the one with rank (i.e. unique ID number) zero.

After this initial operation, communication has become necessary, since only one process has the right value of m. This is done by a call to the MPI collective operation routine mpi_bcast. This call has the effect of "broadcasting" the integer m. This call needs to be made by all processes, and after they have done so, all of them know m.

The sum over the square root is then executed on each process in a slightly different manner. Each term is added to a local variable mys. A stride of totps (the number of processes) in the do-loop ensures that each process adds different terms to its local sum, by skipping all others. For instance, if there are 10 processes, process 0 will add the square-roots of 0,10,20,30,..., while process 7 will add the square-roots of 7,17,27,37,...

After the sums have been completed, further communication is necessary, since each process only has computed a partial, local sum. We need to collect these local sums into one total, and we do so by calling mpi_reduce. The effect of this call is to "reduce" a value local to each process to a variable that is local to only one process, usually the root process. We can do this in various ways, but in our case we choose to sum the values up by specifying mpi_sum in the function call. Afterwards, the total sum resides in the variable s, which is printed out by the root process.

The last operation done in our example is finalizing MPI usage by a call to mpi_finalize, which is necessary for proper program completion.

In this simple example, we have distributed the tasks of computing many square roots among processes, each of which only did a part of the work. We used communication to exchange information about the tasks that needed to be performed, and to collect results. This mode of programming is called "task parallel". Often it is necessary to distribute large amounts of data among processes as well, leading to "data parallel" programs. Of course, the distinction is not always clear.

Implementations

While MPI itself is a portable, platform independent standard, much like a programming language, the actual implementation is necessarily platform dependent since it has to take into account the architecture of the machine or cluster in question.

The most commonly used implementation of MPI for the Linux platform is called OpenMPI. The following considerations will be focussed on this implementation.

Our machines are small to mid-sized shared-memory machines that form a cluster. Since the interconnect between the individual nodes is a bottleneck in efficient program execution, most of the MPI programs running on our machines are executed within a node. This alloows processes to commuincate rapidly through a so-called "shared-memory layer". Our cluster is configured in to preferably schedule processes within a single node.

Currently, two versions of the OpenMPI parallel environment are in common use:

• For the Intel compiler suite, an Intel implementation of OpenMPI is automatically available when setting up the compiler suite with the

  use icsmpi

  command.
• For the gnu compiler, OpenMPI is made available through the

  use openmpi

  setup command.

We do not recommend to have both versions set up simultaneously.

Compiling MPI code

The compilation of MPI programs requires a few compiler options to direct the compiler to the location of header files and libraries. Since these switches are always the same, they have been collected in a macro to avoid unnecessary typing. The macro is has an mpi prefix before the normal compiler name. The commands are mpiifort for the Intel Fortran compiler, mpiicc for the gnu C compilers, respectively. For instance, if a serial C program is compiled by

gcc -O3 -c test.c

the corresponding parallel (MPI) program is compiled (using gnu compiler) by

mpicc -xO3 -c test_mpi.c

In the linking stage, the usage of mpi* macros also includes the proper specification of the MPI libraries. For example, the above MPI program should be linked with something like this:

mpicc -o test_mpi.exe test_mpi.o

Compiling and linking may also be combined by omitting the -c option and including the naming option (-o) in the compilation line.

Here are the corresponding MPI macros for the 6 commonly used compilers on our systems:

Running MPI programs

To run MPI programs, a special Runtime Environment is required. This includes commands for the control of multi-process jobs.

mpirun is used to start a multi-process run of a program. This required to run MPI programs. The most commonly used command line option is -np to specify the number of processes to be started. For instance, the following line will start the program test_mpi.exe with 9 processes:

mpirun -np 9 test_mpi.exe

The mpirun command offers additional options that are sometimes useful or required. Most tend to interfere with the scheduling of jobs in a multi-user environment such as ours and should be used with caution. Please consult the man pages for details.

Note that the usage of a scheduler is mandatory for production jobs on our system. This option is therefore used frequently. For a details about Gridengine and jobs submission on our machines and clusters, go here.

More Information

As already pointed out, this FAQ is not an introduction to MPI programming. The standard reference text on MPI is:

Marc Snir, Steve Otto, Steven Huss-Lederman, David Walker, and Jack Dongarra:
MPI - The Complete Reference (2nd edition), The MIT Press, Cambridge, Massachusetts, 2000;
2 volumes, ISBN 0-262-69215-5 and 0-262-69213-3

This text specifies all MPI routines and concepts, and includes a large number of examples. Most people will find it sufficient for all their needs.

A quite good online tutorial for MPI programming can be found at the Maui HPCC site.

There is also an official MPI webpage which contains the standards documents for MPI and gives access to the MPI Forum.

We are conducting Workshops on a regular basis, some devoted to MPI programming. They are announced on our web site. We might see you there sometime soon.

Some Tools

Standard debugging and profiling tools such as Sun Studio are designed for serial or multi-threaded programs. They do not handle multi-process runs very well.

Quite often, the best way to check the performance of an MPI program is timing it by insertion of suitable routines. MPI supplies a "wall-clock" routine called MPI_WTIME(), that lets you determine how much actual time was spent in a specific segment of your code. An other method is calling the subroutines ETIME and DTIME, which can give you information about the actual CPU time used. However, it is advisable to carefully read the documentation before using them with MPI programs. In this case, refer to the Sun Studio 12: Fortran Library Reference.

We also provide a package called the HPCVL Working Template (HWT), which was created by Gang Liu. The HWT provides 3 main functionalities:

• Maintenance of multiple versions of the same code from a single source file. This is very useful, if your MPI code is based on a serial code that you want to convert.
• Automatic Relative Debugging which allows you to use pre-existing code (for example the serial version of your program) as a reference when checking the correctness of your MPI code.
• Simple Timing which is needed to determine bottlenecks for parallelization, to optimize code, and to check its scaling properties.

The HWT is based on libraries and script files. It is easy to use and portable (written largely in Fortran). Fortran, C, C++, and any mixture thereof are supported, as well as MPI and OpenMP for parallelism. Documentation of the HWT is available. The package is installed on our clusters in /opt/hwt.

Help

Send email to cac.help@queensu.ca. We have scientific programmers on staff who will probably be able to help you out. Of course, we can't do the coding for you but we do our best to get your code ready for parallel machines and clusters.