Parallelism beyond a single node (16 CPUs on hpc-class) requires the use of MPI, however MPI requires major changes to an existing program. Two ways exist to get parallelism within a single 16 CPU node: parallelism can either be obtained with automatic parallelism (the -parallel Intel compiler option) or with OpenMP (the -openmp Intel compiler option). The simplest way to get parallel execution is to add '-parallel' to your compile command. For more information on automatic parallelization with Intel compilers refer to this document. Another simple way to obtain parallelism is by using OpenMP, which can be used to express parallelism on a shared memory machine. Since each of the nodes on hpc-class is a shared memory machine with 16 processors, OpenMP can be used to obtain parallelism for 16 processors. It requires changes to the program but not nearly as much as MPI. (The gains are generally less than for MPI, but greater than that for automatic parallelism.) E.g. Having the OpenMP directive !OMP$ PARALLEL DO just before do j=2,n-1 do i=2,m-1 a(i,j)=(b(i,j+1)+b(i,j-1)+b(i-1,j)+b(i+1,j)+4.d0*b(i,j))/6.d0 enddo enddo signals to an OpenMP compiler that the j loop can be performed on multiple processors. When run, issue setenv OMP_NUM_THREADS 16 ./a.out and the program will be run with 16 "threads" which can run on each of the 16 processors. Everything runs on just one thread until the above directive is reached, when each of the threads performs 1/16-th of the work in the j loop. Without the -openmp flag on the compilation step the directive is ignored as a comment. For C and C++, pragmas are used rather than directives. In general, OpenMP programs run the fastest when most of the operations are on data which is "private" rather than "shared". See the OpenMP Specifications for the meaning of private and shared data with regard to OpenMP. The Intel compilers on hpc-class implement OpenMP 3.0 .