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Parallel Runs
Some of the TURBOMOLE modules are parallelized using the message passing
interface (MPI) for distributed and shared memory machines or with OpenMP or multi-threaded techniques for shared memory and multi-core machines.
Generally there are two hardware scenarios which determine the kind of parallelization that is possible to use:
The list of parallelized programs includes presently:
- ridft -- parallel ground state Hartree-Fock and DFT energies including RI-J and the multipole accelerated RI (MA-RI-J)
- rdgrad -- parallel ground state gradients from ridft calculations
- dscf -- Hartree-Fock and DFT ground state calculations for all available DFT functionals, without the usage of RI-J approximation
- grad -- parallel ground state gradients from dscf calculations
- ricc2 -- parallel ground and excited state calculations of energies and gradients at MP2 and CC2 level using RI, as well as energy calculations of other wave function models, see chapter 9.5.
- mpgrad -- parallel conventional (i.e. non-RI) MP2 energy and gradient calculations. Please note that RI-MP2 is one to two orders of magnitude faster than conventional MP2, so even serial RI-MP2 will be faster than parallel MP2 calculations.
- aoforce -- parallel Hartree-Fock and DFT analytic 2nd derivatives for vibrational frequencies, IR spectra, generation of Hessian for transition state searches and check for minimum structures. SMP only.
- escf -- parallel TDDFT, RPA, CIS excited state calculations (UV-Vis and CD spectra, polarizabilities). SMP only.
- egrad -- parallel TDDFT, RPA, CIS excited state analytic gradients, including polarizability derivatives for RAMAN spectra. SMP only.
- NumForce -- this script can used for a trivial parallelization of the numerical displaced coordinates.
Additional keywords necessary for parallel runs with the MPI binaries are
described in Chapter 17.
However, those keywords do not have to be set by the users. When using the
parallel version of TURBOMOLE, scripts are replacing the binaries. Those
scripts prepare a usual input, run the necessary steps and automatically start the parallel programs.
The users just have to set environment variables, see Sec. 3.2.1 below.
To use the OpenMP parallelization only an environment variable needs to be set. But
to use this parallelization efficiently one should consider a few additional points,
e.g. memory usage, which are described in Sec. 3.2.2.
Subsections
Next: Running Parallel Jobs
Up: How to Run TURBOMOLE
Previous: Modules and Data Flow
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TURBOMOLE