Linear Static 
Hybrid (SMP + DDM) 
 You can use as many DDM processes
(
np ) as allowed by available memory,
and the remainder of the cores can be distributed as SMP
(nt ).
 If multiple subcases with different BCs exist, then
Multilevel DDM will likely be automatically turned ON.
Different BCs will be parallelized.
 Using the PCG solver could show performance benefit in
cases where DDM cannot be run. Refer to SOLVTYP and Solvers for more
information.

Nonlinear Static Analysis 
Hybrid (SMP +DDM) 
 You can use as many DDM processes
(
np ) as allowed by available memory,
and the remainder of the cores can be distributed as SMP
(nt ).
 For multiple nonlinear subcases, if you believe that
solving multiple nonlinear subcases in parallel can
provide more speedup than sequential, then multilevel
DDM can be manually turned on via PARAM,DDMNGRPS,# (refer to Domain Decomposition Method
for certain exceptions).

Nonlinear Transient Analysis 
Hybrid (SMP + DDM) 
 You can use as many DDM processes
(
np ) as allowed by available memory,
and the remainder of the cores can be distributed as SMP
(nt ).
 Multilevel DDM is not supported. Therefore,
subcasebased parallelization is not supported.

Normal Modes Analysis using Lanczos (Eigenvalue
Analysis) 
Hybrid (DDM + SMP) 
 Geometricpartitioning via DDM is supported for
Lanczos, and DDM can be used via
np
for #cores up to the limit enforced by
available memory. The remaining cores can be distributed
via SMP (nt ).

Normal Modes Analysis using AMSES (Eigenvalue
Analysis) 
Single Modal Space  SMP Multiple Modal Spaces  Hybrid
(DDM + SMP) 
 Geometricpartitioning via DDM is not supported for
AMSES.
 If there is only a single modal space, SMP can be used
via
nt .
 If there are multiple modal spaces, then BCbased
parallelization is supported for AMSES and a hybrid
SMP+DDM can be used.
np can be set
equal to the number of modal spaces and the remaining
cores can be distributed via SMP (nt ).

Normal Modes Analysis using AMLS (Eigenvalue
Analysis) 
SMP 
 Geometricpartitioning via DDM is not supported for
AMLS. Similarly, BCbased parallelization is not
supported either. Therefore, only SMP is
recommended.

Linear Modal Frequency Response Analysis This only
discusses FRF solution part. For Eigenvalue extraction, see
above. 
Hybrid (DDM + SMP) 
 Modal Frequency Response parallelization depends on two
main parts:
 Eigenvalue extraction
 FRF solution
 The performance depends on which solution is dominant
with regards to the runtime. If the
#modes are high, and
#loading frequencies are low,
eigenvalue extraction can dominate; otherwise, the FRF
solution dominates.
 Eigenvalue extraction has already been covered above.
 For Modal FRF solution using regular factorization, BCs
are solved sequentially and loading frequencies are
parallelized via DDM. Such frequency partitioning via
DDM can speedup the solution.
 For Modal FRF solution using FASTFR, DDM may not be
useful, unless there is a good compensation via higher
eigenextraction DDM performance.

Linear Modal Transient Response Analysis This only
discusses Transient Energy Equation solution part. For
Eigenvalue extraction, see above. 
Hybrid (DDM + SMP) 
 Modal Transient Response parallelization depends on two
main parts:
 Eigenvalue extraction
 Transient Response solution
 The performance depends on which solution is dominant
with regards to the runtime. If the
#modes are high, and transient
response solution is quick, eigenvalue extraction can
dominate; otherwise, the transient solution dominates.
 Eigenvalue extraction has already been covered above.
 For Modal Transient Response solution, BCs are
parallelized via Multilevel DDM. However, DDM level2
geometric partitioning is not supported for Transient
solution within each MPI group. Therefore, in such
cases, certain MPI processes within each group may
remain idle.

Linear Direct Transient Analysis 
Hybrid (SMP + DDM) 
 For small models, SMP may outperform DDM since BCs are
not parallelized for Linear Direct Transient.
 For larger models, DDM may provide more speedup since
geometricpartitioning is supported.
 The NewmarkBeta time integration method may improve
performance in certain cases.

Linear Direct Frequency Response Analysis 
Hybrid (SMP + DDM) 
 For small models, SMP may outperform DDM since BCs are
not parallelized for Linear Direct Frequency
Response.
 For larger models, DDM may provide more speedup since
frequencysplitting is supported and distributed among
different MPI process groups and within each group
geometricpartitioning is performed using MUMPS.
