Mechanical APDL Basic Analysis Guide - Ansys

Chapter 5: Solution
standing how memory is used by each solver can help you to avoid problems (such as running out of memory
during solution) and maximize the problem size you can handle on your system.
5.3.1. Running ANSYS Solvers under Shared Memory
One of the easiest ways to improve ANSYS solvers' performance is to run the solvers on a shared memory
architecture, using multiple processors on a single machine. For detailed information on using the shared
memory architecture, see Activating Parallel Processing in a Shared-Memory Architecture in the Advanced
Analysis Techniques Guide.
The sparse solver has highly tuned computational kernels that are called in parallel for the expensive matrix
factorization. The PCG solver has several key computation steps running in parallel. For the PCG and sparse
solvers, there is typically little performance gain in using more than four processors for a single ANSYS job.
See "Using Shared-Memory ANSYS" in the Advanced Analysis Techniques Guide or the Distributed ANSYS Guide
for more information on using ANSYS' parallel processing capabilities.
5.3.2. Using ANSYS' Large Memory Capabilities with the Sparse Solver
If you run on a 64-bit workstation or server with at least 8 GB of memory and you use the sparse solver, you
can take advantage of ANSYS' large memory capabilities. The biggest performance improvement comes for
sparse solver jobs that can use the additional memory to run in-core (meaning that the large LN09 file
produced by the sparse solver is kept in memory). You will generally need 10 GB of memory per million
degrees of freedom to run in-core. Modal analyses that can run in-core using 6 to 8 GB of memory (500K -
750K DOFs for 100 or more eigenmodes) will show at least a 30 - 40% improvement in time to solution over
a 2 GB system.
You can configure memory for sparse solve in-core runs explicitly using the BCSOPTION command, but the
easiest way to access this capability is to increase the initial ANSYS memory allocation so that the amount
of memory available to the sparse solver exceeds the in-core memory requirement. The performance improvement
over a 32-bit system configured with nominal I/O performance can be even more significant
when the sparse solver memory requirement for optimal out-of-core operation is larger than a 32-bit system
can allocate. In such cases, I/O for the sparse solver factorization can increase factorization time tenfold on
32-bit systems compared to larger memory systems that run either in optimal out-of-core mode or in-core.
An important factor in big memory systems is system configuration. You will always see the best ANSYS
performance with processor/memory configurations that maximize the memory per node. An 8-processor,
64 GB system is much more powerful for large memory jobs than a 32-processor 64 GB system. ANSYS
cannot effectively use 32 processors for one job but can use 64 GB very effectively to increase the size of
models and reduce solution time. You will see the best performance for jobs that run comfortably within a
given system configuration. For example, a sparse solver job that requires 7500 MB on a system with 8 GB
will not run as well as the same job on a 12-16 GB system. Large memory systems use their memory to hide
I/O costs by keeping files resident in memory automatically, so even jobs too large to run in-core benefit
from large memory.
All ANSYS software supports large memory usage. It is recommended for very large memory machines where
you can run a large sparse solver job in-core (such as large modal analysis jobs) for the greatest speed and
efficiency. To use this option:
1. Increase the initial ANSYS memory allocation via -m (for example, -m 24000). This initial memory
setting should be larger than what the sparse solver actually requires to account for memory used
prior to the sparse solver.
2. You can further refine sparse solver memory using the BCSOPTION command.
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