SPARSE

 — 

Sparse direct equation solver. Applicable to real-value or complex-value symmetric and unsymmetric matrices. Available only for STATIC, HARMIC (full method only), TRANS (full method only), SUBSTR, and PSD spectrum analysis types (ANTYPE). Can be used for nonlinear and linear analyses, especially nonlinear analysis where indefinite matrices are frequently encountered. Well suited for contact analysis where contact status alters the mesh topology. Other typical well-suited applications are: (a) models consisting of shell/beam or shell/beam and solid elements (b) models with a multi-branch structure, such as an automobile exhaust or a turbine fan. This is an alternative to iterative solvers since it combines both speed and robustness. Generally, it requires considerably more memory (~10x) than the PCG solver to obtain optimal performance (running totally in-core). When memory is limited, the solver works partly in-core and out-of-core, which can noticeably slow down the performance of the solver. See the BCSOPTION command for more details on the various modes of operation for this solver.

This solver can be run using shared-memory parallel (SMP), distributed-memory parallel (DMP), or hybrid parallel processing. For DMP, this solver preserves all of the merits of the classic or shared memory sparse solver. The total sum of memory (summed for all processes) is usually higher than the shared memory sparse solver. System configuration also affects the performance of the distributed memory parallel solver. If enough physical memory is available, running this solver in the in-core memory mode achieves optimal performance. The ideal configuration when using the out-of-core memory mode is to use one processor per machine on multiple machines (a cluster), spreading the I/O across the hard drives of each machine, assuming that you are using a high-speed network such as Infiniband to efficiently support all communication across the multiple machines.

This solver supports use of the GPU accelerator capability.

JCG

 — 

Jacobi Conjugate Gradient iterative equation solver. Available only for STATIC and TRANS (full method only) analysis types (ANTYPE). Can be used for structural, thermal, and multiphysics applications. Applicable for symmetric, unsymmetric, complex, definite, and indefinite matrices. Efficient for heat transfer, electromagnetics, piezoelectrics, and acoustic field problems.

This solver can be run using shared-memory parallel (SMP), distributed-memory parallel (DMP), or hybrid parallel processing. For DMP, in addition to the limitations listed above, this solver only runs in a distributed parallel fashion for STATIC and TRANS (full method) analyses in which the stiffness is symmetric and only when not using the fast thermal option (THOPT). Otherwise, this solver disables distributed-memory parallelism at the onset of the solution, and shared-memory parallelism is used instead.

This solver supports use of the GPU accelerator capability. When using the GPU accelerator capability, in addition to the limitations listed above, this solver is available only for STATIC and TRANS (full method) analyses where the stiffness is symmetric and does not support the fast thermal option (THOPT).

PCG

 — 

Preconditioned Conjugate Gradient iterative equation solver (originally licensed from Computational Applications and Systems Integration, Inc.). Requires less disk file space than SPARSE and is faster for large models. Useful for plates, shells, 3D models, large 2D models, and other problems having symmetric or unsymmetric, sparse matrices. Such matrices are typically positive definite or indefinite for symmetric full harmonic analyses that solve a symmetric complex number matrix system. The PCG solver can be used for:

Requires twice as much memory as JCG. Available only for analysis types (ANTYPE): STATIC, TRANS (full method only), HARMIC (full method only), or MODAL (with PCG Lanczos option only). Also available for the use pass of substructure analyses (MATRIX50). The PCG solver can robustly handle models that involve the use of constraint and/or coupling equations (CE, CEINTF, CP, CPINTF, and CERIG). With this solver, you can use the MSAVE command to obtain a considerable memory savings.

The PCG solver can handle ill-conditioned problems by using a higher level of difficulty (see PCGOPT). Ill-conditioning arises from elements with high aspect ratios, contact, and plasticity.

This solver can be run in shared-memory parallel or distributed-memory parallel mode. In DMP mode, this solver preserves all of the merits of the classic or shared-memory PCG solver. The total sum of memory (summed for all processes) is about 30% more than the shared-memory PCG solver.

This solver supports use of the GPU accelerator capability.

MIXED

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Mixed equation solver. Combines the advantages of the sparse solver and PCG solver. It uses an approximate triangular matrix and substitutions as the preconditioner for the PCG algorithm, improving both speed and memory efficiency without compromising accuracy. This method is available for STATIC and TRANS (full method only) analysis types. It typically requires less memory than the sparse solver and offers faster performance for a range of problems. The mixed solver can be executed using shared-memory parallel (SMP), distributed-memory parallel (DMP), or hybrid parallel processing.

The mixed solver has the following limitations and does not support them:

The mixed solver supports GPU acceleration. Ansys recommends using the GPU acceleration with this solver as it is primed for faster solutions. For more information, see Requirements for the GPU Accelerator in Mechanical APDL and Requirements for the GPU Accelerator in Mechanical APDL.

DELE

 — 

Deletes all files from the SPARSE solver run, including the factorized file, .DSPsymb, upon FINISH or /EXIT (default).

KEEP

 — 

Retains all necessary files from the SPARSE solver run, including the .DSPsymb file, in the working directory.