Parallelization in Finite Element Method (FEM) software refers to the process of dividing and distributing computational tasks among multiple processors or cores in order to accelerate the analysis and simulation of complex engineering problems. Parallel computing enables the software to handle larger and more intricate simulations by harnessing the power of modern multi-core CPUs and even distributed computing clusters.
Here are some key aspects of parallelization in FEM software:
Domain Decomposition: One common approach to parallelization involves dividing the simulation domain into smaller subdomains. Each subdomain is then assigned to a different processor or core, and computations are performed concurrently on these separate parts. Once the local computations are completed, the results are combined to obtain the overall solution.
This technology is used in RFEM 6 and RSTAB 9 for the parallel calculation of load cases and load combinations. A solver instance is started for each core, which then calculates load cases or load combinations one after the other.
Multithreading and Multiprocessing: FEM software can use multithreading and multiprocessing techniques to parallelize computations. Multithreading involves dividing a single program into smaller threads that can be executed concurrently. Multiprocessing involves running multiple independent processes or tasks simultaneously.
Both techniques are used in RFEM 6.
Several independent solver processes are started for the parallel calculation of load cases and load combinations. This is the technique of multiprocessing.
If the direct solver is used for the individual calculation of a load case or a load combination, the workload is split into several threads to solve the equation system.
Scalability: Scalability refers to how well FEM software can handle an increasing number of processors or cores. A well-designed parallel FEM software should exhibit good scalability, meaning that as more processors are added, the performance improvement is significant and the simulation time decreases.
Parallel Libraries and APIs: Many FEM software packages leverage parallel programming libraries and APIs (Application Programming Interfaces) to implement parallelization effectively. Examples include OpenMP, CUDA (for GPU parallelization), and others.
OpenMP is used in RFEM 6 for the implementation of multithreading.
CUDA is not used in RFEM 6. The calculation can therefore not be performed on graphics cards or similar computing accelerators.
Solver Parallelization: In FEM simulations, the solution of linear systems of equations is a common bottleneck. Advanced solvers can be parallelized to improve solution speed.
Preprocessing and Postprocessing: While much of the focus is on parallelizing the solver phase, parallelization can also be applied to other aspects of the FEM analysis, including preprocessing tasks (mesh generation, model setup) and postprocessing (visualization, data analysis).
In both preprocessing and postprocessing, parallelization through multithreading is used in RFEM 6 and RSTAB 9.
Effective parallelization in FEM software can significantly reduce simulation times, allowing engineers and researchers to analyze larger and more complex problems efficiently.