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.
Shared Memory vs. Distributed Memory: Parallelization can be achieved in two primary ways – shared memory parallelization and distributed memory parallelization. In shared memory parallelization, multiple processors or cores access the same memory, while in distributed memory parallelization each processor has its own memory, and communication between processors is necessary to exchange information.
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.
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.
Load Balancing: Load balancing is essential to ensure that the computational workload is evenly distributed among all processors. An imbalance in workload can lead to some processors idling while others are overloaded, resulting in inefficient parallelization.
Parallel Libraries and APIs: Many FEM software packages leverage parallel programming libraries and APIs (Application Programming Interfaces) to implement parallelization effectively. Examples include OpenMP, MPI (Message Passing Interface), CUDA (for GPU parallelization), and others.
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. Iterative solvers like the Conjugate Gradient Method can benefit from parallel computation.
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).
Effective parallelization in FEM software can significantly reduce simulation times, allowing engineers and researchers to analyze larger and more complex problems efficiently.