Task-Based Runtime Support and Programming for Finite Element Simulations
Bibliographic record
Abstract
My work studies task-based runtime support for finite element method (FEM) assembly using StarPU. The idea is to replace bulk-synchronous loops with parallelized approach. Local kernels were rewritten in modern C++20/23, relying on non-owning views (std::span, std::mdspan) and a preallocated argument table to avoid repeated allocations. I also used METIS for partitioning and a greedy coloring step to guarantee conflict-free execution.The implementation is done for the project DOLFINx/FEniCS. On the 2D Poisson problem, assembly is already cheap and preprocessing dominates, so there is little to no gain and even loss of time on small model. On a 3D hyperelasticity benchmark, where each element is more expensive, the task-based model works well. Grouping cells into partitions reduces the number of tasks and keeps scheduling overhead low while maintaining balance. These results suggest that task-based assembly is a practical choice for nonlinear or compute-intensive models. Future extensions will need to focus on NUMA-aware scheduling, custom StarPU schedulers, and GPU support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".