Convergence order vs. parallelism in the numerical simulation of the bidomain equations
Bibliographic record
Abstract
The propagation of electrical activity in the human heart can be modelled mathematically by the bidomain equations. The bidomain equations represent a multi-scale reaction-diffusion model that consists of a set of ordinary differential equations governing the dynamics at the cellular level coupled with a set of partial differential equations governing the dynamics at the tissue level. Significant computation is generally required to generate clinically useful data from the bidomain equations. Contemporary developments in computer architecture, in particular multi- and many-core computers and graphics processing units, have made such computations feasible. However, the zeal to take advantage to parallel architectures has typically caused another important aspect of numerical methods for the solution of differential equations to be overlooked, namely the convergence order. It is well known that higher-order methods are generally more efficient than lower-order ones when solutions are smooth and relatively high accuracy is desired. In these situations, serial implementations of high-order methods may remain surprisingly competitive with parallel implementations of low-order methods. In this paper, we examine the effect of order on the numerical solution of the bidomain equations in parallel. We find that high-order methods, in particular high-order time-integration methods with relatively better stability properties, tend to outperform their low-order counterparts, even when the latter are run in parallel. In other words, increasing integration order often trumps increasing available computational resources, especially when relatively high accuracy is desired.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".