Impact of communication volume on the maximum speedup in Parallel computing based on graph partitioning
Bibliographic record
Abstract
In the field of parallel computing, load balancing of parallel simulations is considered as a fundamental problem. The primary goal is to evenly distribute computing load across multiple processors while minimizing processor-to-processor communication. A prevalent approach to solving the problem of balancing is based on a graph model. To balance the load between k processors, we partition the graph into k parts, each one is assigned to a processor. This problem is difficult to solve, but effective heuristics have been proposed, including those based on a multilevel approach, used by most graph partitioning tools. All partitioning methods are only relevant if the number of processors is fixed. It is essential to choose the right number of processors for a simulation to get good performance and more efficiency.In this paper, we test several graphs partitioning using the metis framework then we try to find the optimal number of processors, named in this paper p* to execute a simulation and the max speed up that we can reach using parallelization. We also investigate the intrinsic graph characteristics that affect these parameters.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".