Ant Brood Clustering on Intel Xeon Multi-core: Challenges and Strategies
Bibliographic record
Abstract
Swarm intelligence algorithms such as ant colony optimization (ACO) and particle swarm optimization (PSO) are computationally intensive. Ant Brood Clustering (ABC) algorithm is one of the techniques in the family of ant algorithms. It is based on how ants cluster their brood or corpse into different piles. ABC has been efficiently used in solving the clustering problem in data analytics. However, they are computationally intensive. In this paper, we explore and evaluate eight parallel strategies of the ant brood clustering algorithm on Intel Xeon multi-core shared memory machine exploiting algorithm level and program level parallelism. A speedup of 25.3x is achieved with 56 hardware threads on dual 14 core Intel Xeon shared memory machine with coarse-grained, data level protection strategy. We show that contrary to other swarm intelligence techniques, fine grained parallelism is not suitable for this algorithm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".