Parallelization of the K-Means++ Clustering Algorithm
Bibliographic record
Abstract
K-means++ is the clustering algorithm that is created to improve the process of getting initial clusters in the K-means algorithm. The k-means++ algorithm selects initial k-centroids arbitrarily dependent on a probability that is proportional to each data-point distance to the existing centroids. The most noteworthy problem of this algorithm is when running happens in sequential mode, as this reduces the speed of clustering. In this paper, we develop a new parallel k-means++ algorithm using the graphics processing units (GPU) where the Open Computing Language (OpenCL) platform is used as the programming environment to perform the data assignment phase in parallel while the Streaming SIMD Extension (SSE) technology is used to perform the initialization step to select the initial centroids in parallel on CPU. The focus is on optimizations directly targeted to this architecture to exploit the most of the available computing capabilities. Our objective is to minimize runtime while keeping the quality of the serial implementation. Our outcomes demonstrate that the implementation of targeting hybrid parallel architectures (CPU & GPU) is the most appropriate for large data. We have been able to achieve a 152 times higher throughput than that of the sequential implementation of k-means ++.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".