A High-Efficiency Parallel Mechanism for Canonical Polyadic Decomposition on Heterogeneous Computing Platform
Bibliographic record
Abstract
Canonical Polyadic decomposition (CPD) obtains the low-rank approximation for high-order multidimensional tensors through the summation of a sequence of rank-one tensors, greatly reducing storage and computation overhead. It is increasingly being used in the lightweight design of artificial intelligence and big data processing. The existing CPD technology exhibits inherent limitations in simultaneously achieving high accuracy and high efficiency. In this paper, a heterogeneous computing method for CPD is proposed to optimize computing efficiency with guaranteed convergence accuracy. Specifically, a quasi-convex decomposition loss function is constructed and the extreme points of the Kruskal matrix rows have been solved. Further, the massively parallelized operators in the algorithm are extracted, a software-hardware integrated scheduling method is designed, and the deployment of CPD on heterogeneous computing platforms is achieved. Finally, the memory access strategy is optimized to improve memory access efficiency. We tested the algorithm on real-world and synthetic sparse tensor datasets, numerical experimental results show that compared with the state-of-the-art method, the proposed method has a higher convergence accuracy and computing efficiency. Compared to the standard CPD parallel library, the method achieves efficiency improvements of tens to hundreds of times while maintaining the same accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".