Research on Parallel Algorithm Optimization Strategies in High Performance Computing
Bibliographic record
Abstract
In the past decade, with the rapid growth of mobile internet, cloud computing, and big data technology, data has shown explosive growth in different fields. In the era of big data, people have more information to utilize, but the difficulty of obtaining effective information is also greater than before. Therefore, it is necessary to study parallel computing models and performance optimization for big data processing. Exploring the value behind big data using data processing techniques has become a current research focus in the field of data. Given the importance of parallel applications of artificial intelligence (AI) and big data, it is crucial to focus on analyzing the High Performance Computing (HPC) that integrates the two. The complexity and diversity of storage structures, computer architecture, as well as the large volume and complex data of big data processing problems, pose significant challenges for the application of high-performance computers in the field of big data processing. Big data not only provides AI with an increasingly rich set of training data, but also puts higher demands on the computing power of computer systems. Faced with the problems of large scale and complex computation of big data, this paper proposes a multi strategy parallel genetic algorithm (GA) based on machine learning (ML) for optimizing HPC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".