Measuring the Performance of Parallel Information Processing in Solving Linear Equation Using Multiprocessor Supercomputer
Bibliographic record
Abstract
Evaluation the performance of the algorithms and the method that is used to implement it play a major role in the assessment of the performance of many applications and it help the researchers to decide which algorithm to use and which method to implement it, it also give indicate of the performance of the hardware that the algorithm is tested over. In this paper we evaluate the performance of solving linear equation application over supercomputer which was implemented and using Message Passing interface (MPI) library. The sequential and multithreaded algorithm for solving linear equations has been experimented too and the results has been recorded, the speedup and efficiency of the algorithm has been calculated and the results showed that the parallel algorithm outperforms other methods with the large size matrix of 8192 * 8192 over the number of processors of 64. For large input size, the results also showed that there is a noticeable decrease in running time as the number of processors increase. But in case of multithreaded the results showed that as the matrix size increase the time required for running the algorithm is rapidly increasing although the number of threads increased. This indicates that the parallel performance over for large matrix input size is better and outperforms other methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".