Strong Scaling of The SVD Algorithm For HPC Science: A Petsc-Based Approach
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Bibliographic record
Abstract
The Singular Value Decomposition (SVD) algorithm is ubiquitous in many fields of science and technology. It may be used embedded into other advanced algorithms, solvers or data processing chains. In those scenarios dealing with large data volumes expressed as a huge matrix, there is a need for parallel SVD versions to process it efficiently. We present some ideas and results obtained within the PETSc framework, which enable to design promising HPC scalable solvers. The focused SVD implementations have been taken from the SLEPc library, which is seamless plugged into PETSc to extend its capabilities. Besides its implementation, there is also a randomized-SVD and some wrappers to interface ScaLAPACK and others packages intended to extract singular triplets. This work assesses the strong scaling behaviour attained with these SVD implementations at extracting the leading singular values of a population of both sparse and dense squared matrices. A comparison of performance is provided.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it