Particle Image Velocimetry Data Processing On A Gpu Cluster
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Particle image velocimetry (PIV) data processing is a computationally expensive process. The immense time taken to analyze data can limit the maximum dataset size. Using graphics processing units (GPUs) has been shown to drastically decrease the processing time for PIV image pairs. The open-source PIV data processing software OpenPIV has been ported to run on a GPU to boost speed and efficiency and has outperformed the CPU version of the software. A multipass method is being implemented in OpenPIV to improve both speed and accuracy. The completed algorithm will be tested on an embedder CPU-GPU device, a desktop computer, and the SOSCIP GPU-accelerated supercomputing cluster. Ultimately, OpenPIV will run on a wide variety of computer platforms an enable larger datasets to be collects, leading to better statistics on the resulting velocity fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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