GPU-BASED PARALLEL ALGORITHM OF INTERACTION INDUCED LIGHT SCATTERING SIMULATIONS IN FLUIDS
Bibliographic record
Abstract
We parallelized the sequential algorithm of the four-body correlation function if each combination of two pairs (𝑖,𝑗) and (𝑘,𝑙) was averaged over the time in a separate calculation thread. The generator of pairs used as the input for this algorithm was also parallelized and connected with the 4-body correlation function calculations. We used our algorithm to accelerate extremely intensive calculations of the 4-body polarizability anisotropy correlation functions, which were very important to estimate the interaction induced light scattering spectrum. The resulting C code was used to test our algorithm on Graphics Processing Units (GPUs) with the Compute Unified Device Architecture (CUDA) technology from NVIDIA® Corporation. As a result, we achieved 12 times the acceleration of the 4-body correlation function calculations in comparison to the Central Processing Unit (CPU) core. The peak performance of the GPU calculations was registered at the level of 19 times faster than the CPU core. We also found that acceleration depended on the memory consumption. In the single precision mode, the relative error between the CPU and GPU calculations was found to be within 0.1%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".