clrng: a tool set for parallel random number generation on GPUs in R
Bibliographic record
Abstract
{A novel GPU-accelerated package is proposed to enable efficient parallel random number generation in R, significantly improving performance for large-scale statistical simulations.}(i) Context: Parallel processing with Graphics Processing Units (GPUs) can speed up computationally intensive tasks, which when combined with R, it can largely improve R's limitations in terms of speed, memory usage and single-threaded computation. (ii) Problem: Despite the importance of random number generation for simulation-based statistical inference and modelling, there is currently no R package that supports reproducible, GPU-based parallel random number generation. (iii) Solution: To fill this gap, we introduce the R package clrng, which integrates the OpenCL clRNG library with the gpuR package to enable efficient parallel random number generation on GPUs. (iv) Results: clrng enables reproducible research by setting random initial seeds for streams on both GPU and CPU, thereby accelerating the performance of several types of statistical simulation and modelling. The random number generator in clrng guarantees independent parallel samples even in interactive, ad-hoc R sessions (e.g. interrupted and resumed). This package is portable and flexible, allowing developers to embed its random number generation kernel into a wide range of statistical applications.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.039 |
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".