Multi-streaming and multi-GPU optimization for a matched pair of Projector and Backprojector
Bibliographic record
Abstract
Iterative reconstruction methods are used in X-ray Computed Tomography in order to improve the quality of reconstruction compared to filtered backprojection methods. However, these methods are computationally expensive due to repeated projection and backprojection operations. Among the possible pairs of projector and backprojector, the Separable Footprint (SF) pair has the advantage to be matched in order to ensure the convergence of the reconstruction algorithm. Nevertheless, this pair implies more computations compared to unmatched pairs commonly used in order to reduce the computation time. In order to speed up this pair, the projector and the backprojector can be parallelized on GPU. Following one of our previous work, in this paper, we propose a new implementation which takes benefits from the factorized calculations of the SF pair in order to increase the number of data handled by each thread. We also describe the adaptation of this implementation for multi-streaming computations. The method is tested on large volumes of size 10243 and 20483 voxels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".