Comprehensive Analysis of High-Performance Computing Methods for Filtered Back-Projection
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
This paper provides an extensive analysis concerning runtime, accuracy and noise of High-Performance Computing (HPC) frameworks for Computed Tomography (CT) reconstruction tasks: "conventional" multi-core, multi threaded CPUs, the Compute Unified Device Architecture (CUDA) on GPUs, and the graphics pipeline of GPUs as facilitated by the DirectX or OpenGL programming interfaces, exploiting various built-in hardwired features like rasterization and texture filtering. We compare implementations of the Filtered Back-Projection (FBP) algorithm with fan-beam geometry on all these HPC frameworks. Specifically, an ACR-accredited phantom is reconstructed from the raw attenuation data acquired by a clinical CT scanner. Our analysis shows that a single GPU can run the FBP algorithm for reconstructing a 1024 x 1024 image considerably faster than a 64-core, multi-threaded CPU machine. Moreover, employing the graphics pipeline further increases performance as compared to CUDA, albeit with slightly lower accuracy due to "fast math" operations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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