Performance study of mapping irregular computations on GPUs
Why this work is in the frame
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Bibliographic record
Abstract
Recently, Graphical Processing Units (GPUs) have become increasingly more capable and well-suited to general purpose applications. As a result of the GPUs high degree of parallelism and computational power, there has been a great deal of interest directed toward the platform for parallel application development. Much of the focus, however, has been on very regular applications that exhibit a high degree of data parallelism, as these applications map well to the GPU. Irregular applications, such as the Breadth First Search discussed in this paper, have not been as extensively studied and are more difficult to implement in an efficient fashion on the GPU. We will present both an implementation of the Breadth First Search algorithm as well as that of a Matrix Parenthesization algorithm. These pair of algorithms showcase similar synchronization behavior when implemented on a GPU using CUDA, enabling a more direct comparison between them. The results obtained can be used to showcase some of the synchronization issues present with irregular algorithms on the GPU.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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