Vectorising k-Core Decomposition for GPU Acceleration
Why this work is in the frame
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Bibliographic record
Abstract
k-Core decomposition is a well-studied community detection problem in graph analytics in which each k-core of vertices induces a subgraph where all vertices have degree at least k. The decomposition is expensive to compute on large graphs and efforts to apply massive parallelism have had limited success. This paper presents a vectorisation of the problem that reframes it as a composition of vector primitives on flat, 1d arrays. With such a formulation, we can deploy highly optimised Deep Learning GPU and SIMD frameworks. On a moderate GPU, using PyTorch, we obtain up to 8 × improvement over the best parallel state-of-the-art implemented in C++ and running on an expensive 32-core machine. More importantly, our approach represents a novel abstraction showing that redesigning graph operations as a series of vectorised primitives makes highly-parallel analytics both easier and more accessible for developers. We posit that such an approach can vastly accelerate the use of cheap GPU hardware in complex graph analytics.
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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 | 0.000 | 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