Graph Coloring on the GPU and Some Techniques to Improve Load Imbalance
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Bibliographic record
Abstract
Graphics processing units (GPUs) have been increasingly used to accelerate irregular applications such as graph and sparse-matrix computation. Graph coloring is a key building block for many graph applications. The first step of many graph applications is graph coloring/partitioning to obtain sets of independent vertices for subsequent parallel computations. However, parallelization and optimization of coloring for GPUs have been a challenge for programmers. This paper studies approaches to implementing graph coloring on a GPU and characterizes their program behaviors with different graph structures. We also investigate load imbalance, which can be the main cause for performance bottlenecks. We evaluate the effectiveness of different optimization techniques, including the use of work stealing and the design of a hybrid algorithm. We are able to improve graph coloring performance by approximately 25% compared to a baseline GPU implementation on an AMD Radeon HD 7950 GPU. We also analyze some important factors affecting performance.
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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 | 0.001 | 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