Parallel Graph Partitioning on a CPU-GPU Architecture
Bibliographic record
Abstract
Graph partitioning has important applications in multiple areas of computing, including scheduling, social networks, and parallel processing. In recent years, GPUs have proven successful at accelerating several graph algorithms. However, the irregular nature of the real-world graphs poses a problem for GPUs, which favor regularity. In this paper, we discuss the design and implementation of a parallel multilevel graph partitioner for a CPU-GPU system. The partitioner aims to overcome some of the challenges arising due to memory constraints on GPUs and maximizes the utilization of GPU threads through suitable load-balancing schemes. We present a lock-free shared-memory scheme since fine-grained synchronization among thousands of threads imposes too high a performance overhead. The partitioner, implemented in CUDA, outperforms serial Metisand parallel MPI-based ParMetis. It performs similar to theshared-memory CPU-based parallel graph partitioner mt-metis.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".