A Recursive Partitioning Approach to Improving Hypergraph Partitioning
Bibliographic record
Abstract
Balanced hypergraph partitioning (BHP) is a fundamental combinatorial optimization problem in application specific integrated circuit (ASIC) and field-programmable gate array (FPGA) design flows. Multi-level partitioners (MLP) comprise a set of heuristics and are a popular way to solve BHP problems. The BHP problem becomes more complex when the number of partitions (K) is large. Recursive bi-partitioning is a traditional way to solve the problem for larger values of K. However, recursive bi-partitioning is usable for only a smaller number of partitions. This work proposes to generalize recursive bi-partitioning to recursive K-way partitioning to make recursive partitioning usable for all non-prime values of K. The practical usefulness of the proposed recursive method was evaluated by enhancing a recent MLP, TritonPart, and assessed the performance using Titan23 benchmarks. The proposed recursive method helped TritonPart to successfully solve problems with larger K values than it could solve without using the proposed method. Secondly, it also improved the runtime of TritonPart. A comparison with MT-KaHyPar showed it can find solutions with 80% lower cutsize. In short, the proposed recursive method is a useful way to enhance the capability of existing MLPs in terms of their runtime and ability to solve problems of larger K values.
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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".