Bibliographic record
Abstract
Fundamental limitations of traditional data center network architectures have led to the development of architectures that provide enormous bisection bandwidth for up to hundreds of thousands of servers. Because these architectures rely on homogeneous switches, implementing one in a legacy data center usually requires replacing most existing switches. Such forklift upgrades are typically prohibitively expensive; instead, a data center manager should be able to selectively add switches to boost bisection bandwidth. Doing so adds heterogeneity to the network's switches and heterogeneous high-performance interconnection topologies are not well understood. Therefore, we develop the theory of heterogeneous Clos networks. We show that our construction needs only as much link capacity as the classic Clos network to route the same traffic matrices and this bound is the optimal. Placing additional equipment in a highly constrained data center is challenging in practice, however. We propose LEGUP to design the topology and physical arrangement of such network upgrades or expansions. Compared to current solutions, we show that LEGUP finds network upgrades with more bisection bandwidth for half the cost. And when expanding a data center iteratively, LEGUP's network has 265% more bisection bandwidth than an iteratively upgraded fat-tree.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.115 | 0.045 |
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 source (direct Gemma or distilled Codex), 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".