Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.002 | 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