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
With the emergence of many-core architectures, it is quite likely that multiple applications will run concurrently on a system. Existing locally and globally adaptive routing algorithms largely overlook issues associated with workload consolidation. The shortsightedness of locally adaptive routing algorithms limits performance due to poor network congestion avoidance. Globally adaptive routing algorithms attack this issue by introducing a congestion propagation network to obtain network status information beyond neighboring nodes. However, they may suffer from intra- and inter-application interference during output port selection for consolidated workloads, coupling the behavior of otherwise independent applications and negatively affecting performance. To address these two issues, we propose Destination-Based Adaptive Routing (DBAR). We design a novel low-cost congestion propagation network that leverages both local and non-local network information for more accurate congestion estimates. Thus, DBAR offers effective adaptivity for congestion beyond neighboring nodes. More importantly, by integrating the destination into the selection function, DBAR mitigates intra- and inter-application interference and offers dynamic isolation among regions. Experimental results show that DBAR can offer better performance than the best baseline algorithm for all measured configurations; it is well suited for workload consolidation. The wiring overhead of DBAR is low and DBAR provides improvement in the energy-delay product for medium and high injection rates.
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 | 0.003 | 0.001 |
| 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