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 trend towards increasing number of processor cores in future chip architectures, scalable directory-based protocols for maintaining cache coherence will be needed. However, directory-based protocols face well-known problems in delay and scalability. Most current protocol optimizations targeting these problems maintain a firm abstraction of the interconnection network fabric as a communication medium: protocol optimizations consist of end-to-end messages between requestor, directory and sharer nodes, while network optimizations separately target lowering communication latency for coherence messages. In this paper, we propose an implementation of the cache coherence protocol within the network, embedding directories within each router node that manage and steer requests towards nearby data copies, enabling in-transit optimization of memory access delay. Simulation results across a range of SPLASH-2 benchmarks demonstrate significant performance improvement and good system scalability, with up to 44.5% and 56% savings in average memory access latency for 16 and 64-node systems, respectively, when compared against the baseline directory cache coherence protocol. Detailed micro architecture and implementation characterization affirms the low area and delay impact of in-network coherence
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 | 0.000 | 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 it