Photonic Interconnects for Exascale and Datacenter Architectures
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
Exascale and datacenter systems require terabits per second of internode communication bandwidth to meet the performance demands of high-performance computing applications. High-radix routers combined with scalable dragonfly topology have been proposed to reduce execution time and improve power dissipation. Although the dragonfly network has low diameter for exascale networks, fewer global links reduce the bisection bandwidth and require adaptive routing to prevent hot spots due to congestion. Moreover, the number of ports in a high-radix router affects the router cost when implemented with alternate emerging technologies. In this article, the authors advocate multitier network topologies that combine scalable topologies for local (intracabinet) and global (intercabinet) interconnects such as the k-ary n-cube, the flattened butterfly, and the dragonfly, to lead to improved bisection, manageable radix, and reduced link costs, albeit at higher packet latency owing to increased diameter. Because the performance per watt delivered by metallic interconnects or coaxial cables significantly exceeds the available power budget, we envision an entire exascale network composed of photonic links for communication and CMOS routers for switching. Results indicate that multitier topologies are comparable to the single-level dragonfly topology in terms of power and latency while providing higher bisection and reduced area overhead.
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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