Optically interconnected high-performance servers
Bibliographic record
Abstract
ABSTRACT In this project the viability of an optically-enhanced chassis providing u 10nGbit/s bandwidth for both point-to-point and broadcast communication between servers is determined. Keywords: High performance computers, medium access layer, optical backplane, optical interconnection 1. INTRODUCTION High performance computing latforms such as data centerp s for Internet search engines and supercomputers for climate modeling are expected to support increasing bandwidth as the speed of multi-core processors increases [1, 2]. However, the full computational potential of processors has become difficult to achieve due to larger amount of data to transfer between processors in a computer cluster. The project in collaboration with Reflex Photonics aim at specifically addressing the density and power dissipation issues regarding the next generation of high-performance computing platforms. The primary objective is to develop a power-efficient interconnection architecture with optically enhanced backplane that uses LightABLE parallel optical engine prototypes [3]. This parallelism requires also some modifications in the implementation of the Media Access Control (MAC) layer in the transmitter and the receiver sides. The project enablesto investigate and compare the energy efficiency of optical interconnection for different applications running on the server.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".