A Fast, Single-Instruction–Multiple-Data, Scalable Priority Queue
Bibliographic record
Abstract
In this paper, we address a key challenge in designing flow-based traffic managers (TMs) for next-generation networks. One key functionality of a TM is to schedule the departure of packets on egress ports. This scheduling ensures that packets are sent in a way that meets the allowed bandwidth quotas for each flow. A TM handles policing, shaping, scheduling, and queuing. The latter is a core function in traffic management and is a bottleneck in the context of high-speed network devices. Aiming at high throughput and low latency, we propose a single-instruction-multiple-data (SIMD) hardware priority queue (PQ) to sort out packets in real time, supporting independently the three basic operations of enqueuing, dequeuing, and replacing in a single clock cycle. A proof of validity of the proposed hardware PQ data structure is presented. The implemented PQ architecture is coded in C++. Vivado high-level synthesis is used to generate synthesizable register transfer logic from the C++ model. This implementation on a ZC706 field-programmable gate array (FPGA) shows the scalability of the proposed solution for various queue depths with almost constant performance. It offers a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> throughput improvement when compared to prior works, and it supports links operating at 100 Gb/s.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".