Kernel- vs. User-Level Networking: A Ballad of Interrupts and How to Mitigate Them
Bibliographic record
Abstract
Networking performance has become especially important in the current age with growing demands on services over the Internet. Recent advances in network controllers has exposed bottlenecks in various parts of network processing. User-level networking, which bypasses the operating system's network stack and replaces it with one re-implemented in the userspace, is often framed as a silver bullet to mitigate any performance issues arising in the kernel network stack. However, there is often no comprehensive study on where this performance increase ultimately comes from. \n \nThis work aims to explore potential areas from which improvements in overall performance can arise. Most importantly, it is identified that asynchronous interrupts and their handling is a major source of overhead associated with the kernel network stack. Several proposals are presented with the goal of reducing the need for interrupts in the kernel network stack, simulating the execution model of user-level networking. It is shown that a small kernel modification with around 30 lines of code change results in a substantial performance increase without the need to replace the kernel network stack in its entirety.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".