Bibliographic record
Abstract
An important class of computer software, such as network servers, exhibits concurrency through many loosely coupled and potentially long-running communication sessions. For these applications, a long-standing open question is whether thread-per-session programming can deliver comparable performance to event-driven programming. This paper clearly demonstrates, for the first time, that it is possible to employ user-level threading for building thread-per-session applications without compromising functionality, efficiency, performance, or scalability. We present the design and implementation of a general-purpose, yet nimble, user-level M:N threading runtime that is built from scratch to accomplish these objectives. Its key components are efficient and effective load balancing and user-level I/O blocking. While no other runtime exists with comparable characteristics, an important fundamental finding of this work is that building this runtime does not require particularly intricate data structures or algorithms. The runtime is thus a straightforward existence proof for user-level threading without performance compromises and can serve as a reference platform for future research. It is evaluated in comparison to event-driven software, system-level threading, and several other user-level threading runtimes. An experimental evaluation is conducted using benchmark programs, as well as the popular Memcached application. We demonstrate that our user-level runtime outperforms other threading runtimes and enables thread-per-session programming at high levels of concurrency and hardware parallelism without sacrificing performance.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".