Bibliographic record
Abstract
While it is well known that network coding achieves optimal flow rates in multicast sessions, its potential for practical use has remained to be a question, due to its high computational complexity. With GPU computing gaining momentum as a result of increased hardware capabilities and improved programmability, we show in this paper how the GPU can be used to improve network coding performance dramatically. Our previous work presented the first attempt in the literature to maximize the performance of network coding by taking advantage of not only multi-core CPUs, but also hundreds of computing cores in commodity off-the-shelf Graphics Processing Units (GPU). This paper represents another step forward, and presents a new array of GPU-based algorithms that improve network encoding by a factor of 2.2, and network decoding by a factor of 2.7 to 27.6 across a range of practical configurations. With just a single NVIDIA GTX 280 GPU, our implementation of GPU-based network encoding outperforms an 8-core Intel Xeon server by a margin of at least 4.3 to 1 in all practical test cases, and over 3000 peers can be served at high-quality video rates if network coding is used in a streaming server. With 128 blocks, for example, coding rates up to 294 MB/second can be achieved with a variety of block sizes.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".