Bibliographic record
Abstract
Network function virtualization (NFV) [50] is increasingly used to implement network operations traditionally implemented in customized ASICs. NFV employs commodity, general-purpose computer hardware located in a datacenter. General-purpose computing hardware has performance limitations limiting the scope of NFV. An emerging solution is to leverage programmable accelerators such as GPUs and FPGAs for NFV. However, traditional computer architecture research of NFV applications is challenging, due to the lack of NFV benchmark suites. This dissertation presents a set of candidates for NFV benchmark suite, based on analysis of most common NFV application. We then study NFV acceleration on GPUs. We identify overheads that are especially pronounced in Service Function Chains (SFC) that are common in NFV. This dissertation proposes GPUChain, a mechanism to enhance SFC acceleration on GPUs by moving the chaining capability onto the GPU. GPUChain avoids the significant latency overheads incurred by a CPU-centric SFC execution model. GPUChain achieves an average latency reduction of 44% and improves throughput by 168% versus a GPU supporting pre-registered kernels that are triggered to launch by an external device [110] while incurring only 0.007% area overhead.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".