INRFlow: An interconnection networks research flow-level simulation framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents INRFlow, a mature, frugal, flow-level simulation framework for modelling large-scale networks and computing systems. INRFlow is designed to carry out performance-related studies of interconnection networks for both high performance computing systems and datacentres. It features a completely modular design in which adding new topologies, routings or traffic models requires minimum effort. Moreover, INRFlow includes two different simulation engines: a static engine that is able to scale to tens of millions of nodes and a dynamic one that captures temporal and causal relationships to provide more realistic simulations. We will describe the main aspects of the simulator, including system models, traffic models and the large variety of topologies and routings implemented so far. We conclude the paper with a case study that analyses the scalability of several typical topologies. INRFlow has been used to conduct a variety of studies including evaluation of novel topologies and routings (both in the context of graph theory and optimization), analysis of storage and bandwidth allocation strategies and understanding of interferences between application and storage traffic. • We present our flow-level simulation framework INRFlow. • It is a mature, flexible and efficient tool for simulating large scale systems. • It models network, storage, scheduler and applications. • It has been used extensively for our research in the past. • INRFlow is open source and programmed in C.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it