10-Gigabit iWARP Ethernet: Comparative Performance Analysis with InfiniBand and Myrinet-10G
Bibliographic record
Abstract
iWARP is a set of standards enabling remote direct memory access (RDMA) over Ethernet. iWARP supporting RDMA and OS bypass, coupled with TCP/IP offload engines, can fully eliminate the host CPU involvement in an Ethernet environment. With the iWARP standard and the introduction of 10-Gigabit Ethernet, there is now an alternative path to the proprietary interconnects for high-performance computing, while maintaining compatibility with existing Ethernet infrastructure and protocols. Recently, NetEffect Inc. has introduced an iWARP-enabled 10-Gigabit Ethernet channel adapter. In this paper we assess the potential of such an interconnect for high-performance computing by comparing its performance with two leading cluster interconnects, infiniband and myrinet-10G. The results show that the NetEffect iWARP implementation achieves an unprecedented latency for Ethernet, and saturates 87% of the available bandwidth. It also scales better with multiple connections. At the MPI level, iWARP performs better than infiniband in queue usage and buffer re-use.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".