Performance of MC2 and the ECMWF IFS Forecast Model on the Fujitsu VPP700 and NEC SX‐4M
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
The NEC SX‐4M cluster and Fujitsu VPP700 supercomputers are both based on custom vector processors using low‐power CMOS technology. Their basic architectures and programming models are however somewhat different. A multi‐node SX‐4M cluster contains up to 32 processors per shared memory node, with a maximum of 16 nodes connected via the proprietary NEC IXS fibre channel crossbar network. A hybrid combination of inter‐node MPI message‐passing with intra‐node tasking or threads is possible. The Fujitsu VPP700 is a fully distributed‐memory vector machine with a crossbar interconnect which also supports MPI. The parallel performance of the MC2 model for high‐resolution mesoscale forecasting over large domains and of the IFS RAPS 4.0 benchmark are presented for several different machine configurations. These include an SX‐4/32, an SX‐4/32M cluster and up to 100 PE′s of the VPP700. Our results indicate that performance degradation for both models on a single SX‐4 node is primarily due to memory contention within the internal crossbar switch. Multinode SX‐4 performance is slightly better than single node. Longer vector lengths and SDRAM memory on the VPP700 result in lower per processor execution rates. Both models achieve close to ideal scaling on the VPP700.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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