Parallel catastrophe modelling on a cell processor
Bibliographic record
Abstract
In this paper we study the potential performance improvements for catastrophe modelling systems that can be achieved through parallelization on a Cell Processor. We studied and parallelized a critical section of catastrophe modelling, the so called "inner loop", and implemented it on a Cell Processor running on a regular Playstation 3 platform. The Cell Processor is known to be a challenging environment for software development. In particular, the small internal storage available at each SPE of the Cell Processor is a considerable challenge for catastrophe modelling because the catastrophe modelling algorithm requires frequent accesses to large lookup tables. Our parallel solution is a combination of multiple techniques: streaming data to the SPEs and parallelizing inner loop computations, building caches on the SPEs to store parts of the large catastrophe modelling lookup tables, vectorizing the computation on the SPEs, and double-buffering the file I/O. On a (Playstation 3) Cell Processor with six active SPEs and 4-way vectorization on each SPE (implying a maximum theoretical 24x speedup), we were able to measure a sustained 16x speedup for our parallel catastrophe modelling code over a wide range of data sizes for real life Japanese earthquake data.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".