Intelligent prefetching and caching for scientific data mining in the middleware GAMine.
Bibliographic record
Abstract
Scientific data mining applications are widespread in different scientific fields. They are composed of huge datasets, complicated algorithms and often deployed on high performance parallel platforms. Especially, the increasingly large-scale data sets cause the data access to be the most time-consuming stage of the overall execution time. Caching and prefetching can be used to enhance the efficiency of data access to improve the applications' performance. Traditional OS's file system's caching and prefetching strategies as well other enhanced approaches ignore the applications' runtime situation. As a result, not all data retrieval latency can be hidden, or cache units have to be larger if data access is remote. The first step of our approach is to build a middleware---GAMine, which is independent of data sets and applications and provides a generic data access optimization strategy for scientific data mining applications. It supports both client/server and peer-to-peer architectures, and has a flexible, symmetric design. Secondly, within our GAMine, the prefetching strategy exploits the knowledge of access patterns and system parameters (latency and throughput) to set the preferred prefetch depth. In addition, GAMine can be told to select different caching policies according to different access patterns and architectures. As a result, the middleware can hide more latency and avoid cache pollution. Finally, GAMine can monitor the data consumption rate and the data delivery rate to set the prefetch depth dynamically to the optimal value as regards latency hiding and the cache size. Thus even in the dynamic situation, the latency can still be hidden at anytime due to the middleware's adaptation. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .H8. Source: Masters Abstracts International, Volume: 44-01, page: 0393. Thesis (M.Sc.)--University of Windsor (Canada), 2005.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".