Bibliographic record
Abstract
This article presents Forma , a practical, safe, and automatic data reshaping framework that reorganizes arrays to improve data locality. Forma splits large aggregated data-types into smaller ones to improve data locality. Arrays of these large data types are then replaced by multiple arrays of the smaller types. These new arrays form natural data streams that have smaller memory footprints, better locality, and are more suitable for hardware stream prefetching. Forma consists of a field-sensitive alias analyzer, a data type checker, a portable structure reshaping planner, and an array reshaper. An extensive experimental study compares different data reshaping strategies in two dimensions: (1) how the data structure is split into smaller ones ( maximal partition × frequency-based partition × affinity-based partition ); and (2) how partitioned arrays are linked to preserve program semantics ( address arithmetic-based reshaping × pointer-based reshaping ). This study exposes important characteristics of array reshaping. First, a practical data reshaper needs not only an inter-procedural analysis but also a data-type checker to make sure that array reshaping is safe. Second, the performance improvement due to array reshaping can be dramatic: standard benchmarks can run up to 2.1 times faster after array reshaping. Array reshaping may also result in some performance degradation for certain benchmarks. An extensive micro-architecture-level performance study identifies the causes for this degradation. Third, the seemingly naive maximal partition achieves best or close-to-best performance in the benchmarks studied. This article presents an analysis that explains this surprising result. Finally, address-arithmetic-based reshaping always performs better than its pointer-based counterpart.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.026 |
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".