Region-Based Data Layout via Data Reuse Analysis
Bibliographic record
Abstract
Data-structure splicing techniques, such as structure splitting, field reordering, and pointer inlining reorganize data structures to improve cache and translation look-aside buffer (TLB) utilization. Structure types are typically transformed globally in the program, requiring updates to all references to elements of a transformed type. These techniques often rely on instrumentation, tracing, or sampling to create models that guide their transformations. Furthermore, compilers often cannot prove that their transformations are legal and must rely on manual inspection and manual transformation. Applying data-layout transformations locally -- as opposed to globally -- to regions of code removes the need for expensive profiling and simplifies legality verification. This work introduces RebaseDL, a static analysis that finds profitable and legal region-based data layout transformation opportunities that improve access locality. These opportunities are found within code regions that exhibit data reuse. Going beyond structure splicing, RebaseDL also identifies transformation opportunities that do not involve structure types, that is, it identifies data packing transformations. The analysis is implemented in LLVM and it detects multiple transformation opportunities within the SPEC CPU benchmark suite, where the transformation obtains speedups of up to 1.34x for transformed regions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.012 | 0.006 |
| 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".