Identifying opportunities for automatic remote field cloning
Bibliographic record
Abstract
Optimizing compilers can automatically re-arrange data objects to produce memory reference patterns with better reference locality while retaining correct application semantics. This paper describes a new technique, Remote Field Cloning (RFC), that consists of replicating the value from a field of a remote data object. This replication prevents long jumps in the data references of an application. RFC was motivated by work on cache-conscious re-engineering of algorithms. When hand-crafted into suitable algorithms, RFC produces speedups of up to 40% for a well-know refinement-based pathfinding problem. This paper presents a motivating example and a general problem statement of remote field cloning as an optimization problem. A new algorithm that discovers opportunities for RFC is applied to the SPEC2000 benchmark suite and discovers many such opportunities. However a correlation of the percentage of execution time spent in procedures that contain these opportunities reveals that the expected impact of RFC in the SPEC2000 suite is limited.
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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 | 0.003 |
| 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 | 0.000 |
| 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".