DASS: Dynamic Adaptive Sub-Target Specialization
Bibliographic record
Abstract
A new microprocessor within a given processor architecture may introduce performance-improving features that either can only be accessed through novel instructions or require new code-generation techniques to be beneficial. In response, compilers must be extend-ed/improved to make use of these new instructions and to generate better schedules for the new hardware. The compiler improvements that enable these specializations can take significant time to develop, thus applications compiled Ahead-Of-Time (AOT) will often not benefit from code specialization without later recompilation. Furthermore, code compiled for a specific hardware sub-target lacks performance portability, thus, for better performance, there is a need to maintain multiple builds for each processor architecture leading to significant development and maintenance costs. This paper demonstrates that such challenges can be overcome by applying code specialization only to a small percentage of the code in a program. Moreover, it proposes DASS, a novel Dynamic Adaptive Sub-Target Specialization technique to recompile selected parts of a program at runtime. Empirical evidence indicates that selective specialization can achieve up to 93% of whole-program specialization speedup by statically specializing less than 1.5% of the application code. Furthermore, DASS can dynamically achieve performance close to that of static specialization, reaching up to 83% of statically attainable speedup while performing recompilation and redirection during execution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".