Optimizing the Cache Performance of Non-Numeric Applications
Bibliographic record
Abstract
Optimizing the Cache Performance of Non-Numeric Applications Chi-Keung Luk Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2000 The latency of accessing instructions and data from the memory subsystem is an increasingly crucial performance bottleneck in modern computer systems. While cache hierarchies are an important first step, they alone cannot solve the problem. Further, though a variety of latency-hiding techniques have been proposed, their success has been largely limited to regular, numeric applications. Few promising latency-hiding techniques that can handle irregular, non-numeric codes have been proposed, in spite of the popularity of such codes in computer applications. This dissertation investigates hardware and software techniques for coping with the instruction-access latency and data-access latency in non-numeric applications. To deal with instruction-access latency, we propose cooperative instruction prefetching, a novel technique which significantly outperforms state-of-the-art instruction prefetching schemes by being able to prefetch more aggressively and much further ahead of time while at the same time substantially reducing the amount of useless prefetches. To cope with data-access latency, we investigate three complementary techniques. First, we study how to use compiler-inserted data prefetching to tolerate the latency of accessing pointer-based data structures. To schedule prefetches early enough, we design three prefetching schemes to overcome the pointer-chasing problem associated with these data structures, and we automate them in an optimizing research compiler. Second, we study how to safely perform an important class of locality optimizations, namely dynamic iii data layout optimizations, in non-n...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".