Cache prefetching and speculation on multi-threaded processors
Bibliographic record
Abstract
Data prefetching is an important mechanism for hiding memory latency in single-threaded, desktop workloads. For multi-threaded, commercial workloads, prefetching offers much more modest improvements in performance at a high cost in cache power and bandwidth to the higher level caches. This paper shows that by combining speculation with a selective prefetching scheme, we can reduce the cache access power overhead while improving performance. We demonstrate that “likely-to-miss” load instructions can be accurately identified and we propose two hardware-based techniques for improving load latencies in multi-threaded commercial workloads. First, we modify a next-four-lines prefetching scheme to only perform the prefetch for likely-to-miss loads. Second, we forward addresses for likely-to-miss loads to the L2 and L3 caches for tag look-up immediately after address translation. Combined, these two techniques reduce the extra cache access power of the L3 cache by up to 53% while slightly improving performance when compared with a simple next-four-lines prefetcher running standard, commercial-workload benchmarks.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".