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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".