Bibliographic record
Abstract
Most processors employ hardware data prefetching to hide memory access latencies. However the prefetching requests from different threads on a multi-core processor can cause severe interference with prefetching and/or demand requests of others. The data prefetching can lead to significant performance degradation due to shared resource contention on shared memory multi-core systems. This paper proposes a thread-aware data prefetching mechanism based on low-overhead run-time information to tune prefetching modes and aggressiveness, mitigating the resource contention in the memory system. Our solution has two new components: 1) a filtering mechanism that informs the hardware about which prefetching requests can cause shared data invalidation and should be discarded, and 2) a self-tuning prefetcher that uses run-time feedback to adjust each thread's data prefetching mode and arguments. On a set of parallel benchmarks, our thread-aware data prefetching mechanisms improve the overall performance of 64-core system by 11% and reduce the energy-delay product by 13% over a multi-mode prefetch baseline system with a two level cache organization and a conventional MESI-based directory coherence protocol. We compare our approach to the feedback directed prefetching (FDP) technique and find that it provides better performance on multi-core systems, while reducing the energy delay product.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".