Reducing the harmful effects of last-level cache polluters with an OS-level, software-only pollute buffer
Bibliographic record
Abstract
It is well recognized that LRU cache-line replacement can be ineffective for applications with large working sets or non-localized memory access patterns. Specifically, in last-level processor caches, LRU can cause cache pollution by inserting non-reuseable elements into the cache while evicting reusable ones. The work presented in this paper addresses last-level cache pollution through a dynamic operating system mechanism, called ROCS, requiring no change to underlying hardware and no change to applications. ROCS employs hardware performance counters on a commodity processor to characterize application cache behavior at run-time. Using this online profiling, cache unfriendly pages are dynamically mapped to a pollute buffer in the cache, eliminating competition between reusable and non-reusable cache lines. The operating system implements the pollute buffer through a page-coloring based technique, by dedicating a small slice of the last-level cache to store non-reusable pages. Measurements show that ROCS, implemented in the Linux 2.6.24 kernel and running on a 2.3 GHz PowerPC 970FX, improves performance of memory-intensive SPEC CPU 2000 and NAS benchmarks by up to 34%, and 16% on average.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".