Memory page placement based on predicted cache behaviour in CC-NUMA multiprocessors
Bibliographic record
Abstract
An important characteristic of CC-NUMA multiprocessors is the relative difference in latency between local and remote memory accesses. For many applications running on these systems, the amount of time spent stalled on remote memory accesses can make up a significant fraction of the total execution time. Previous work has shown that proper placement of pages in memory can reduce much of this time by changing remote memory accesses to local memory accesses. This work has also shown that such placement decisions are most effective when they are based on the caching behaviour of those pages. In this thesis, we present a new method of predicting such caching behaviour at allocation time, and making appropriate placement decisions based on these predictions. This method required minimal additions to the memory subsystem of the University of Toronto Tornado operating system, and no special hardware for monitoring the memory hierarchy. We also show that this method can result in improvements of up to 35% in total execution time over traditional placement policies such as first-touch placement when the data sets of the applications being run exceeds the size of a local memory node. These results hold for both single application and multiprogrammed workloads.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".