Thread and Memory Placement on NUMA Systems: Asymmetry Matters.
Bibliographic record
Abstract
It is well known that the placement of threads and memory plays a crucial role for performance on NUMA (Non-Uniform Memory-Access) systems. The conventional wisdom is to place threads close to their memory, to collocate on the same node threads that share data, and to segregate on different nodes threads that compete for memory bandwidth or cache resources. While many studies addressed thread and data placement, none of them considered a crucial property of modern NUMA systems that is likely to prevail in the future: asymmetric interconnect. When the nodes are connected by links of different bandwidth, we must consider not only whether the threads and data are placed on the same or different nodes, but how these nodes are connected. We study the effects of asymmetry on a widely available ×86 system and find that performance can vary by more than 2× under the same distribution of thread and data across the nodes but different inter-node connectivity. The key new insight is that the best-performing connectivity is the one with the greatest total bandwidth as opposed to the smallest number of hops. Based on our findings we designed and implemented a dynamic thread and memory placement algorithm in Linux that delivers similar or better performance than the best static placement and up to 218% better performance than when the placement is chosen randomly.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".