Fast on-demand Memory Mapping for Shared Memory and Disaggregated Systems
Bibliographic record
Abstract
Efficient synchronization of memory mapping information is increasingly important as systems evolve toward greater resource disaggregation and heterogeneity. When memory is exported between processes, establishing shared mapping often requires costly page table walk and updates, particularly in fault-driven models. To study these costs, we implement an XPMEM-inspired shared-memory driver and evaluate techniques to reduce mapping overhead. Our approach combines parallel batched on-demand pinning, bypassing unnecessary cache-policy lookups in PFN mapping, and dynamic re-registration to expand registered regions without tearing down existing mappings. In our evaluation, these optimizations reduce cold-start memory copy by up to 13.22 × over XPMEM in multi-process workloads, with particular benefits for collective communication patterns and rapidly resizing buffers. While developed in a shared-memory context, the results highlight general strategies—avoiding redundant translation work, enabling parallel mapping operations, and preserving mapping state—that can inform the design of memory management in disaggregated systems, including GPU disaggregation and heterogeneous memory environments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".