Using idle workstations to implement predictive prefetching
Bibliographic record
Abstract
The benefits of Markov-based predictive prefetching have been largely overshadowed by the overhead required to produce high-quality predictions. While both theoretical and simulation results for prediction algorithms appear promising, substantial limitations exist in practice. This outcome can be partially attributed to the fact that practical implementations ultimately make compromises in order to reduce overhead. These compromises limit the level of algorithm complexity, the variety of access patterns and the granularity of trace data that the implementation supports. This paper describes the design and implementation of GMS-3P (Global Memory System with Parallel Predictive Prefetching), an operating system kernel extension that offloads prediction overhead to idle network nodes. GMS-3P builds on the GMS global memory system, which pages to and from remote workstation memory. In GMS-3P, the target node sends an online trace of an application's page faults to an idle node that is running a Markov-based prediction algorithm. The prediction node then uses GMS to prefetch pages to the target node from the memory of other workstations in the network. Our preliminary results show that predictive prefetching can reduce the remote-memory page fault time by 60% or more and that, by offloading prediction overhead to an idle node, GMS-3P can reduce this improved latency by between 24% and 44%, depending on the Markov model order.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".