Bibliographic record
Abstract
Memory can consume a substantial amount of power in database servers, yet memory power has received considerably less attention than CPU power. Memory power consumption is also highly non-proportional. Thus, memory power becomes even more significant in the common case in which a database server is either not completely busy or not completely full. In this paper, we study the application of two memory power optimization techniques - rank-aware allocation and rate-based layout - to database systems. By concentrating memory load, rather than spreading it out evenly, these techniques create and exploit memory idleness to achieve power savings. We have implemented these techniques in a prototype database system called DimmStore. DimmStore is part of a memory power testbed which includes customized hardware with direct power measurement capabilities, allowing us to measure the techniques' effectiveness. We use the testbed to empirically characterize the power saving opportunities provided by these techniques, as well as their performance impact, under YCSB and TPC-C workloads. Under simple YCSB workloads, power savings ranged up to 50%, depending on load and space utilization, with little performance impact. Savings were smaller, but still significant, for TPC-C, which has more complex data locality characteristics.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.137 | 0.042 |
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".