Bibliographic record
Abstract
The emergence of persistent memory (PM), such as Intel Optane DC Persistent Memory Modules (DCPMM), opened up many opportunities for building high-performance indexes directly on PM. However, the many PM indexes proposed by prior work had their evaluation based on PM emulation using DRAM and therefore it was not clear how they would perform on real PM hardware. Moreover, they typically used ad hoc, in-house benchmarks and did not collect PM-specific hardware metrics that are key performance indicators and are instrumental for users and developers to understand the performance behavior of PM indexes. These issues call for a systematic, fair and reproducible approach for evaluating PM indexes. This demonstration highlights the principles and lessons learned from our recent evaluation of PM indexes on real DCPMM and showcases PiBench, a unified benchmarking framework that enables fair and reproducible evaluation of PM indexes. In addition to common metrics, PiBench uniquely integrates monitoring tools to collect PM-specific hardware counters, allowing in-depth performance analysis. Our demonstration is enabled by PiBench Online, a new interactive system built on top of PiBench. Using PiBench Online, users can upload their own index implementations, run preset or customized workloads, and analyze results interactively, all through an easy-to-use web interface. PiBench is open-source and PiBench Online is deployed at https://pibench.org. We hope PiBench Online can promote fair comparison and reproducibility in database and systems communities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".