Bibliographic record
Abstract
Volatile media have dominated the realm of main memory on servers and desktop computers for decades. In 2019, Intel released the Optane Data Center Persistent Memory Module (DCPMM), which offers the capacity and persistence of block devices while providing the byte addressability and low latency of DRAM. These new memory modules allow programmers to develop data structures that can survive in main memory across crashes and power failures, without relying on secondary power sources such as batteries. This work presents the design of a persistent memory hash table data structure that incorporates several features to maximize efficiency: the locks for concurrency control are kept in volatile DRAM, an embedded memory allocator is used, a parallel table resize operation is implemented, and a mechanism is provided to incrementally expand the underlying memory-mapped file. We compare PHPRX experimentally against the Dash persistent memory hash table published recently by Lu et al., and demonstrate substantial speed-ups on an Intel Xeon server equipped with genuine Intel Optane DCPMM. Our performance advantage holds despite PHPRX using a space-efficient incremental approach to expanding the underlying memory-mapped file, as opposed to the much simpler static allocation approach used by Dash.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".