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 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 | 0.000 | 0.000 |
| 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".