Efficient Spatio-Textual Similarity Join Processing on NUMA Systems
Bibliographic record
Abstract
Due to the rapid growth in the use of location based services (LBS), abundant spatially referenced text data is being generated. Hence, spatio-textual queries and in particular, spatio-textual join have gained prominence in recent times. Spatio-textual similarity join (STSJ) is an expensive operation, which is used to retrieve documents that are both textually relevant and spatially nearby. NUMA architectures are becoming increasingly prevalent in modern multi-core machines. Applications that are agnostic of the underlying NUMA topology may not be able to fully exploit the hardware. Due to the compute intensive nature of STSJ, efficient processing of STSJ is important, particularly in the context of NUMA architectures. Previous work on spatio-textual similarity join has not addressed this challenge. To remedy this, we explore several approaches to parallelize spatio-textual similarity join on modern NUMA architecture machines. Specifically, we propose three NUMA-aware algorithms. Our best-performing NUMA-aware algorithm exploits topology-aware work-stealing with adaptive data placement. Experimental evaluation involving four real-world datasets (on two different hardware architectures) demonstrates that our NUMA-aware algorithms perform significantly better than existing approaches that do not consider NUMA-awareness.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".