Exploiting multithreaded architectures to improve the hash join operation
Why this work is in the frame
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Bibliographic record
Abstract
As database management systems gain importance in our everyday life, it is essential to have efficient implementations of important database operations such as the hash join. Improvements in processor architectures including simultaneous multithreaded architectures and Chip Multiprocessors have opened opportunities for taking advantage of the new multithreaded hardware. Recently, several efforts have been done to enhance database performance through architecture-aware data management. In this paper, we present a new architecture-aware hash join (AA_HJ) algorithm for main memory database systems, where all the data resides in memory. AA_HJ relies on sharing critical structures at the cache level, and distributing the load evenly between threads. Our timing results show a performance improvement up to 2.9x for the Intel® Pentium® 4 HT and up to 4.6x on the Intel® Quad Xeon® Dual-Core machine, compared to single-threaded hash join. The L2 load miss rate is reduced by up 82%.
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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 | 0.001 | 0.001 |
| 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 it