Tree structured data processing on GPUs
Bibliographic record
Abstract
In order to reduce the computing time for processing large tree-structured data sets, parallel processing has been used. Recently, research has been done on parallel computing of tree-structured data on Graphics Processing Units (GPUs). GPU device cannot directly access the tree structured data on hard disks which is commonly stored as objects or linked-lists. So, it is required to copying this tree structured data from hard disk to device memory for the computation and copying tree structured data in its normal structure is very costly because of lots of pointers overhead. Existing tree data structures on GPUs are commonly applied to storing a particular kind of tree, and support limited types of tree traversals. In this work, a tree data structure is proposed to store different kind of trees as a linear data structure (fast in copying). The proposed data structure is applied on general trees and binary trees and supports four common types of tree traversals: pre-order, post-order, in-order and breadth-first traversals. Therefore, most of the tree algorithms can be implemented on GPUs by using this proposed data structure. The results show that the proposed data structure is successfully implemented for all the traversals for binary as well as general trees.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".