Providing Serializability for Pregel-like Graph Processing Systems
Bibliographic record
Abstract
There is considerable interest in the design and development of distributed systems that can execute algorithms to process large graphs. Serializability guarantees that parallel executions of a graph algorithm produce the same results as some serial execution of that algorithm. Serializability is required by many graph algorithms for accuracy, correctness, or termination but existing graph processing systems either do not provide serializability or cannot provide it eciently. To address this deciency, we provide a complete solution that can be implemented on top of existing graph processing systems. Our solution formalizes the notion of serializability and the conditions under which it can be provided for graph processing systems. We propose a novel partitionbased synchronization approach that enforces these conditions to eciently provide serializability. We implement our partition-based technique into the open source graph processing system Giraph and demonstrate that our technique is congurable, transparent to algorithm developers, and provides large across-the-board performance gains of up to 26 over existing techniques.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".