A Hierarchical Synchronous Parallel Model for Wide-Area Graph Analytics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Graph analytics has emerged as one of the fundamental techniques to support modern Internet applications. As real-world graph data is generated and stored globally, the scale of the graph that needs to be processed keeps growing. It is critical to efficiently process graphs across multiple geographically distributed datacenters, running wide-area graph analytics. Existing graph analytics frameworks are not designed to run across multiple datacenters well, as they implement a Bulk Synchronous Parallel model that requires excessive wide-area data transfers. In this paper, we present a new Hierarchical Synchronous Parallel model designed and implemented for synchronization across datacenters with a much improved efficiency in inter-datacenter communication. Our new model requires no modifications to graph analytics applications, yet guarantees their convergence and correctness. Our prototype implementation on Apache Spark can achieve up to 32% lower WAN bandwidth usage, 49% faster convergence, and 30% less total cost for benchmark graph algorithms, with input data stored across five geographically distributed datacenters.
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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 | 0.000 |
| 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 it