Bibliographic record
Abstract
A snapshot is an important object in distributed computing whose implementation in asynchronous systems has been studied extensively. It consists of a collection of m >1 components, each storing a value, and supports two atomic operations: an UPDATE of a specified component's value and a SCAN of all components to determine their values at some point in time.In this paper, we investigate implementations of a multiwriter snapshot object in a synchronous shared memory model. In this setting, we show that a snapshot object can be efficiently implemented and prove a tight tradeoff between the complexity of the SCAN and the UPDATE operations. First, we describe a wait-free implementation that performs UPDATE in O(1) time and SCAN in O(m) time, using only slightly more than twice the amount of space needed to simply store the m values. We also describe a variant that performs UPDATE in O(1) time and SCAN in O(n) time.Second, we describe a wait-free implementation that performs UPDATE in O(log m) time and SCAN in O(1) time, and a variant that performs UPDATE in O(log n) time and SCAN in O(1) time.Third, we show how to combine these implementations to realize two implementations that perform UPDATE in Θ(log(m/c)) time and SCAN in Θ(c) time, for 1≤c≤m, or perform UPDATE in Θ(log(n/c)) time and SCAN in Θ(c) time, for 1≤c≤n. This implies that Time[UPDATE] ∈O(log(minm,n/Time[SCAN])). We also prove that Time[UPDATE] ∈ Ω(log(minm,n/Time[SCAN]) ), which matches our upper bound.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".