Enhanced State History Tree (eSHT): A Stateful Data Structure for Analysis of Highly Parallel System Traces
Bibliographic record
Abstract
Behaviors of distributed systems with many cores and/or many threads are difficult to understand. This is why dynamic analysis tools such as tracers are useful to collect run-time data and help programmers debug and optimize complex programs. However, manual trace analysis on very large traces with billions of events can be a difficult problem which automated trace visualizers and analyzers aim to solve. Trace analysis and visualization software needs fast access to data which it cannot achieve by searching through the entire trace for every query. A number of solutions have adopted stateful analysis which rearranges events into a more query friendly structures after a single pass through the trace. In this paper, we look into current implementations and model the behavior of previous work, the State History Tree (SHT), on traces with many thread creation and deletion. This allows us to identify which properties of the SHT are responsible for inefficient disk usage and high memory consumption. We then propose a more efficient data structure, the enhanced State History Tree (eSHT), to store and query computed states, in order to limit disk usage and reduce the query time for any state. Next, we compare the use of SHT and eSHT on traces with many attributes. We finally verify the scalability of our new data structure according to trace size. As shown by our results, the proposed solution makes near optimal use of disk space, reduces the algorithm's memory usage logarithmically for previously problematic cases, and speeds up queries on traces with many attributes by an order of magnitude. The proposed solution builds upon our previous work, enabling it to easily scale up to traces containing a million threads.
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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 | 0.002 |
| Open science | 0.003 | 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 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".