Lockless multi-core high-throughput buffering scheme for kernel tracing
Bibliographic record
Abstract
Studying execution of concurrent real-time online systems, to identify far-reaching and hard to reproduce latency and performance problems, requires a mechanism able to cope with voluminous information extracted from execution traces. Furthermore, the workload must not be disturbed by tracing, thereby causing the problematic behavior to become unreproducible. In order to satisfy this low-disturbance constraint, we created the LTTng kernel tracer. It is designed to enable safe and race-free attachment of probes virtually anywhere in the operating system, including sites executed in non-maskable interrupt context. In addition to being reentrant with respect to all kernel execution contexts, LTTng offers good performance and scalability, mainly due to its use of per-CPU data structures, local atomic operations as main buffer synchronization primitive, and RCU (Read-Copy Update) mechanism to control tracing. Given that kernel infrastructure used by the tracer could lead to infinite recursion if traced, and typically requires non-atomic synchronization, this paper proposes an asynchronous mechanism to inform the kernel that a buffer is ready to read. This ensures that tracing sites do not require any kernel primitive, and therefore protects from infinite recursion. This paper presents the core of LTTng's buffering algorithms and measures its performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".