System execution path profiling using hardware performance counters
Bibliographic record
Abstract
The task critical execution path, obtained from a kernel trace, reports the time spent waiting for each task involved in a heterogeneous and distributed application. However, additional profiling is needed to understand and identify the problematic code associated with long-lasting path edges. Hardware counter sampling provides insight on software performance at the microarchitecture level, for instance extracting the call stack every 100K execution cycles to understand where the execution time is spent. Similarly, extracting the call stack at the end of a long waiting system call is often useful. This technique is readily available for either statically or JIT compiled code. However, interpreted code is indirectly executed on the processor and the link between the statements and the executed assembly is missing. We propose an architecture to efficiently record call stacks along the execution path, including interpreted programs, in a low intrusive way that maintains the abstraction boundary between the kernel, the interpreter, and the user code. The method consists in sending a signal from within the performance counter interrupt handler. The user-space code receiving the signal can inspect and record the state of the program. We implemented a profiler for the CPython interpreter using this technique. We studied the benefit, the accuracy, and the cost of the proposed technique compared to an all-kernel monitoring solution.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".