ATLAS offline software performance monitoring and optimization
Bibliographic record
Abstract
In a complex multi-developer, multi-package software environment, such as the ATLAS offline Athena framework, tracking the performance of the code can be a non-trivial task in itself. In this paper we describe improvements in the instrumentation of ATLAS offline software that have given considerable insight into the performance of the code and helped to guide optimisation. Code can be instrumented firstly using the PAPI tool, which is a programing interface for accessing hardware performance counters. PAPI events can count floating point operations, cycles and instructions and cache accesses. Triggering PAPI to start/stop counting for each algorithm and processed event gives a good understanding of the whole algorithm level performance of ATLAS code. Further data can be obtained using pin, a dynamic binary instrumentation tool. Pintools can be used to obtain similar statistics as PAPI, but advantageously without requiring recompilation of the code. Fine grained routine and instruction level instrumentation is also possible. Pintools can additionally interrogate the arguments to functions, like those in linear algebra libraries, so that a detailed usage profile can be obtained. These tools have charaterised the extensive use of vector and matrix operations in ATLAS tracking. Currently, CLHEP is used here, which is not an optimal choice. To help evaluate replacement libraries a testbed has been setup allowing comparison of the performance of different linear algebra libraries (including CLHEP, Eigen and SMatrix/SVector). Results are then presented via the ATLAS Performance Management Board framework, which runs daily with the current development branch of the code and monitors reconstruction and Monte-Carlo jobs. This framework analyses the CPU and memory performance of algorithms and an overview of results are presented on a web page. These tools have provided the insight necessary to plan and implement performance enhancements in ATLAS code by identifying the most common operations, with the call parameters well understood, and allowing improvements to be quantified in detail.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".