Optimizing ATLAS code with different profilers
Bibliographic record
Abstract
After the current maintenance period, the LHC will provide higher energy collisions with increased luminosity. In order to keep up with these higher rates, ATLAS software needs to speed up substantially. However, ATLAS code is composed of approximately 6M lines, written by many different programmers with different backgrounds, which makes code optimisation a challenge. To help with this effort different profiling tools and techniques are being used. These include well known tools, such as the Valgrind suite and Intel Amplifier; less common tools like Pin, PAPI, and GOoDA; as well as techniques such as library interposing. In this paper we will mainly focus on Pin tools and GOoDA. Pin is a dynamic binary instrumentation tool which can obtain statistics such as call counts, instruction counts and interrogate functions' arguments. It has been used to obtain CLHEP Matrix profiles, operations and vector sizes for linear algebra calculations which has provided the insight necessary to achieve significant performance improvements. Complimenting this, GOoDA, an in-house performance tool built in collaboration with Google, which is based on hardware performance monitoring unit events, is used to identify hot-spots in the code for different types of hardware limitations, such as CPU resources, caches, or memory bandwidth. GOoDA has been used in improvement of the performance of new magnetic field code and identification of potential vectorization targets in several places, such as Runge-Kutta propagation code.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".