Deconstructing the overhead in parallel applications
Bibliographic record
Abstract
Performance problems in parallel programs manifest as lack of scalability. These scalability issues are often very difficult to debug. They can stem from synchronization overhead, poor thread scheduling decisions, or contention for hardware resources, such as shared caches. Traditional profiling tools attribute program cycles to different functions, but do not generate immediate insight into issues limiting scalability. Profiling information is very program-specific and is usually processed manually by a human expert in a time-consuming and cumbersome process. Our experience in tuning performance of parallel applications led us to discover that performance tuning can be considerably simplified, and even to some degree automated, if profiling measurements are organized according to several intuitive performance factors common to most parallel programs. In this work we present these factors and propose a hierarchical framework composing them. We present three case studies where analyzing profiling data according to the proposed principle led us to improve performance of three parallel programs by a factor of 6-20×. Our work lays foundation for new ways of organizing and visualizing profiling data in performance tuning tools.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".