Microarchitectural Analysis of Pre-Processing Stage in Machine Learning Workloads
Bibliographic record
Abstract
As Machine Learning (ML) has become integral for various applications, ML workloads are now important considerations for deployment across diverse use cases, ranging from data centers to edge devices. ML encompasses diverse application fields, including vision, audio, text, and multimodal areas, each involving specific raw data formats that often needs pre-processing to become more interpretable for the models and to ensure a more balanced and standardized data distribution. This stage can also include data augmentation to improve model robustness and performance. Therefore, most ML workloads incorporate a stage, commonly referred to as pre-processing, prior to processing the actual data in complex ML model. As the amount of data size increases at a drastic rate, the preprocessing stage now requires closer attention given its significant computation time. In this paper, we conduct an in-depth microarchitectural analysis of the ML pipeline's pre-processing stage to uncover bottlenecks and utilize a bottom-up approach to deliver valuable insights by identifying code hotspots.
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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 | 0.002 |
| 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) | 0.004 | 0.001 |
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".