Machine learning for performance and power modeling of heterogeneous systems
Bibliographic record
Abstract
Modern processing systems with heterogeneous components (e.g., CPUs, GPUs) have numerous configuration and design options such as the number and types of cores, frequency, and memory bandwidth. Hardware architects must perform design space explorations in order to accurately target markets of interest under tight time-to-market constraints. This need highlights the importance of rapid performance and power estimation mechanisms. This work describes the use of machine learning (ML) techniques within a methodology for the estimating performance and power of heterogeneous systems. In particular, we measure the power and performance of a large collection of test applications running on real hardware across numerous hardware configurations. We use these measurements to train a ML model; the model learns how the applications scale with the system's key design parameters. Later, new applications of interest are executed on a single configuration, and we gather hardware performance counter values which describe how the application used the hardware. These values are fed into our ML model's inference algorithm, which quickly identify how this application will scale across various design points. In this way, we can rapidly predict the performance and power of the new application across a wide range of system configurations. Once the initial run of the program is complete, our ML algorithm can predict the application's performance and power at many hardware points faster than running it at each of those points and with a level of accuracy comparable to cycle-level simulators.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".