Machine learning for performance and power modeling of heterogeneous systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it