Fast IPC estimation for performance projections using proxy suites and decision trees
Bibliographic record
Abstract
Accurate IPC estimates are critical for generating performance projections of key workloads on future designs. However, the need to respond to projections requests in a timely manner in the face of rapidly evolving applications and software stacks and tight schedule constraints, often preclude design teams from executing detailed workload analysis, sampling and simulation flows for such purposes. We address this problem by taking advantage of the large amount of data that performance modeling teams commonly generate as part of architectural studies across thousands of workload scenarios. We propose two methods for exploiting these datasets: one that builds proxy suites, and another that builds decision-tree based classifiers. Both methods can generate IPC estimates for a target workload without collecting new workload samples, or running a single additional simulation. We discuss our experience using these techniques to estimate the IPC of numerous commercial workloads on four industrial x86 processor designs. The resulting IPC estimates were on average, within 2% of those obtained via measurements or detailed cycle-accurate simulations Importantly, using these methods, we were able to generate IPC estimates for a target workload in a matter of hours to 1-2 days, compared to several weeks using conventional approaches.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".