A quantitative analysis framework to evaluate the performance and costs of computer architectural alternatives
Bibliographic record
Abstract
Computer architects are currently confronted with the task of evaluating the quality of architectural designs not just in terms of the single familiar criterion of performance but also in terms of design criteria such as die area, power consumption and energy consumption. In this dissertation, I propose and evaluate a quantitative analysis framework, implemented in the Solomon software tool, that can precisely characterize how the best architecture design in a study changes across the full spectrum of architectural contexts which describe the importance of each criterion to the analysis. Just as computer architects currently use average performance to compare architectures when multiple benchmarks are important, the analysis framework I propose uses an architectural cost index value to compare architectures by aggregating multiple criteria in accordance with their importance to the architectural context. The architectural cost index for each architecture is then graphed across all architectural contexts to portray how the best architecture changes in different contexts. To compute an architecture's cost index, I propose a new aggregation function called the trade-off sum that represents an architectural context as a linear trade-off such as: an N% area increase is expected to improve performance by at least 0.5N%. To evaluate the analysis framework, I conduct a major architectural study to determine how effectively 21 vector architectures use additional area to improve media program performance. To compute the area for these architectures, I develop a functional area modeling methodology that is accurate to within 14% of the measured area of circuits in commercial microprocessors for eight major processor components and within 5% for five of these eight components. Architects can use the Functional Area Modeling Tool for Architects (FAMTA) to automatically compute the area of processor components from high level descriptions, much like a compiler allows developers to write software in a high-level language rather than machine language. Using the new analysis framework, I demonstrate that current media hardware designs are best in contexts where an N% area increase is expected to improve performance by 0.98N%–1.39N%. When the improvement can be less than 0.98N% (performance is more important), then vector media architectures, which incorporate vector processing technology, use area more effectively to improve performance than current designs.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".