An Analytical Cache Performance Evaluation Framework for Embedded Out-of-Order Processors Using Software Characteristics
Bibliographic record
Abstract
Utilizing analytical models to evaluate proposals or provide guidance in high-level architecture decisions is been becoming more and more attractive. A certain number of methods have emerged regarding cache behaviors and quantified insights in the last decade, such as the stack distance theory and the memory level parallelism (MLP) estimations. However, prior research normally oversimplified the factors that need to be considered in out-of-order processors, such as the effects triggered by reordered memory instructions, and multiple dependences among memory instructions, along with the merged accesses in the same MSHR entry. These ignored influences actually result in low and unstable precisions of recent analytical models. By quantifying the aforementioned effects, this article proposes a cache performance evaluation framework equipped with three analytical models, which can more accurately predict cache misses, MLPs, and the average cache miss service time, respectively. Similar to prior studies, these analytical models are all fed with profiled software characteristics in which case the architecture evaluation process can be accelerated significantly when compared with cycle-accurate simulations. We evaluate the accuracy of proposed models compared with gem5 cycle-accurate simulations with 16 benchmarks chosen from Mobybench Suite 2.0, Mibench 1.0, and Mediabench II. The average root mean square errors for predicting cache misses, MLPs, and the average cache miss service time are around 4%, 5%, and 8%, respectively. Meanwhile, the average error of predicting the stall time due to cache misses by our framework is as low as 8%. The whole cache performance estimation can be sped by about 15 times versus gem5 cycle-accurate simulations and 4 times when compared with recent studies. Furthermore, we have shown and studied the insights between different performance metrics and the reorder buffer sizes by using our models. As an application case of the framework, we also demonstrate how to use our framework combined with McPAT to find out Pareto optimal configurations for cache design space explorations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.000 |
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 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".