A Framework for Explainable, Comprehensive, and Customizable Memory-Centric Workloads
Bibliographic record
Abstract
Explainable workloads with analyzable memory traffic patterns are key for accurate performance estimates at early design exploration phase for novel memory solutions. This paper proposes RAMify: a tunable framework for generating explainable memory-centric workloads. By being memory-aware: RAMify offers several tuning knobs enabling the generation of an extensive set of different workloads, each of them is low-level tuned to produce a particular DRAM access pattern. RAMify enables a systematic way to explore and evaluate novel memory subsystem proposals at early design phases, validate their performance, stress their behaviour, and qualitatively compare them against other policies under various memory-aware scenarios to facilitate data-driven design choices. We evaluated with extensive experiments across three different cycle-accurate memory simulators and a full-system multi-core simulator. Results show that using RAMify, we were able to 1) make interesting observations about the comparative behavior of two of the state-of-the-art memory technologies (DDR4 and HBM) that were not possible to make in non memory-centric benchmarks, and 2) We managed to reveal discrepancies in state-of-the-art memory simulator policies and scheduling techniques.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".