Heterogeneous memory architectures: A HW/SW approach for mixing die-stacked and off-package memories
Bibliographic record
Abstract
Die-stacked DRAM is a technology that will soon be integrated in high-performance systems. Recent studies have focused on hardware caching techniques to make use of the stacked memory, but these approaches require complex changes to the processor and also cannot leverage the stacked memory to increase the system's overall memory capacity. In this work, we explore the challenges of exposing the stacked DRAM as part of the system's physical address space. This non-uniform access memory (NUMA) styled approach greatly simplifies the hardware and increases the physical memory capacity of the system, but pushes the burden of managing the heterogeneous memory architecture (HMA) to the software layers. We first explore simple (and somewhat impractical) schemes to manage the HMA, and then refine the mechanisms to address a variety of hardware and software implementation challenges. In the end, we present an HMA approach with low hardware and software impact that can dynamically tune itself to different application scenarios, achieving performance even better than the (impractical-to-implement) baseline 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".