A Software-Managed Approach to Die-Stacked DRAM
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Bibliographic record
Abstract
Advances in die-stacking (3D) technology have enabled the tight integration of significant quantities of DRAM with high-performance computation logic. How to integrate this technology into the overall architecture of a computing system is an open question. While much recent effort has focused on hardware-based techniques for using die-stacked memory (e.g., caching), in this paper we explore what it takes for a software-driven approach to be effective. First we consider exposing die-stacked DRAM directly to applications, relying on the static partitioning of allocations between fast on-chip and slow off-chip DRAM. We see only marginal benefits from this approach (9% speedup). Next, we explore OS-based page caches that dynamically partition application memory, but we find such approaches to be worse than not having stacked DRAM at all! We analyze the performance bottlenecks in OS page caches, and propose two simple techniques that make the OS approach viable. The first is a hardware-assisted TLB shoot-down, which is a more general mechanism that is valuable beyond stacked DRAM, and enables OS-managed page caches to achieve a 27% speedup, the second is a software-implemented prefetcher that extends classic hardware prefetching algorithms to the page level, leading to 39% speedup. With these simple and lightweight components, the OS page cache can provide 70% of the performance benefit that would be achievable with an ideal and unrealistic system where all of main memory is die-stacked. However, we also found that applications with poor locality (e.g., graph analyses) are not amenable to any page-caching schemes -- whether hardware or software -- and therefore we recommend that the system still provides APIs to the application layers to explicitly control die-stacked DRAM allocations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it