Toward Efficient Programmer-Managed Two-Level Memory Hierarchies in Exascale Computers
Why this work is in the frame
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Bibliographic record
Abstract
Future exascale systems will require very aggressive memory systems simultaneously delivering huge storage capacities and multi-TB/s bandwidths. To achieve the bandwidth targets, in-package, die-stacked memory technologies will likely be necessary. However, these integrated memories do not provide enough capacity to achieve the overall per-node memory size requirements. As a result, conventional off-package memory (e.g., DIMMs) will still be needed. This creates a "two-level memory" (TLM) organization where a portion of the machine's memory space provides high bandwidth, and the remainder provides capacity at a lower level of performance. Effective use of such a heterogeneous memory organization may require the co-design of the software applications along with the advancements in memory architecture. In this paper, we explore the efficacy of programmer-driven approaches to managing a TLM system, using three Exascale proxy applications as case studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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