Coherency overhead of Processing-in-Memory in the presence of shared data
Bibliographic record
Abstract
Processing-in-Memory (PIM) architectures are an instance of Near-Data Processing (NDP) that promise to bridge the power-and performance-walls caused by the high latency and power costs associated with external memory access. PIM systems can minimize data transfers to and from processor/memory, relegating (parts of) processing to memory. However, the effects of processor (s)/PIM-systems shared access to data on coherency overhead are not yet understood. In this manuscript, we model coherency overhead of shared data access by processor and PIM systems: i.e., shared access to common data. We present an analytical model that quantifies performance in function of degree of interleaving and PIM system latencies. We evaluate our model using simple image processing kernels, and experimentally validate our approach using PIMSIM, an open-source PIM simulator. Results show that our analytical model can predict coherency overhead within 2% of error margin, and we identify several interesting behaviors that warrant further research towards widespread adoption of PIM.
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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 | 0.006 |
| 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 | 0.001 | 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".