ALP: Alleviating CPU-Memory Data Movement Overheads in Memory-Centric Systems
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Bibliographic record
Abstract
Partitioning applications between near-data processing (NDP) and host CPU cores causes inter-segment data movement overhead, which is caused by moving data generated by one segment (e.g., instructions, functions) and used in other consecutive segments. Prior works take two approaches to this problem. The first approach maps segments to NDP or host cores based on the properties of each segment, neglecting the inter-segment data movement overhead. The second approach partitions applications based on the overall memory bandwidth savings, and does not offload each segment to the best-fitting core if they incur high inter-segment data movement. We show that 1) mapping each segment to its best-fitting core ideally can provide substantial benefits, and 2) the inter-segment data movement reduces this benefit significantly. We introduce ALP, a new programmer-transparent technique to alleviate the inter-segment data movement overhead between host and memory in NDP systems. ALP proactively and accurately transfers the required data between the segments based on the key observation that the instructions that generate the inter-segment data stay the same across different executions of a program. ALP uses a compiler pass to identify these instructions and uses specialized hardware to transfer their produced data at runtime. We evaluate ALP across a wide range of workloads and demonstrate 54.3% and 45.4% average speedup over CPU-only and NDP-only executions, respectively.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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