Beyond the Memory Wall: A Case for Memory-centric HPC System for Deep\n Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the models and the datasets to train deep learning (DL) models scale,\nsystem architects are faced with new challenges, one of which is the memory\ncapacity bottleneck, where the limited physical memory inside the accelerator\ndevice constrains the algorithm that can be studied. We propose a\nmemory-centric deep learning system that can transparently expand the memory\ncapacity available to the accelerators while also providing fast inter-device\ncommunication for parallel training. Our proposal aggregates a pool of memory\nmodules locally within the device-side interconnect, which are decoupled from\nthe host interface and function as a vehicle for transparent memory capacity\nexpansion. Compared to conventional systems, our proposal achieves an average\n2.8x speedup on eight DL applications and increases the system-wide memory\ncapacity to tens of TBs.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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