Architectural Support for Efficient Data Movement in Fully Disaggregated Systems
Bibliographic record
Abstract
Traditional data centers include monolithic servers that tightly integrate CPU, memory and disk (Figure 1a). Instead, Disaggregated Systems (DSs) [8, 13, 18, 27] organize multiple compute (CC), memory (MC) and storage devices as independent, failure-isolated components interconnected over a high-bandwidth network (Figure 1b). DSs can greatly reduce data center costs by providing improved resource utilization, resource scaling, failure-handling and elasticity in modern data centers [5, 8-10, 10, 11, 13, 18, 27] The MCs provide large pools of main memory (remote memory), while the CCs include the on-chip caches and a few GBs of DRAM (local memory) that acts as a cache of remote memory. In this context, a large fraction of the application's data (~ 80%) [8, 18, 27] is located in remote memory, and can cause large performance penalties from remotely accessing data over the network.
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.
How this classification was reachedexpand
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".