Temporal streams in commercial server applications
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
Commercial server applications remain memory bound on modern multiprocessor systems because of their large data footprints, frequent sharing, complex non-strided access patterns, and long chains of dependant misses. To improve memory system performance despite these challenging access patterns, researchers have proposed prefetchers that exploit temporal streams-recurring sequences of memory accesses. Although prior studies show substantial performance improvement from such schemes, they fail to explain why temporal streams arise; that is, they treat commercial applications as a black box and do not identify the specific behaviors that lead to recurring miss sequences. In this paper, we perform an information-theoretic analysis of miss traces from single-chip and multi-chip multiprocessors to identify recurring temporal streams in web serving, online transaction processing, and decision support workloads. Then, using function names embedded in the application binaries and Solaris kernel, we identify the code modules and behaviors that give rise to temporal streams.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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