Performance analysis of electronic commerce multiprocessor server
Bibliographic record
Abstract
The performance of an electronic commerce server, i.e. a system running electronic commerce applications, is evaluated in the case of a shared-bus multiprocessor architecture. In particular, we focus on the memory subsystem design. We have analyzed the common case of a system using the MESI coherence protocol, for maintaining coherency among the processor private caches. We have evaluated the miss ratio and the bus traffic of such a system by varying cache size, number of ways, scheduling policy and number of processors, highlighting the relations with different types of data sharing generated by the application or the kernel. We found that passive sharing and false sharing are the major sources of coherence overhead in the case of relatively large caches (over 1M-byte size). False sharing is mainly due to kernel data, and can be eliminated by using appropriate data structure design techniques. A scheduling technique, like cache-affinity can reduce passive sharing but it is not effective in every load condition. Thus, a special coherence protocol could be a better solution to completely eliminate passive sharing overhead and boost performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".