Performance analysis of a dual processor workstation
Bibliographic record
Abstract
Performance of personal computers has been increasing steadily since their introduction in the early eighties. Personal computers available today employ technologies that were not long ago exclusively used on super computers and main-frames. One of the most recent developments in the personal computer technology is the usage of multiprocessors in order to increase the computing power. However, since it is known that computing power increase is not directly proportional to the number of processors, the performance increase gained from additional processors is difficult to predict. Many researchers claim that personal computer technology, based on Intel x86 design, has scaling problems when multiple processors are used. Computer manufacturers so far have failed to provide reliable information related to scaling problems. In order to analyze the performance gain from an additional processor in a personal computer and to study the performance dependence of the computer architecture on the operating system, a dual processor computer system was designed and built from the functional blocks available on the market. The fast Fourier transform algorithm was adopted and modified, to be used as the computation intensive performance indicator. The FFT algorithm was run on the designed two-processor system under Windows NT Workstation version 4.0, and Linux version 2.0 with and without the use of the threading techniques and the performance gain due to the second processor was determined. It was observed that the addition of the second processor resulted in the maximum speedup of 1.91 under Windows NT and 1.67 under Linux, when the threading techniques were used. Furthermore it was observed that without the use of the threading, the addition of the second processor, under specific conditions, impaired the system's performance. The paper presents the details of the performance study.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 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".