Chaos in computer performance

Modern computer microprocessors are composed of hundreds of millions of transistors that interact through intricate protocols. Their performance during program execution may be highly variable and present aperiodic oscillations. In this paper, we apply current nonlinear time series analysis techniques to the performances of modern microprocessors during the execution of prototypical programs. Our results present pieces of evidence strongly supporting that the high variability of the performance dynamics during the execution of several programs display low-dimensional deterministic chaos, with sensitivity to initial conditions comparable to textbook models. Taken together, these results show that the instantaneous performances of modern microprocessors constitute a complex (or at least complicated) system and would benefit from analysis with modern tools of nonlinear and complexity science

How to improve computer performance

Nowadays a large number of people use computers for different purposes such as games, work, listening to music, watching videos, making presentations etc. However, while operating a computer, most users often have to cope with the low performance of their machines. And as some people do not know how to optimize the computers, they buy a new one, even more expensive, thinking that it will solve the problem. In this case basic knowledge of computer optimization can save both time and money

Computer performance analysis - Measurement objectives and tools

Objectives and measurements in computer performance analysi

On the importance of nonlinear modeling in computer performance prediction

Computers are nonlinear dynamical systems that exhibit complex and sometimes even chaotic behavior. The models used in the computer systems community, however, are linear. This paper is an exploration of that disconnect: when linear models are adequate for predicting computer performance and when they are not. Specifically, we build linear and nonlinear models of the processor load of an Intel i7-based computer as it executes a range of different programs. We then use those models to predict the processor loads forward in time and compare those forecasts to the true continuations of the time seriesComment: Appeared in "Proceedings of the 12th International Symposium on Intelligent Data Analysis

Trends in computational capabilities for fluid dynamics

Milestones in the development of computational aerodynamics are reviewed together with past, present, and future computer performance (speed and memory) trends. Factors influencing computer performance requirements for both steady and unsteady flow simulations are identified. Estimates of computer speed and memory that are required to calculate both inviscid and viscous, steady and unsteady flows about airfoils, wings, and simple wing body configurations are presented and compared to computer performance which is either currently available, or is expected to be available before the end of this decade. Finally, estimates of the amounts of computer time that are required to determine flutter boundaries of airfoils and wings at transonic Mach numbers are presented and discussed

Solving the Klein-Gordon equation using Fourier spectral methods: A benchmark test for computer performance

The cubic Klein-Gordon equation is a simple but non-trivial partial differential equation whose numerical solution has the main building blocks required for the solution of many other partial differential equations. In this study, the library 2DECOMP&FFT is used in a Fourier spectral scheme to solve the Klein-Gordon equation and strong scaling of the code is examined on thirteen different machines for a problem size of 512^3. The results are useful in assessing likely performance of other parallel fast Fourier transform based programs for solving partial differential equations. The problem is chosen to be large enough to solve on a workstation, yet also of interest to solve quickly on a supercomputer, in particular for parametric studies. Unlike other high performance computing benchmarks, for this problem size, the time to solution will not be improved by simply building a bigger supercomputer.Comment: 10 page

Maze computer performance in Down syndrome

Introduction: These changes are the main causes of defi cits in perceptual-motor skills responsible for motor skill acquisition and performance of functional activities. AIMS: The current study aimed at verifying the quantitative performance of people with DS in undertaking a computer task to compare the performances of typical development (TD). Method: 60 subjects participated in the study, 30 with Down’s syndrome and 30 with typical development, matched by sex. Individuals were aged from 10–36. The groups were divided into three subgroups by age: Group 1 (G1) 10–18; Group 2 (G2) 18–25; Group 3 (G3) 25–36. They performed a computer maze task. During the acquisition phase all groups attempted the maze 30 times, and then after 5 minutes they performed 5 repetitions of Maze 1 for the retention phase. Finally, for the transfer phase, they performed fi ve repetitions in Maze 2. The dependent variables were submitted to a group, age group, gender and block ANOVA with repeated measures on the last factor. Results: In acquisition phase there was a significant decrease in movement time (MT) between the fi rst and last acquisition block, but only for the DS-group. In retention, there was a significant effect of Group, and an interaction between Block and Group, indicating that MTs increased from retention to transfer, but only for the DS-group. Conclusion: It was found that participants with DS improved performance during acquisition and retention, but they had diffi culty in performing the transfer of the computational task for a similar situation. The age and gender were not signifi cant in any of the stages of the study.Introduction: These changes are the main causes of defi cits in perceptual-motor skills responsible for motor skill acquisition and performance of functional activities. AIMS: The current study aimed at verifying the quantitative performance of people with DS in undertaking a computer task to compare the performances of typical development (TD). Method: 60 subjects participated in the study, 30 with Down’s syndrome and 30 with typical development, matched by sex. Individuals were aged from 10–36. The groups were divided into three subgroups by age: Group 1 (G1) 10–18; Group 2 (G2) 18–25; Group 3 (G3) 25–36. They performed a computer maze task. During the acquisition phase all groups attempted the maze 30 times, and then after 5 minutes they performed 5 repetitions of Maze 1 for the retention phase. Finally, for the transfer phase, they performed fi ve repetitions in Maze 2. The dependent variables were submitted to a group, age group, gender and block ANOVA with repeated measures on the last factor. Results: In acquisition phase there was a significant decrease in movement time (MT) between the fi rst and last acquisition block, but only for the DS-group. In retention, there was a significant effect of Group, and an interaction between Block and Group, indicating that MTs increased from retention to transfer, but only for the DS-group. Conclusion: It was found that participants with DS improved performance during acquisition and retention, but they had diffi culty in performing the transfer of the computational task for a similar situation. The age and gender were not signifi cant in any of the stages of the study