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Performance prediction

About: Performance prediction is a research topic. Over the lifetime, 2965 publications have been published within this topic receiving 36826 citations.


Papers
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Journal ArticleDOI
TL;DR: An analysis of an asynchronous phase-coded spread-spectrum multiple-access communication system reveals which code parameters have the greatest impact on communication performance and provides analytical tools for use in preliminary system design.
Abstract: An analysis of an asynchronous phase-coded spread-spectrum multiple-access communication system is presented. The results of this analysis reveal which code parameters have the greatest impact on communication performance and provide analytical tools for use in preliminary system design. Emphasis is placed on average performance rather than worst-case performance and on code parameters which can be computed easily.

1,723 citations

Journal ArticleDOI
TL;DR: In this paper, a simulation and digital computer modeling effort is described in which a wind turbine- generator system is adapted for stability evaluation using a large scale transient stability computer program, which provides the capability of simulating a wide variety of wind variations, in addition to the usual network disturbances.
Abstract: A simulation and digital computer modeling effort is described in which a wind turbine- generator system is adapted for stability evaluation using a large scale transient stability computer program. Component models of the MOD-2 wind generator system are described and their digital model equations are provided. A versatile wind velocity model is described, which provides the capability of simulating a wide variety of wind variations, in addition to the usual network disturbances. Computed results obtained from runs of the enhanced stability program are provided that illustrate the wind turbine-generator system dynamic performance for changes in wind velocity.

605 citations

Proceedings ArticleDOI
20 Oct 2006
TL;DR: This paper derives and validate regression models for performance and power, and presents optimizations for a baseline regression model to obtain application-specific models to maximize accuracy in performance prediction and regional power models leveraging only the most relevant samples from the microarchitectural design space to maximizing accuracy in power prediction.
Abstract: We propose regression modeling as an efficient approach for accurately predicting performance and power for various applications executing on any microprocessor configuration in a large microarchitectural design space. This paper addresses fundamental challenges in microarchitectural simulation cost by reducing the number of required simulations and using simulated results more effectively via statistical modeling and inference.Specifically, we derive and validate regression models for performance and power. Such models enable computationally efficient statistical inference, requiring the simulation of only 1 in 5 million points of a joint microarchitecture-application design space while achieving median error rates as low as 4.1 percent for performance and 4.3 percent for power. Although both models achieve similar accuracy, the sources of accuracy are strikingly different. We present optimizations for a baseline regression model to obtain (1) application-specific models to maximize accuracy in performance prediction and (2) regional power models leveraging only the most relevant samples from the microarchitectural design space to maximize accuracy in power prediction. Assessing sensitivity to the number of samples simulated for model formulation, we find fewer than 4,000 samples from a design space of approximately 22 billion points are sufficient. Collectively, our results suggest significant potential in accurate and efficient statistical inference for microarchitectural design space exploration via regression models.

472 citations

Journal ArticleDOI
01 Aug 2011-Energy
TL;DR: In this article, a CFD model for the evaluation of energy performance and aerodynamic forces acting on a straight-bladed vertical-axis Darrieus wind turbine is presented. But the model is not suitable for the application of wind turbines to the power grid.

409 citations

Proceedings Article
16 Mar 2016
TL;DR: Ernest, a performance prediction framework for large scale analytics, and evaluation on Amazon EC2 using several workloads shows that the prediction error is low while having a training overhead of less than 5% for long-running jobs.
Abstract: Recent workload trends indicate rapid growth in the deployment of machine learning, genomics and scientific workloads on cloud computing infrastructure. However, efficiently running these applications on shared infrastructure is challenging and we find that choosing the right hardware configuration can significantly improve performance and cost. The key to address the above challenge is having the ability to predict performance of applications under various resource configurations so that we can automatically choose the optimal configuration. Our insight is that a number of jobs have predictable structure in terms of computation and communication. Thus we can build performance models based on the behavior of the job on small samples of data and then predict its performance on larger datasets and cluster sizes. To minimize the time and resources spent in building a model, we use optimal experiment design, a statistical technique that allows us to collect as few training points as required. We have built Ernest, a performance prediction framework for large scale analytics and our evaluation on Amazon EC2 using several workloads shows that our prediction error is low while having a training overhead of less than 5% for long-running jobs.

401 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202324
202271
2021144
2020162
2019150
2018112