14221074
Accurate and efficient processor performance prediction via regression tree based modeling
Computer architects usually evaluate new designs using cycle-accurate processor simulation. This approach provides a detailed insight into processor performance, power consumption and complexity. However, only configurations in a subspace can be simulated in practice due to long simulation time and limited resource, leading to suboptimal conclusions which might not be applied to a larger design space. In this paper, we propose a performance prediction approach which employs state-of-the-art techniques from experiment design, machine learning and data mining. According to our experiments on single and multi-core processors, our prediction model generates highly accurate estimations for unsampled points in the design space and show the robustness for the worst-case prediction. Moreover, the model provides quantitative interpretation tools that help investigators to efficiently tune design parameters and remove performance bottlenecks.
3489230
Efficient mart-aided modeling for microarchitecture design space exploration and performance prediction
Computer architects usually evaluate new designs by cycle-accurate processor simulation. This approach provides detailed insight into processor performance, power consumption and complexity. However, only configurations in a subspace can be simulated in practice due to long simulation time and limited resource, leading to suboptimal conclusions which might not be applied in a larger design space. In this paper, we propose an automated performance prediction approach which employs state-of-the-art techniques from experiment design, machine learning and data mining. Our method not only produces highly accurate estimations for unsampled points in the design space, but also provides interpretation tools that help investigators to understand performance bottlenecks. According to our experiments, by sampling only 0.02% of the full design space with about 15 millions points, the median percentage errors, based on 5000 independent test points, range from 0.32% to 3.12% in 12 benchmarks. Even for the worst-case performance, the percentage errors are within 7% for 10 out of 12 benchmarks. In addition, the proposed model can also help architects to find important design parameters and performance bottlenecks.
2524422
Versatile prediction and fast estimation of Architectural Vulnerability Factor from processor performance metrics
The shrinking processor feature size, lower threshold voltage and increasing clock frequency make modern processors highly vulnerable to transient faults. Architectural Vulnerability Factor (AVF) reflects the possibility that a transient fault eventually causes a visible error in the program output, and it indicates a system's susceptibility to transient faults. Therefore, the awareness of the AVF especially at early design stage is greatly helpful to achieve a trade-off between system performance and reliability. However, tracking the AVF during program execution is extremely costly, which makes accurate AVF prediction extraordinarily attractive to computer architects. In this paper, we propose to use Boosted Regression Trees, a nonparametric tree-based predictive modeling scheme, to identify the correlation across workloads, execution phases and processor configurations between a key processor structure's AVF and various performance metrics. The proposed method not only makes an accurate prediction but quantitatively illustrates individual performance variable's importance to the AVF. Moreover, to reduce the prediction complexity, we also utilize a technique named Patient Rule Induction Method to extract some simple selecting rules on important metrics. Applying these rules during run time can fast identify execution intervals with a relatively high AVF.
3439449
Classification of functional data: A segmentation approach
We suggest a classification and feature extraction method on functional data where the predictor variables are curves. The method, called functional segment discriminant analysis (FSDA), combines the classical linear discriminant analysis and support vector machine. FSDA is particularly useful for irregular functional data, characterized by spatial heterogeneity and local patterns like spikes. FSDA not only reduces the computation and storage burden by using a fraction of the spectrum, but also identifies important predictors and extracts features. FSDA is highly flexible, easy to incorporate information from other data sources and/or prior knowledge from the investigators. We apply FSDA to two public domain data sets and discuss the understanding developed from the study.
709845
Supporting Live Video on Ad Hoc Wireless Networks: Unbalanced Multiple Description Coding, Single Path Transport and Recursive Error Concealment
Robust transmission of live video over ad hoc wireless networks presents new challenges: high bandwidth requirements are coupled with delay constraints; nodes are constrained in processing power and storage capacity; ad hoc wireless networks suffer from bursty packet losses that drastically degrade the viewing experience. Accordingly, we propose a simple, but practical unbalanced multiple description (UMD) codec that uses single path transport only. At the UMD decoder, the lost Low- Resolution (LR) frames can be receded from the corresponding ones of the correctly received High- Resolution (HR) stream. This makes LR more robust to packet losses and ensures continuous video playback. The simulation results show that the proposed UMD codec has higher decoded quality, smaller quality fluctuation and lower probability of pause than other error resilience techniques, especially when channel burstiness becomes large. We also propose a novel sequence-based error concealment (EC) algorithm for our UMD decoder. It recursively uses multi-frame recovery principle to frame-by-frame reduce error drift in the HR description with respect to the LR description. In fact, the algorithm can be applied to most UMD schemes. It has the advantage of working on integer-, half- or quarter- pixel precision and looking backward without buffering future frames. Experimental results show that error decreases the fastest if the UMD decoder uses our EC algorithm. It can provide satisfactory performance on both objective and subjective evaluation.
26704614
A New Approach to Forecast Crude Oil Price Based on Fuzzy Neural Network
A new oil price forecasting model based on fuzzy neural network, which combings RBF neural network, Markov chain based semi parametric model and wavelet analysis is proposed in this paper. The high degree of prediction accuracy confirms the performance of the model in accurate forecasts, reinforcement learning properties and mapping capabilities.
16012768
Continuous Optimization based-on Boosting Gaussian Mixture Model
A new estimation of distribution algorithm (EDA) based on Gaussian mixture model (GMM) is proposed, in which boosting, an efficient ensemble learning method, is adopted to estimate GMM. By boosting simple GMM with two components, it has the ability of learning the model structure and parameters automatically without any requirement for prior knowledge. Moreover, since boosting can be viewed as a gradient search for a good fit of some objective in function space, the new EDA is time efficient. A set of experiments is implemented to evaluate the efficiency and performance of the new algorithm. The results show that, with a relatively smaller population and less number of generations, the new algorithm can perform as well as compared EDAs in optimizing multimodal functions
29928622
An event-state approach to handling temporal queries in an extended relational database system
Many database applications such as office information systems, computer aided design and manufacturing require temporal support. The requirement for storing all the historical information has resulted in performance and storage problems. This paper presents and illustrates an event-state approach to effectively and efficiently store and retrieve temporal data. This model demonstrates that by using the events that have occurred, facilities can be constructed that would permit the processes of 'rolling-back' to a historical state and/or 'rolling-forward' to the then current state. The proposed temporal features have been incorporated in a relational database system. Processing efficiency and temporal query capabilities have been illustrated and discussed.<<ETX>>
43513684
A representation theorem for recovering contraction relations satisfying wci
A notion of an image structure associated with the canonical epistemic state is introduced. Based on it, we get a representation result for recovering contraction inference relations satisfying the condition weak conjunctive inclusion (wci) in terms of F-standard epistemic AGM states. In effect, this result establishes a representation theorem for belief contraction functions satisfying AGM postulates (k-1) - (k-7), and Rott's (wci) and (k-8c), and hence generalizes Rott's corresponding result in the finite framework.