The Experts below are selected from a list of 75555 Experts worldwide ranked by ideXlab platform

Timothy Sherwood - One of the best experts on this subject based on the ideXlab platform.

  • wavelet based Phase Classification
    International Conference on Parallel Architectures and Compilation Techniques, 2006
    Co-Authors: Ted Huffmire, Timothy Sherwood
    Abstract:

    Phase analysis has proven to be a useful method of summarizing the time-varying behavior of programs, with uses ranging from reducing simulation time to guiding run-time optimizations. Although Phase Classification techniques based on basic block vectors have shown impressive accuracies on SPEC benchmarks, commercial programs remain a significant challenge due to their complex behaviors and multiple threads. Some behaviors, such as L2 cache misses, may have less correlation with the code and therefore are much harder to capture with basic block frequency vectors.Comparing the similarity of two or more intervals requires a good metric, one that is not only fast enough to analyze the full execution of the program, but that is also highly correlated with important performance degrading events (such as L2 misses). We examine the use of many different interval similarity metrics and their uses for program Phase analysis across a range of commercial applications and show that there is still significant room for improvement. To address this problem, we introduce a novel wavelet-based Phase Classification scheme that captures and compares images of memory behavior in two or more dimensions. Over a set of five commercial applications, we show that a wavelet-based scheme can strictly outperform a broad range of prior metrics both in terms of accuracy and overhead.

  • PACT - Wavelet-based Phase Classification
    Proceedings of the 15th international conference on Parallel architectures and compilation techniques - PACT '06, 2006
    Co-Authors: Ted Huffmire, Timothy Sherwood
    Abstract:

    Phase analysis has proven to be a useful method of summarizing the time-varying behavior of programs, with uses ranging from reducing simulation time to guiding run-time optimizations. Although Phase Classification techniques based on basic block vectors have shown impressive accuracies on SPEC benchmarks, commercial programs remain a significant challenge due to their complex behaviors and multiple threads. Some behaviors, such as L2 cache misses, may have less correlation with the code and therefore are much harder to capture with basic block frequency vectors.Comparing the similarity of two or more intervals requires a good metric, one that is not only fast enough to analyze the full execution of the program, but that is also highly correlated with important performance degrading events (such as L2 misses). We examine the use of many different interval similarity metrics and their uses for program Phase analysis across a range of commercial applications and show that there is still significant room for improvement. To address this problem, we introduce a novel wavelet-based Phase Classification scheme that captures and compares images of memory behavior in two or more dimensions. Over a set of five commercial applications, we show that a wavelet-based scheme can strictly outperform a broad range of prior metrics both in terms of accuracy and overhead.

John D. Corbett - One of the best experts on this subject based on the ideXlab platform.

Ted Huffmire - One of the best experts on this subject based on the ideXlab platform.

  • wavelet based Phase Classification
    International Conference on Parallel Architectures and Compilation Techniques, 2006
    Co-Authors: Ted Huffmire, Timothy Sherwood
    Abstract:

    Phase analysis has proven to be a useful method of summarizing the time-varying behavior of programs, with uses ranging from reducing simulation time to guiding run-time optimizations. Although Phase Classification techniques based on basic block vectors have shown impressive accuracies on SPEC benchmarks, commercial programs remain a significant challenge due to their complex behaviors and multiple threads. Some behaviors, such as L2 cache misses, may have less correlation with the code and therefore are much harder to capture with basic block frequency vectors.Comparing the similarity of two or more intervals requires a good metric, one that is not only fast enough to analyze the full execution of the program, but that is also highly correlated with important performance degrading events (such as L2 misses). We examine the use of many different interval similarity metrics and their uses for program Phase analysis across a range of commercial applications and show that there is still significant room for improvement. To address this problem, we introduce a novel wavelet-based Phase Classification scheme that captures and compares images of memory behavior in two or more dimensions. Over a set of five commercial applications, we show that a wavelet-based scheme can strictly outperform a broad range of prior metrics both in terms of accuracy and overhead.

  • PACT - Wavelet-based Phase Classification
    Proceedings of the 15th international conference on Parallel architectures and compilation techniques - PACT '06, 2006
    Co-Authors: Ted Huffmire, Timothy Sherwood
    Abstract:

    Phase analysis has proven to be a useful method of summarizing the time-varying behavior of programs, with uses ranging from reducing simulation time to guiding run-time optimizations. Although Phase Classification techniques based on basic block vectors have shown impressive accuracies on SPEC benchmarks, commercial programs remain a significant challenge due to their complex behaviors and multiple threads. Some behaviors, such as L2 cache misses, may have less correlation with the code and therefore are much harder to capture with basic block frequency vectors.Comparing the similarity of two or more intervals requires a good metric, one that is not only fast enough to analyze the full execution of the program, but that is also highly correlated with important performance degrading events (such as L2 misses). We examine the use of many different interval similarity metrics and their uses for program Phase analysis across a range of commercial applications and show that there is still significant room for improvement. To address this problem, we introduce a novel wavelet-based Phase Classification scheme that captures and compares images of memory behavior in two or more dimensions. Over a set of five commercial applications, we show that a wavelet-based scheme can strictly outperform a broad range of prior metrics both in terms of accuracy and overhead.

Zhen-chao Dong - One of the best experts on this subject based on the ideXlab platform.

Alejandro Castillo Atoche - One of the best experts on this subject based on the ideXlab platform.

  • Material Phase Classification by means of Support Vector Machines
    Computational Materials Science, 2018
    Co-Authors: Jaime Ortegon, Rene Ledesma-alonso, Romeli Barbosa, Javier Vázquez Castillo, Alejandro Castillo Atoche
    Abstract:

    Abstract The pixel’s Classification of images obtained from random heterogeneous materials (RHM) is a relevant step for 3D stochastic reconstruction and to compute their physical properties, like Effective Transport Coefficients (ETC). A bad Classification will impact on the computed properties. However, the literature on the topic discusses mainly the correlation functions or the properties formulae, giving little or no attention to the Classification; authors mention either the use of a threshold or, in few cases, the use of Otsu’s method. This paper presents a Classification approach based on Support Vector Machines (SVM) and a comparison with the Otsu-based approach, based on accuracy, precision and recall. The data used for the SVM training are the key for a better Classification; these data are the grayscale value, the magnitude and direction of pixels gradient. For the validation cases, the recall of the solid Phase is significantly better, whilst improving the accuracy for the SVM method. Finally, a discussion about the impact on the correlation functions is presented in order to show the benefits of the proposal.

  • Material Phase Classification by means of Support Vector Machines
    arXiv: Computational Physics, 2017
    Co-Authors: Jaime Ortegon, Rene Ledesma-alonso, Romeli Barbosa, Javier Vázquez Castillo, Alejandro Castillo Atoche
    Abstract:

    The pixel's Classification of images obtained from random heterogeneous materials is a relevant step to compute their physical properties, like Effective Transport Coefficients (ETC), during a characterization process as stochastic reconstruction. A bad Classification will impact on the computed properties; however, the literature on the topic discusses mainly the correlation functions or the properties formulae, giving little or no attention to the Classification; authors mention either the use of a threshold or, in few cases, the use of Otsu's method. This paper presents a Classification approach based on Support Vector Machines (SVM) and a comparison with the Otsu's-based approach, based on accuracy and precision. The data used for the SVM training are the key for a better Classification; these data are the grayscale value, the magnitude and direction of pixels gradient. For instance, in the case study, the accuracy of the pixel's Classification is 77.6% for the SVM method and 40.9% for Otsu's method. Finally, a discussion about the impact on the correlation functions is presented in order to show the benefits of the proposal.