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公开(公告)号：US20150261569A1
公开(公告)日：2015-09-17
申请号：US14208257
申请日：2014-03-13
发明人： Deanna Postles Dunn Berger , Kathryn M. Jackson , Joshua D. Massover , Gary E. Strait , Hanno Ulrich , Craig R. Walters
IPC分类号： G06F9/48
CPC分类号： G06F9/4881 , G06F11/30 , G06F11/3024 , G06F11/3055 , G06F11/3409 , G06F11/3476 , G06F11/348
摘要： Embodiments are directed to systems and methodologies for efficiently sampling data for analysis by a pipeline analysis algorithm. The amount of sampled data is maximized without increasing sampling overhead by sampling “non-pipeline activity” data if the subject pipeline is inactive during the sampling time. The non-pipeline activity data is selected to include overall system information that is relevant to the subject pipeline's performance but is not necessarily dependent on whether the subject pipeline is active. In some embodiments, the non-pipeline activity data allows for confirmation of a pipeline performance characteristic that must otherwise be inferred by the subsequent pipeline analysis algorithm from data sampled while the pipeline was active. In some embodiments, the non-pipeline activity data allows the pipeline analysis algorithm to analyze additional performance characteristics that cannot otherwise be inferred from the data sampled while the pipeline was active.
摘要翻译： 实施例涉及用于通过流水线分析算法有效地采样数据进行分析的系统和方法。 如果在采样时间内主体管线不活动，则采样数据的数量最大化，而不会通过采样“非流水线活动”数据而增加采样开销。 选择非流水线活动数据以包括与主体管线的性能相关的整体系统信息，但不一定取决于主体流水线是否活动。 在一些实施例中，非流水线活动数据允许确认流水线性能特征，否则在流水线活动时，必须由随后的流水线分析算法从采样的数据中推断。 在一些实施例中，非流水线活动数据允许流水线分析算法分析在流水线处于活动状态时所采样的数据不能被推断的附加性能特征。
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公开(公告)号：US10176013B2
公开(公告)日：2019-01-08
申请号：US14208257
申请日：2014-03-13
发明人： Deanna Postles Dunn Berger , Kathryn M. Jackson , Joshua D. Massover , Gary E. Strait , Hanno Ulrich , Craig R. Walters
摘要： Embodiments are directed to systems and methodologies for efficiently sampling data for analysis by a pipeline analysis algorithm. The amount of sampled data is maximized without increasing sampling overhead by sampling “non-pipeline activity” data if the subject pipeline is inactive during the sampling time. The non-pipeline activity data is selected to include overall system information that is relevant to the subject pipeline's performance but is not necessarily dependent on whether the subject pipeline is active. In some embodiments, the non-pipeline activity data allows for confirmation of a pipeline performance characteristic that must otherwise be inferred by the subsequent pipeline analysis algorithm from data sampled while the pipeline was active. In some embodiments, the non-pipeline activity data allows the pipeline analysis algorithm to analyze additional performance characteristics that cannot otherwise be inferred from the data sampled while the pipeline was active.
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公开(公告)号：US20150261533A1
公开(公告)日：2015-09-17
申请号：US14501190
申请日：2014-09-30
发明人： Deanna Postles Dunn Berger , Kathryn M. Jackson , Joshua D. Massover , Gary E. Strait , Hanno Ulrich , Craig R. Walters
IPC分类号： G06F9/30
CPC分类号： G06F9/4881 , G06F11/30 , G06F11/3024 , G06F11/3055 , G06F11/3409 , G06F11/3476 , G06F11/348
摘要： Embodiments are directed to methodologies for efficiently sampling data for analysis by a pipeline analysis algorithm. The amount of sampled data is maximized without increasing sampling overhead by sampling “non-pipeline activity” data if the subject pipeline is inactive during the sampling time. The non-pipeline activity data is selected to include overall system information that is relevant to the subject pipeline's performance but is not necessarily dependent on whether the subject pipeline is active. In some embodiments, the non-pipeline activity data allows for confirmation of a pipeline performance characteristic that must otherwise be inferred by the subsequent pipeline analysis algorithm from data sampled while the pipeline was active. In some embodiments, the non-pipeline activity data allows the pipeline analysis algorithm to analyze additional performance characteristics that cannot otherwise be inferred from the data sampled while the pipeline was active.
摘要翻译： 实施例涉及用于通过流水线分析算法有效地采样数据进行分析的方法。 如果在采样时间内主体管线不活动，则采样数据的数量最大化，而不会通过采样“非流水线活动”数据而增加采样开销。 选择非流水线活动数据以包括与主体管线的性能相关的整体系统信息，但不一定取决于主体流水线是否活动。 在一些实施例中，非流水线活动数据允许确认流水线性能特征，否则在流水线活动时，必须由随后的流水线分析算法从采样的数据中推断。 在一些实施例中，非流水线活动数据允许流水线分析算法分析在流水线处于活动状态时所采样的数据不能被推断的附加性能特征。
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公开(公告)号：US10572304B2
公开(公告)日：2020-02-25
申请号：US14501190
申请日：2014-09-30
发明人： Deanna Postles Dunn Berger , Kathryn M. Jackson , Joshua D. Massover , Gary E. Strait , Hanno Ulrich , Craig R. Walters
摘要： Embodiments are directed to methodologies for efficiently sampling data for analysis by a pipeline analysis algorithm. The amount of sampled data is maximized without increasing sampling overhead by sampling “non-pipeline activity” data if the subject pipeline is inactive during the sampling time. The non-pipeline activity data is selected to include overall system information that is relevant to the subject pipeline's performance but is not necessarily dependent on whether the subject pipeline is active. In some embodiments, the non-pipeline activity data allows for confirmation of a pipeline performance characteristic that must otherwise be inferred by the subsequent pipeline analysis algorithm from data sampled while the pipeline was active. In some embodiments, the non-pipeline activity data allows the pipeline analysis algorithm to analyze additional performance characteristics that cannot otherwise be inferred from the data sampled while the pipeline was active.
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