Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.