Abstract: A network is crawled using a trained learning model to identify a set of secondary-source documents related to an event. A hub page from the set of secondary-source documents is identified that includes a link predicted to link to a new relevant secondary-source document. The new document is added to the set of secondary-source documents. Information is extracted from the set of secondary-source documents. Feedback is received indicative of a relevancy level for the extracted information as applied to the event. Each document is classified into one or more categories related to the event, based on the extracted information and the received feedback information. A learning model is trained based on the received feedback.

Abstract: A computer system, method, and computer readable product are provided for event-based serverless computing with tiered latency. In various embodiments, computer-executable instructions are received along with an indication of an event whose occurrence triggers execution of those instructions within a container on a serverless computing platform. Additionally, the instructions come with an indication of an acceptable amount of latency between the event occurring and the instructions being executed. Multiple different actions may be made to execute the instructions based on that indicated latency.

Abstract: A network is crawled using a trained learning model to identify a set of secondary-source documents related to an event. A hub page from the set of secondary-source documents is identified that includes a link predicted to link to a new relevant secondary-source document. The new document is added to the set of secondary-source documents. Information is extracted from the set of secondary-source documents. Feedback is received indicative of a relevancy level for the extracted information as applied to the event. Each document is classified into one or more categories related to the event, based on the extracted information and the received feedback information. A learning model is trained based on the received feedback.

Abstract: Predicting program performance on hardware devices, in one aspect, may comprise obtaining a set of existing applications and observed performance on a target hardware device. The set of existing applications are run on one or more general purpose computer processors and application features are extracted from the existing application. A machine learning technique is employed to train a predictive model based on the extracted application features and the observed performance for predicting application performance on the target hardware device.

Abstract: A computer system, method, and computer readable product are provided for event-based serverless computing with tiered latency. In various embodiments, computer-executable instructions are received along with an indication of an event whose occurrence triggers execution of those instructions within a container on a serverless computing platform. Additionally, the instructions come with an indication of an acceptable amount of latency between the event occurring and the instructions being executed. Multiple different actions may be made to execute the instructions based on that indicated latency.

Abstract: Predicting program performance on hardware devices, in one aspect, may comprise obtaining a set of existing applications and observed performance on a target hardware device. The set of existing applications are run on one or more general purpose computer processors and application features are extracted from the existing application. A machine learning technique is employed to train a predictive model based on the extracted application features and the observed performance for predicting application performance on the target hardware device.

Abstract: Predicting program performance on hardware devices, in one aspect, may comprise obtaining a set of existing applications and observed performance on a target hardware device. The set of existing applications are run on one or more general purpose computer processors and application features are extracted from the existing application. A machine learning technique is employed to train a predictive model based on the extracted application features and the observed performance for predicting application performance on the target hardware device.

Abstract: Embodiments relate to pre-computation slice (p-slice) merging for prefetching in a computer processor. An aspect includes determining a plurality of p-slices corresponding to a delinquent instruction. Another aspect includes selecting a first p-slice and a second p-slice of the plurality of p-slices. Another aspect includes traversing the first p-slice and the second p-slice to determine that divergent instructions exist between the first p-slice and the second p-slice. Another aspect includes, based on determining that divergent instructions exist between the first p-slice and the second p-slice, determining whether the first p-slice and the second p-slice converge after the divergent instructions. Another aspect includes, based on determining that the first p-slice and the second p-slice converge after the divergent instructions, merging the first p-slice and the second p-slice into a single merged p-slice.

Abstract: Predicting program performance on hardware devices, in one aspect, may comprise obtaining a set of existing applications and observed performance on a target hardware device. The set of existing applications are run on one or more general purpose computer processors and application features are extracted from the existing application. A machine learning technique is employed to train a predictive model based on the extracted application features and the observed performance for predicting application performance on the target hardware device.