Abstract: Disclosed are embodiments of a installed software program that receive a model from a product management system. The model is trained to select one of a plurality of predefined states based on operational parameter values of the installation of the software program. Each of the plurality of predefined states define configuration values of the installation of the software program. The defined configuration values indicate, in some embodiments, updates to operational parameter values of the installation of the software program.

Abstract: Disclosed are embodiments of a installed software program that receive a model from a product management system. The model is trained to select one of a plurality of predefined states based on operational parameter values of the installation of the software program. Each of the plurality of predefined states define configuration values of the installation of the software program. The defined configuration values indicate, in some embodiments, updates to operational parameter values of the installation of the software program.

Abstract: Disclosed are embodiments of a installed software program that receive a model from a product management system. The model is trained to select one of a plurality of predefined states based on operational parameter values of the installation of the software program. Each of the plurality of predefined states define configuration values of the installation of the software program. The defined configuration values indicate, in some embodiments, updates to operational parameter values of the installation of the software program.

Abstract: Systems and methods for parallelization of machine learning computing code are described herein. In one aspect, embodiments of the present disclosure include a method of generating a plurality of instruction sets from machine learning computing code for parallel execution in a multi-processor environment, which may be implemented on a system, of, partitioning training data into two or more training data sets for performing machine learning, identifying a set of concurrently-executable tasks from the machine learning computing code, assigning the set of tasks to two or more of the computing elements in the multi-processor environment, and/or generating the plurality of instruction sets to be executed in the multi-processor environment to perform a set of processes represented by the machine learning computing code.

Abstract: Systems and methods for architecture-adaptable automatic parallelization of computing code are described herein. In one aspect, embodiments of the present disclosure include a method of generating a plurality of instruction sets from a sequential program for parallel execution in a multi-processor environment, which may be implemented on a system, of, identifying an architecture of the multi-processor environment in which the plurality of instruction sets are to be executed, determining running time of each of a set of functional blocks of the sequential program based on the identified architecture, determining communication delay between a first computing unit and a second computing unit in the multi-processor environment, and/or assigning each of the set of functional blocks to the first computing unit or the second computing unit based on the running times and the communication time.

Abstract: Systems and methods for determining related node failures in a multi-node system use log data obtained from the nodes. This log data is processed in various ways to indicate clusters of nodes that experience related failures.

Abstract: Systems and methods for determining related node failures in a multi-node system use log data obtained from the nodes. This log data is processed in various ways to indicate clusters of nodes that experience related failures.