An industrial integrated development environment (IDE) provides a development framework for designing, programming, and configuring multiple aspects of an industrial automation system using a common design environment and data model. Projects creating using embodiments of the IDE system can be built on an object-based model rather than, or in addition to, a tag-based architecture. To this end, the IDE system can support the use of automation objects that serve as building blocks for this object-based development structure. These automation objects represent corresponding physical industrial assets and have associated programmatic attributes relating to those assets, including data logging and device configuration parameters. Functional relationships between automation objects can be defined to yield object hierarchies, and object attributes can be propagated across objects up and down the hierarchy.

Embodiments described herein are generally directed to improving predictions regarding workload performance to facilitate dynamic auto device selection. In an example, based on telemetry samples collected from a computer system in real-time and indicative of a state of the computer system, one or more workload performance prediction models are built or updated for a heterogeneous set of computer resources of the computer system with reference to one or more optimization goals. At a time of execution of a workload, a particular computer resource of the heterogeneous set of computer resources on which to dispatch the workload is dynamically determined by: (i) generating multiple predicted performance scores each corresponding to one of the computer resources based on the state of the computer system and the one or more workload performance prediction models; and (ii) selecting the particular computer resource based on the predicted performance scores.

A computing device is coupled to a display device, and includes a data monitoring software application program executing on a processor within a data monitoring system. Via the data monitoring software application program, various techniques are performed for generating user interfaces for data monitoring and event response. In a first technique, the data monitoring software application program displays a user interface that includes a first region including a data visualization and a second region including one or more images of a video stream. In a second technique, the data monitoring software application program generates a user interface associated with an event, receive an input corresponding to interaction with a user interface element in the user interface, and initiates an event channel associated with the event in response to the input.

In one embodiment, a system for managing a virtualization environment includes a set of host machines, each of which includes a hypervisor, virtual machines, and a virtual machine controller, and a data migration system configured to identify one or more existing storage items stored at one or more existing File Server Virtual Machines (FSVMs) of an existing virtualized file server (VFS). For each of the existing storage items, the data migration system is configured to identify a new FSVMs of a new VFS based on the existing FSVM, send a representation of the storage item from the existing FSVM to the new FSVM, such that representations of storage items are sent between different pairs of FSVMs in parallel, and store a new storage item at the new FSVM, such that the new storage item is based on the representation of the existing storage item received by the new FSVM.

In one embodiment, a method for margin determination for a computing system with a real time operating system and priority preemptive scheduling comprises: scheduling a set of tasks to be executed in one or more partitions, wherein each is assigned a priority, wherein the tasks comprise periodic and/or aperiodic tasks; executing the set of tasks on the computing system within the scheduled periodic time window; introducing an overhead task executed for an execution duration controlled either by the real time operating system or by the overhead task; controlling the overhead task to converge on a point of failure at which a length of the execution duration of the overhead task causes either: 1) a periodic task to fail to execute within a deadline, or 2) time available for the aperiodic tasks to execute to fall below a threshold; and defining a partition margin corresponding to the point of failure.

Systems, apparatuses, and methods for dynamic graphics processing unit (GPU) register allocation are disclosed. A GPU includes at least a plurality of compute units (CUs), a control unit, and a plurality of registers for each CU. If a new wavefront requests more registers than are currently available on the CU, the control unit spills registers associated with stack frames at the bottom of a stack since they will not likely be used in the near future. The control unit has complete flexibility determining how many registers to spill based on dynamic demands and can prefetch the upcoming necessary fills without software involvement. Effectively, the control unit manages the physical register file as a cache. This allows younger workgroups to be dynamically descheduled so that older workgroups can allocate additional registers when needed to ensure improved fairness and better forward progress guarantees.

A computer-implemented system for dynamic aggregation of data and minimization of data loss is disclosed. The system may be configured to perform instructions for: aggregating information from a plurality of networked systems by collecting a set of data from the networked systems, the set of data comprising data associated with a predetermined period of time and comprising one or more central variables that are included in data associated with more than one networked systems of the plurality of networked systems and one or more associated variables that describe one or more aspects of the central variables; retrieving one or more data transformation rules based on a relational map among the central variables and the associated variables; and aggregating the first set of data into one or more master data structures corresponding to the central variables based on the data transformation rules.

A method for execution by a query processing system includes determining a query request that indicates a plurality of operators, where the plurality of operators includes at least one relational algebra operator and further includes at least one non-relational operator. A query operator execution flow is generated from the query request that indicates a serialized ordering of the plurality of operators. A query resultant of the query is generated by facilitating execution of the query via a set of nodes of a database system that each perform a plurality of operator executions in accordance with the query operator execution flow, where a subset of the set of nodes each execute at least one operator execution corresponding to the at least one non-relational operator in accordance with the execution of the query.

Techniques for optimizing CPU usage in a host system based on VM guest OS power and performance management are provided. In one embodiment, a hypervisor of the host system can capture information from a VM guest OS that pertains to a target power or performance state set by the guest OS for a vCPU of the VM. The hypervisor can then perform, based on the captured information, one or more actions that align usage of host CPU resources by the vCPU with the target power or performance state.

A memory device comprises a memory control unit including a processor configured to control operation of the memory array according to a first memory management protocol for memory access operations, the first memory management protocol including boundary conditions for multiple operating conditions comprising program/erase (P/E) cycles, error management operations, drive writes per day (DWPD), and power consumption; monitor operating conditions of the memory array for the P/E cycles, error management operations, DWPD, and power consumption; determine when a boundary condition for one of the multiple operating conditions is met; and in response to determining that a first boundary condition for a first monitored operating condition is met, change one or more operating conditions of the first memory management protocol to establish a second memory management protocol for the memory access operations, the second memory management protocol including a change boundary condition of a second monitored operating condition.