There is provided a processor configured to transfer data to a plurality of processor circuits. The apparatus includes broadcast circuitry that broadcasts first machine learning data to at least a subset of the plurality of processor circuits.

Techniques for asynchronous handling of service requests are disclosed. A service receives a request from a requesting entity. The request includes a function identifier and function input. Responsive to receiving the message, the service selects a first event handler to process the request. The service translates, via the first event handler, the function identifier to a native function call. The service initiates execution of the native function call using the function input, and receives output corresponding to the execution of the native function call. Responsive to receiving the output, the service selects a second event handler to process the output. The service generates, at least in part by the second event handler, a response based on the output. The service transmits the response to the requesting entity.

Techniques for transforming plug-in application recipe (PIAR) variables are disclosed. A PIAR definition identifies a trigger and an action. Trigger variable values, exposed by a first plug-in application, are necessary to evaluate the trigger. Evaluating the trigger involves determining whether a condition is satisfied, based on values of trigger variables. A second plug-in application exposes an interface for carrying out an action. Evaluating the action involves carrying out the action based on input variable values. A user selects, via a graphical user interface of a PIAR management application, a variable for a trigger or action operation and a transformation operation to be applied to the variable. The PIAR management application generates a PIAR definition object defining the trigger, the action, and the transformation operation, and stores the PIAR definition object for evaluation on an ongoing basis.

Various systems, processes, and products may be used to filter multicast messages in virtual environments. In one implementation, a multicast filtering address is received by a network adapter from at least one of a number of virtual machines of a computer system. Responsive to receiving the multicast filtering address, a determination is made whether a multicast filtering store of the network adapter is full. Responsive to determining that the multicast filtering store of the network adapter is full, the multicast filtering address is stored in a local filtering store of the at least one virtual machine.

Various systems, processes, and products may be used to filter multicast messages in virtual environments. In one implementation, a multicast filtering address is received by a network adapter. A frequency of use of the multicast filtering address is determined and, based on the frequency of use of the multicast filtering address, the multicast filtering address is stored in either a multicast filtering store of the network adapter or a local filtering store of a respective virtual machine.

Various systems, processes, and products may be used to filter multicast messages in virtual environments. In one implementation, a multicast filtering address is received by a network adapter from at least one of a number of virtual machines. An amount of filtering data is determined corresponding to the at least one virtual machine and, based on the amount of the filtering data corresponding to the at least one virtual machine, the multicast filtering address is stored in either a multicast filtering store of the network adapter or a local filtering store of the at least one virtual machine.

Various systems, processes, and products may be used to filter multicast messages in virtual environments. In one implementation, a multicast filtering address is received by a network adapter from at least one of a number of virtual machines. A priority of the multicast filtering address is determined and, based on the priority, the multicast filtering address is stored in either a multicast filtering store of the network adapter or a local filtering store of the at least one virtual machine.

Various systems, processes, and products may be used to filter multicast messages in virtual environments. In one implementation, hardware resources are virtualized to provide a plurality of virtual machines where a number of the virtual machines are configured to receive multicast messages. A network adapter is configured to receive a multicast filtering address from at least one of the number of virtual machines and hash the multicast filtering address to create a hash value. The hash value is linked to the virtual machine via a memory entry.

The devices, systems, and methods described herein enable automatically configuring an electronic device using artificial intelligence (AI). The devices, systems, and methods enable accessing telemetry data representing device usage data, inputting the accessed telemetry data into machine learning models that are matched to device metadata, and determining notifications to publish to components of the electronic device. The notifications represent events predicted to occur on the electronic device. The notifications are published to the components of the electronic device such that the electronic device is configured according to the published notifications. The determined notifications enable the identification of optimal settings for the electronic device based on the usage pattern of the device and enable components of the electronic device to preemptively take action on events which are predicted to occur in the future.

The devices, systems, and methods described herein enable automatically configuring an electronic device using artificial intelligence (AI). The devices, systems, and methods enable accessing telemetry data representing device usage data, inputting the accessed telemetry data into machine learning models that are matched to device metadata, and determining notifications to publish to components of the electronic device. The notifications represent events predicted to occur on the electronic device. The notifications are published to the components of the electronic device such that the electronic device is configured according to the published notifications. The determined notifications enable the identification of optimal settings for the electronic device based on the usage pattern of the device and enable components of the electronic device to preemptively take action on events which are predicted to occur in the future.