Methods, apparatus, systems, and computer-readable media for engaging an automated assistant to perform multiple tasks through a multitask command. The multitask command can be a command that, when provided by a user, causes the automated assistant to invoke multiple different agent modules for performing tasks to complete the multitask command. During execution of the multitask command, a user can provide input that can be used by one or more agent modules to perform their respective tasks. Furthermore, feedback from one or more agent modules can be used by the automated assistant to dynamically alter tasks in order to more effectively use resources available during completion of the multitask command.

In some examples, a system receives a first unit of work to be scheduled in the system that includes a plurality of collections of processing units to execute units of work, where each respective collection of processing units of the plurality of collections of processing units is associated with a corresponding scheduling queue. The system selects, for the first unit of work according to a first criterion, candidate collections from among the plurality of collections of processing units, and enqueues the first unit of work in a schedule queue associated with a selected collection of processing units that is selected, according to a selection criterion, from among the candidate collections.

Integration of an external calendar application with a task facilitation service includes mechanisms for creating tasks within the task facilitation service based on calendar data of the calendar application received by the task facilitation service and processed using various dynamic models and algorithms. Further examples of integration include the task facilitation service generating recommendations for new calendar items and modifications to existing calendar items by leveraging the data and models available to the task facilitation service.

Approaches for scheduling a set of tasks at compute nodes within a cluster computing environment based on a priority, are described, In an example, a cascaded priority mapping comprising cascaded priority value nodes, wherein the priority value nodes correspond to the set of tasks that are to be scheduled. Each of the priority value nodes specify a priority value attributed to respective tasks from amongst the set of tasks.

Disclosed techniques relate to distributing graphics work based on priority. In some embodiments, circuitry implements a plurality of tracking slots for sets of graphics work. A set of graphics processor sub-units may each implement multiple distributed hardware slots. Control circuitry may attempt to assign a first set of graphics work having a first priority to a graphics processor sub-unit that is currently executing graphics work having an equal or higher priority than the first priority, where the first set of graphics work is from a first tracking slot. The control circuitry may, in response to a failure of the attempt, generate a signal to graphics software that indicates the failure, wherein the signal indicates the first tracking slot. Disclosed techniques may reduce or avoid problems relating to higher priority work being scheduled behind lower priority work.

Implementations for scheduling a sub-idle thread priority class are described. An example method may include assigning, by a scheduler of a computer system, a sub-idle execution priority class to a processing thread associated with a request queue of an input/output (I/O) device; identifying, by a processing device, a work completion request in the request queue; and responsive to predicting, for a processor of the computer system, an idle time exceeding a threshold idle period, running the processing thread.

Disclosed herein is a method implemented by a task mining engine. The task mining engine is stored as processor executable code on a memory. The processor executable code is executed by a processor that is communicatively coupled to the memory. The method includes receiving recorded tasks identifying user activity with respect to a computing environment and clustering the recorded user tasks into steps by processing and scoring each recorded user task. The method also includes extracting step sequences that identify similar combinations or repeated combinations of the steps to mimic the user activity.

A method for access to the shared resources of a computer platform including a multicore processor, shared resources between first partitions according to which requests to access the shared resources emitted by the first partitions are sent to a second partition that, during its execution on the processor, performs said accesses; multiple cores are reserved synchronously for the execution of the second access partition during a predetermined time; the separate accesses to separate shared resources done by the second partition having to be done by separate reserved cores; and all of the accesses to a shared resource done by the second access partition having to be executed, during said predetermined time, by a single core among the reserved cores.

Systems and methods are provided for displaying messages. The systems and methods include operations for: receiving, by a client device, a plurality of events from a plurality of channels; receiving, by the client device from a server, a configuration file comprising one or more rules for processing the sets of events from the plurality of channels; determining that a first event of the plurality of events is associated with a first event type and that a second event of the plurality of events is associated with a second event type; assigning, based on the configuration file, a first priority to the first event based on the first event type and a second priority to the second event based on the second event type; and displaying, by the client device, the first event and the second event according to the first priority and the second priority.

Integration of an external productivity/task management application with a task facilitation service includes mechanisms for creating tasks within the task facilitation service based on task list data of the task management application received by the task facilitation service and processed using various dynamic models and algorithms. Further examples of integration include the task facilitation service generating recommendations for new task list items and modifications to existing task list items by leveraging the data and models available to the task facilitation service.