A computer-implemented data processing method, including the steps of: providing a first program including a group of operations arranged to satisfy a first set of operation dependencies, the group of operations being adapted for computing data from at least one data source, generating a second program including the group of operations, arranged to satisfy a second set of operation dependencies, and processing the data from the at least one data source with the second program. The group of operations includes a first operation, a second operation, and a third operation. The first set of operation dependencies includes a first dependency between the first operation and the second operation, a second dependency between the first operation and the third operation, and a third dependency between the second operation and the third operation.

A processor includes a task scheduling unit and a compute unit coupled to the task scheduling unit. The task scheduling unit performs a task dependency assessment of a task dependency graph and task data requirements that correspond to each task of the plurality of tasks. Based on the task dependency assessment, the task scheduling unit schedules a first task of the plurality of tasks and a second proxy object of a plurality of proxy objects specified by the task data requirements such that a memory transfer of the second proxy object of the plurality of proxy objects occurs while the first task is being executed.

Described are techniques for scheduling tasks on a heterogeneous system on a chip (SoC). The techniques including receiving a directed acyclic graph at a meta pre-processor associated with a heterogeneous SoC and communicatively coupled to a scheduler, wherein the directed acyclic graph corresponds to a control flow graph of tasks associated with an application executed by the heterogeneous SoC. The techniques further including determining a rank for a respective task in the directed acyclic graph, wherein the rank is based on a priority of the respective task and a slack in the directed acyclic graph. The techniques further including providing the respective task to the scheduler for execution on the heterogeneous SoC according to the rank.

The exemplary embodiments are related to a device, a system, and a method for implementing a hardware mechanism that is configured to validate the performance of scheduling software utilized by a safety-critical system. The hardware device may receive an indication that a first frame of a frame schedule is in use. The hardware device may also monitor a time parameter corresponding to the first frame. The hardware device may also determine whether an indication that a second frame of the frame schedule is in use is received prior to the expiration of the time parameter. When the indication that the second frame of the frame scheduler is in use is not received prior to the expiration of time parameter, the hardware device may send a signal to an operating system of the safety-critical system indicating that an error in executing the frame scheduled has occurred.

Embodiments of an activities-defined software object execution management platform include instantiation of a program based on a program configuration, including customizable scheduling configurations and execution steps of program stages. A current state of the program is received from a state persistence storage. A stage configuration of the current stage is configured. A program execution readiness is determined to identify when to execute the current stage of the program based on an execution configuration and program-specific parameterized values. The current stage is instantiated based on the program execution readiness. An execution status of the stage is determined based on a validation configuration. A previous stage is determined to rollback before the current stage based on the execution status and a rollback configuration. The current state is updated in the persistent storage based on the execution of the state step to form a subsequent state of the program.

Techniques for scheduling operations for a task graph on a processing device are provided. The techniques include receiving a task graph that specifies one or more passes, one or more resources, and one or more directed edges between passes and resources; identifying independent passes and dependent passes of the task graph; based on performance criteria of the processing device, scheduling commands to execute the passes; and transmitting scheduled commands to the processing device for execution as scheduled.

A method, which may be performed by a computing system, involves determining that a plurality of notifications, including a first notification, is to be sent to a first client device, the first notification indicating a first task that is to be performed with respect to a resource accessible to the computing system; determining that a second task has a dependency relationship with the first task; determining at least one first parameter relating to the first task and at least one second parameter relating to the second task; determining, based at least in part on the at least one first parameter and the at least one second parameter, a first priority score corresponding to the first notification; and causing the plurality of notifications to be presented by the first client device in an order that is determined based at least in part on the first priority score.

Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; scheduling a job for the first API request using a global scheduler, the global scheduler to schedule, based at least in part on available bandwidth of processing components including a segmenter, a chunk processor, and a reducer, at least one job queue associated at least one of the processing components; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.

Disclosed is a task allocation method, apparatus, electronic device, and computer-readable storage medium. The task allocation method includes: in response to receiving a synchronization signal, executing, by the master processing core, a task update instruction to obtain a to-be-executed task segment; receiving, by a processing core for executing the task, the to-be-executed task segment, wherein the processing core for executing the task includes the master processing core and/or the slave processing core; executing, by the processing core for executing the task, the to-be-executed task segment; and in response to completion of execution of the to-be-executed task segment, sending, by the processing core for executing the task, a synchronization request signal, wherein the synchronization request signal is configured to trigger generation of the synchronization signal.

An electronic device and storage medium for process scheduling are provided. The electronic device includes a memory and at least one processor, wherein the memory stores instructions to enable the at least one processor to execute a host operating system (OS) and at least one virtual machine, and wherein the host OS is configured to receive information for at least one process from the virtual machine, allocate at least one core to the virtual machine based on the information for the at least one process, and provide, to the virtual machine, information related to allocation of the at least one core. Other various embodiments are possible as well.