A job scheduler system includes one or more hardware processors, a memory including a job group queue stored in the memory, and a job scheduler engine configured to create a first job group in the job group queue, the first job group includes a generation counter having an initial value, receive a first request to steal the first job group, determine a state of the first job group based at least in part on the generation counter, the state indicating that the first job group is available to steal, based on the determining the state of the first job group, atomically increment the generation counter, thereby making the first job group unavailable for stealing, and alter an execution order of the first job group ahead of at least one other job group in the job group queue.

The present subject matter relates to a method comprising acquiring a master role by a processing unit of a multi-processor system, executing by the processing unit a master function part of a set of tasks: comprising searching an available processing unit of the multi-processor system; wherein in case an available processing unit is found, controlling the found processing unit to perform a slave function part of the set of tasks, and in case no available processing unit is found, executing by the processing unit the slave function part of the set of tasks, wherein the master function comprises a master to slave switching function for releasing the master role and the slave function composes a slave to master switching function for acquiring the master role.

A data processing method implemented by a computer device, includes generating a target task including a buffer application task or a buffer release task, when the target task is the buffer application task, a first buffer corresponding to the buffer application task is used when the second task is executed, or when the target task is the buffer release task, a second buffer corresponding to the buffer release task is used when the first task is executed, obtaining a buffer entry corresponding to the target task after a preceding task of the target task is executed and before a successive task of the target task is executed, where the buffer entry includes a memory size of a buffer corresponding to the target task, a memory location of the buffer, and a memory address of the buffer, and executing the target task to apply for or release the buffer.

An exemplary artificial intelligence/machine learning hardware computing environment having an exemplary DNN module cooperating with one or more memory components can perform data sharing and distribution as well reuse of a buffer data to reduce the number of memory component read/writes thereby enhancing overall hardware performance and reducing power consumption. Illustratively, data from a cooperating memory component is read according to a selected operation of the exemplary hardware and written to corresponding other memory component for use by one or more processing elements (e.g., neurons). The data is read in such a manner to optimize the engagement of the one or more processing elements for each processing cycle as well as to reuse data previously stored in the one or more cooperating memory components. Operatively, the written data is copied to a shadow memory buffer prior to being consumed by the processing elements.

Implementations of this specification include identifying a plurality of transactions to be executed in the blockchain, wherein the transactions are arranged in an execution order, wherein the transactions include one or more smart contract calls to smart contracts each having a whitelist identifying one or more accounts that are authorized to execute the smart contract, and wherein the execution order includes a smart contract call to a smart contract that does not have a whitelist arranged after the plurality of transactions; identifying groups of transactions within the plurality of transactions; instructing nodes of the blockchain network to execute each of the groups of transactions in parallel; determining that the nodes of the blockchain network have completed executing all of the groups of transactions; and in response, instructing the nodes of the blockchain network to execute the smart contract call that does not include a whitelist.

The present disclosure provides a method for task orchestrating, an orchestrator, a device and a readable storage medium. According to the method provided in the present disclosure, task scripts are edited as descriptive language scripts, and data dependency relationships between source tables and target tables in the tasks corresponding to the task scripts are automatically generated according to the descriptive language scripts of the tasks. The execution of the tasks is driven in a data-driven manner according to the data dependency relationships between the source tables and the target tables in the tasks. There is no need for a technical person to manually edit the workflow file. When some tasks change, the data dependency relationships between the source and target tables in the tasks can be automatically updated according to the task scripts, without affecting the orchestration process between tasks.

Edge device task management is provided. It is determined whether a subtask cancel and migrate plan exists when a request to run a higher priority subtask of a second plurality of subtasks corresponding to a second task is received while a first task comprised of a first plurality of subtasks is running on a first cluster of edge devices. In response to determining that the subtask cancel and migrate plan does exist, a lower priority subtask of the first plurality of subtasks is canceled from a designated edge device of the first cluster of edge devices based on the subtask cancel and migrate plan. The lower priority subtask is migrated to another edge device for running based on the subtask cancel and migrate plan. The higher priority subtask of the second plurality of subtasks is sent to the designated edge device of the first cluster of edge devices for running.

Embodiments described herein provide multi-level scheduling for threads in a data processing system. One embodiment provides a data processing system comprising one or more processors, a computer-readable memory coupled to the one or more processors, the computer-readable memory to store instructions which, when executed by the one or more processors, configure the one or more processors to receive execution threads for execution on the one or more processors, map the execution threads into a first plurality of buckets based at least in part on a quality of service class of the execution threads, schedule the first plurality of buckets for execution using a first scheduling algorithm, schedule a second plurality thread groups within the first plurality of buckets for execution using a second scheduling algorithm, and schedule a third plurality of threads within the second plurality of thread groups using a third scheduling algorithm.

A deep neural network (“DNN”) module can determine whether processing of certain values in an input buffer or a weight buffer by neurons can be skipped. For example, the DNN module might determine whether neurons can skip the processing of values in entire columns of a neuron buffer. Processing of these values might be skipped if an entire column of an input buffer or a weight buffer are zeros, for example. The DNN module can also determine whether processing of single values in rows of the input buffer or the weight buffer can be skipped (e.g. if the values are zero). Neurons that complete their processing early as a result of skipping operations can assist other neurons with their processing. A combination operation can be performed following the completion of processing that transfers the results of the processing operations performed by a neuron to their correct owner.

Systems and processes for user configurable task triggers are provided. In one example, at least one user input, including a selection of at least one condition of a plurality of conditions and a selection of at least one task of a plurality of tasks, is received. Stored context data corresponding to an electronic device is received. A determination is whether the stored context data indicates an occurrence of the at least one selected condition. In response to determining that the stored context data indicates an occurrence of the at least one selected condition, the at least one selected task associated with the at least one selected condition is performed.