Apparatus and method for managing pipeline depth of a data processing device. For example, one embodiment of an apparatus comprises: an interface to receive a plurality of work requests from a plurality of clients; and a plurality of engines to perform the plurality of work requests; wherein the work requests are to be dispatched to the plurality of engines from a plurality of work queues, the work queues to store a work descriptor per work request, each work descriptor to include information needed to perform a corresponding work request, wherein the plurality of work queues include a first work queue to store work descriptors associated with first latency characteristics and a second work queue to store work descriptors associated with second latency characteristics; engine configuration circuitry to configure a first engine to have a first pipeline depth based on the first latency characteristics and to configure a second engine to have a second pipeline depth based on the second latency characteristics.

The present disclosure relates to resource scheduling methods and apparatuses. In one example method, a scheduling node receives a task. The scheduling node obtains a target execution duration level to which the task belongs, where the target execution duration level represents a time length, and the target execution duration level indicates to use a target compute module of a target compute node in multiple compute nodes to execute the task. The scheduling node sends the task to the target compute node.

Systems and systems for hybrid centralized distributive scheduling and conflict resolution of multiple scheduler instances that share physical resources in a cloud computing system. The cloud computing system includes a plurality of scheduler instances, a global resource manager (GRM) for high-level resource management and conflict resolution for the scheduler instances, and a plurality of physical hosts. Each physical host has a respective local resource manager (LRM). The scheduler instances are responsible for initially processing of scheduling and resource allocation for resource requests, and proposing candidate physical hosts (and respective resource allocation) for the resource requests to the GRM. The GRM is responsible for conflict resolution through its general conflict resolvers of filtering, sorting and counting. The GRM decides which physical hosts among the candidate physical hosts will run the runtime instances of the resource requests after resolving conflicts among the scheduler instances.

An app migration system including at least one processor which places an app in one of an inside and an outside of a space joined by at least one user in a user group in which information is shareable; sets, for the app, a permission corresponding to a placement location of the app; migrates the app in one of a route between a public space and a private space and a route between the inside and the outside of the space; and sets, for the migrated app, a permission corresponding to a migration destination of the app.

A query operation is performed to obtain information for a select entity of a computing environment. The information includes boost information of one or more boost features currently available for the select entity. The one or more boost features are to be used to temporarily adjust one or more processing attributes of the select entity. The boost information obtained from performing the query operation is provided in an accessible location to be used to perform one or more actions to facilitate processing in the computing environment.

In various examples, a VPU and associated components may be optimized to improve VPU performance and throughput. For example, the VPU may include a min/max collector, automatic store predication functionality, a SIMD data path organization that allows for inter-lane sharing, a transposed load/store with stride parameter functionality, a load with permute and zero insertion functionality, hardware, logic, and memory layout functionality to allow for two point and two by two point lookups, and per memory bank load caching capabilities. In addition, decoupled accelerators may be used to offload VPU processing tasks to increase throughput and performance, and a hardware sequencer may be included in a DMA system to reduce programming complexity of the VPU and the DMA system. The DMA and VPU may execute a VPU configuration mode that allows the VPU and DMA to operate without a processing controller for performing dynamic region based data movement operations.

A system of monitoring and optimizing computing resources usage for computing application may include predicting a first performance metric for job load capacity of a computing application for optimal job concurrency and optimal resource utilization. The system may include generating an alerting threshold based on the first performance metric. The system may further include, in response to a difference between the alerting threshold and a job load of the computing application within an interval exceeding a threshold, predicting a second performance metric for job load capacity of the computing application for optimal job concurrency and optimal resource utilization. The system may further include, in response to a difference between the first performance metric and the second performance metric exceeding a difference threshold, updating the alerting threshold with a job load capacity with the optimal resource utilization rate corresponding to the second performance metric.

A method of generating an instruction performance skeleton employs an instruction unit configured to receive a natural language instruction. From the natural language instruction, a sequence of clauses may be extracted. The instruction unit then determines a target website or websites on which to perform the task. The object models of the target website are generated. A comparison of the sequence of actions to the object model and its labelling hierarchical class structure is performed. Based on this comparison, an instruction performance skeleton is generated. In future, on the basis of a further natural language instruction that is similar to the previous natural language instruction, the instruction performance skeleton may be modified to generate a playback performance skeleton to arrange performance of a task.

In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may determine first thermal attribute values associated with multiple information handling systems (IHSs) with respect to a period of time as the IHSs execute a first workload; determine multiple variance ranges respectively associated with the first thermal attributes; periodically determine second thermal attribute values associated with the IHSs as the IHSs execute a second workload; determine that a thermal attribute value of the second thermal attribute values exceeds a respective variance range of the variance ranges as a first information handling system (IHS) of the IHSs executes the second workload; generate an alert based at least on the thermal attribute value exceeding the respective variance range; and in response to the alert, transfer at least a portion of the second workload from the first IHS to a second IHS of the IHSs.

An apparatus includes a plurality of processing cores, and a memory including a plurality of task queues corresponding to the plurality of processing cores, respectively, wherein at least one processing core of the plurality of processing cores is configured, by executing a scheduler, to determine execution of task rescheduling, based on states of the plurality of processing cores, tasks stored in the plurality of task queues, and at least one reference value, and, when the task rescheduling is executed, move a first task stored in a first task queue to a second task queue.