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.

A method of managing resources in a graphics processing pipeline includes conditionally suspending a task when the task reaches a phase boundary during execution of a program within a texture/shading unit. Suspending the task comprises freeing resources allocated to the task and resources are subsequently re-allocated to the task, such that the task is ready to continue execution, only after determining that the conditions associated with un-suspending the task are satisfied.

A model coordination method for a first device is provided. The first device stores at least one model segment. The at least one model segment is configured to realize a part of functions of a preset model. The method includes: determining a first model segment from the at least one model segment stored in the first device, wherein when the first model segment is executed and a second model segment is executed by a second device, a part of or all the functions of the preset model are realized, the second model segment is one of at least one model segment stored in the second device, and the at least one model segment stored in the second device is configured to realize a part of the functions of the preset model. A model coordination apparatus is also provided.

Various aspects are disclosed for distributed application management using an embedded persistent queue framework. In some aspects, task execution data is monitored from a plurality of task execution engines. A task request is identified. The task request can include a task and a Boolean predicate for task assignment. The task is assigned to a task execution engine embedded in a distributed application process if the Boolean predicate is true, and a capacity of the task execution engine is sufficient to execute the task. The task is enqueued in a persistent queue. The task is retrieved from the persistent queue and executed.

Methods and systems disclosed herein relate generally to evaluating resource loads to determine when to transform queues and to specific techniques for transforming at least part of queues so as to correspond to alternative resources

A credit regulation and monitoring module receives a command for an application that is to be executed. In response to the command, credit amount for execution of the command is calculated. Further, an outstanding credit amount is determined based on an outstanding credit table and the other commands being executed. It is determined whether the credit amount and the outstanding credit are below a threshold value. If so, the command is executed and an outstanding credit table is updated to reduce the amount of credit available according to the credit amount allocated to the command.

Computer-implemented methods, systems, and computer program products for controlling application feature processing based on central processing unit (CPU) usage and/or feature requirement to apply the application feature are disclosed. The computer-implemented method for controlling application feature processing based on central processing unit (CPU) usage to apply the application feature includes monitoring system metrics for availability of system resources; determining availability of system resources required to perform the particular application feature; determining if the particular application feature is to be applied based on the determination of the availability of system resources; and applying the particular application feature based on results of the determination of the availability of system resources.

Methods and systems disclosed herein relate generally to evaluating resource loads to determine when to transform queues and to specific techniques for transforming at least part of queues so as to correspond to alternative resources.

A computer implemented method using an artificial intelligence (A.I.) module to explain large scale scheduling solutions includes receiving an original instance of a resource constrained scheduling problem. The instance includes a set of tasks and a variety of resource requirements and a variety of constraints. An optimizer process determines a schedule for the set of tasks while minimizing a makespan of the schedule. A minimal set of resource links is generated based on resource dependencies between tasks. The resource links are added to the original instance of scheduling problem, as precedence constraints. All the resource constraints are removed from the original instance of the resource constrained scheduling problem. A set of critical tasks is computed using a non-resource constrained critical path. Schedules are provided with an explanation of an optimized order of the set of tasks based on the use of the non-resource constrained critical path.

A deep neural network (“DNN”) module can compress and decompress neuron-generated activation data to reduce the utilization of memory bus bandwidth. The compression unit can receive an uncompressed chunk of data generated by a neuron in the DNN module. The compression unit generates a mask portion and a data portion of a compressed output chunk. The mask portion encodes the presence and location of the zero and non-zero bytes in the uncompressed chunk of data. The data portion stores truncated non-zero bytes from the uncompressed chunk of data. A decompression unit can receive a compressed chunk of data from memory in the DNN processor or memory of an application host. The decompression unit decompresses the compressed chunk of data using the mask portion and the data portion. This can reduce memory bus utilization, allow a DNN module to complete processing operations more quickly, and reduce power consumption.