A parallel processing unit (PPU) can be divided into partitions. Each partition is configured to operate similarly to how the entire PPU operates. A given partition includes a subset of the computational and memory resources associated with the entire PPU. Software that executes on a CPU partitions the PPU for an admin user. A guest user is assigned to a partition and can perform processing tasks within that partition in isolation from any other guest users assigned to any other partitions. Because the PPU can be divided into isolated partitions, multiple CPU processes can efficiently utilize PPU resources.

Embodiments of systems and methods for managing performance optimization of applications executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, a method may include: identifying, by an IHS, a first application; assigning a first score to the first application based upon: (i) a user's presence state, (ii) a foreground or background application state, (iii) a power adaptor state, and (iv) a hardware utilization state, detected during execution of the first application; identifying, by the IHS, a second application; assigning a second score to the second application based upon: (i) another user's presence state, (ii) another foreground or background application state, (iii) another power adaptor state, and (iv) another hardware utilization state, detected during execution of the second application; and prioritizing performance optimization of the first application over the second application in response to the first score being greater than the second score.

A system for container migration includes containers running instances of an application running on a cluster, an orchestrator with a controller, a memory, and a processor in communication with the memory. The processor executes to monitor a vitality metric of the application. The vitality metric indicates that the application is in either a live state or a dead state. Additionally, horizontal scaling for the application is disabled and the application is scaled-down until the vitality metric indicates that the application is in the dead state. Responsive to the vitality metric indicating that the application is in the dead state, the application is scaled-up until the vitality metric indicates that the application is in the live state. Also, responsive to the vitality metric indication transitioning from the dead state to the live state, the application is migrated to a different cluster while the horizontal scaling of the application is disabled.

Systems and methods for providing a guaranteed batch pool are described, including receiving a job request for execution on the pool of resources; determining an amount of time to be utilized for executing the job request based on available resources from the pool of resources and historical resource usage of the pool of resources; determining a resource allocation from the pool of resources, wherein the resource allocation spreads the job request over the amount of time; determining that the job request is capable of being executed for the amount of time; and executing the job request over the amount of time, according to the resource allocation.

A method for establishing system resource prediction and resource management model through multi-layer correlations is provided. The method builds an estimation model by analyzing the relationship between a main application workload, resource usage of the main application, and resource usage of sub-application resources and prepares in advance the specific resources to meet future requirements. This multi-layer analysis, prediction, and management method is different from the prior arts, which only focus on single-level estimation and resource deployment. The present invention can utilize more interactive relationships at different layers to effectively perform predictions, thereby achieving the advantage of reducing hidden resource management costs when operating application services.

In a method of group control and management among electronic devices, wherein the electronic devices is in communication with a control device, a projectable space instance is provided for the control device to create a workspace, wherein a control and management tool and a plurality of unified tools for driving respective electronic devices are selectively added to the projectable space instance. The projectable space instance is then parsed with a projector by the control device to automatically generate a projected workspace corresponding to the workspace to be created via the projectable space instance. The control and management tool realizes at least one status information of at least a first one of the electronic devices by way of the unified tools, and controls at least a second one of the electronic devices to execute at least one task corresponding to the at least one status information.

Techniques for generating a dataflow graph include generating a first dataflow graph with a plurality of first nodes representing first computer operations in processing data, with at least one of the first computer operations being a declarative operation that specifies one or more characteristics of one or more results of processing of data, and transforming the first dataflow graph into a second dataflow graph for processing data in accordance with the first computer operations, the second dataflow graph including a plurality of second nodes representing second computer operations, with at least one of the second nodes representing one or more imperative operations that implement the logic specified by the declarative operation, where the one or more imperative operations are unrepresented by the first nodes in the first dataflow graph.

A method includes identifying, by a first instance of a service, a first number of data partitions of a data source to be processed by the service and a second number of instances of the service available to process the first number of data partitions. The method further includes separating the first number of data partitions into a first set of data partitions and a second set of data partitions in view of the second number of instances of the service, determining a target number of data partitions from the first set of data partitions to be claimed by each of the second number of instances of the service, and claiming, by the first instance of the service, the target number of data partitions from the first set of data partitions and up to one data partition from the second set of data partitions.

The invention relates to a method for organizing tasks, in at least some nodes of a computer cluster, comprising: First, launching two containers on each of said nodes, a standard container and a priority container, next, for all or part of said nodes with two containers, at each node, while a priority task does not occur, assigning one or more available resources of the node to the standard container thereof in order to execute a standard task, the priority container thereof not executing any task, when a priority task occurs, dynamically switching only a portion of the resources from the standard container thereof to the priority container thereof, such that, the priority task is executed in the priority container with the switched portion of the resources, and the standard task continues to be executed, without being halted, in the standard container with the non-switched portion of the resources.

A system for managing access to a shared resource includes a plurality of nodes is described. The system may determine a quota for each of a plurality of nodes in view of one or more factors associated with a respective node and set a local counter stored in a memory of each of the nodes to the value of the relevant quota. In response to a request to access a resource made by an end device, the system may serve the request with a first node in the nodes, wherein the local counter stored in the memory of the first node is more than zero, and decrement the local counter stored in the memory of the first node by one after serving the request. Synchronization data is sent from each of the nodes, including the value of the local counter stored in the memory of the relevant node.