An information processing apparatus includes a storage unit configured to store at least a first boot program and a second boot program corresponding to the first boot program, a controller configured to read and execute a program, detect, in accordance with occurrence of a read error at reading of the first boot program, an address of a storage area storing a program in which the read error has occurred in the first boot program, and specify, from an address of a storage area storing the second boot program, an address corresponding to the detected address. The controller reads and executes the second boot program stored in the specified address.

In some embodiments, a method comprises: determining, by a computing device, a potential for tasks of one or more applications not to be completed at a given time, the one or more applications being hosted on remote computing devices; initiating, by the computing device, a call to one or more application programmable interfaces (APIs) of the remote computing devices to retrieve data about the one or more tasks from the one or more applications, the tasks being generated by the one or more applications and including a time of completion being that of the given time; and initiating, by the computing device, an update to at least one of the one or more applications using the retrieved data, so that at least one of the generated tasks is completed before or at the given time.

A method for enabling a task management interface includes receiving an instruction for enabling the task management interface, displaying the task management interface in response to the instruction for enabling the task management interface, where the task management interface includes a preview interface of at least one application program and an icon corresponding to at least one function of the application program, receiving an operation instruction for the icon, and switching the application program corresponding to the icon to a foreground and executing the function in response to the operation instruction.

A data stream may include a plurality of records that are ordered, and the plurality of records may be assigned to a processing shard. A first set of virtual shards may be formed, the first set of virtual shards having a first quantity of virtual shards that perform parallel processing operations on behalf of the processing shard. First records of the plurality of records may be processed using the first set of virtual shards. The first quantity of virtual shards may be modified, based at least in part on an observed record age, to a second quantity of virtual shards that perform parallel processing operations on behalf of the processing shard. A second set of virtual shards may be formed having the second quantity of virtual shards. Second records of the plurality of records may be processed using the second set of virtual shards.

A device may receive information identifying a set of tasks to be executed by a microservices application that includes a plurality of microservices. The device may determine an execution time of the set of tasks based on a set of parameters and a model. The set of parameters may include a first parameter that identifies a first number of instances of a first microservice of the plurality of microservices, and a second parameter that identifies a second number of instances of a second microservice of the plurality of microservices. The device may compare the execution time and a threshold. The threshold may be associated with a service level agreement. The device may selectively adjust the first number of instances or the second number of instances based on comparing the execution time and the threshold.

This application provides a method for managing a resource in a computer system and a terminal device. The method includes: obtaining data, where the data includes application sequence feature data related to a current foreground application, and the data further includes at least one of the following real-time data: a system time of the computer system, current status data of the computer system, and current location data of the computer system; selecting, from a plurality of machine learning models based on at least one of the real-time data, a target machine learning model that matches the real-time data; inputting the obtained data into the target machine learning model to rank importance of a plurality of applications installed in the computer system; and performing resource management based on a result of the importance ranking.

Systems and methods of selecting digital platforms for execution of voice-based commands are provided. The system receives an application that performs an action associated with a service via digital platforms. The system debugs the application to validate parameters of the action on at least two platforms of the digital platforms. The system receives data packets comprising an input audio signal detected by a sensor of a client device, and parses the input audio signal to identify the action and the service. The system selects a first platform from the digital platforms to perform the action. The system initiates, responsive to selection of the first platform, an interactive data exchange to populate parameters of an action data structure corresponding to the action. The system executes the action via the selected platform using the action data structure.

A system and method for facilitating management of cloud infrastructure by using smart bots is disclosed. The method includes obtaining one or more insights associated with one or more user accounts on a cloud infrastructure from one or more cloud infrastructure resources and determining one or more cloud infrastructure issues associated with the one or more user accounts by validating the obtained one or more insights based on a set of predefined rules. The method further includes creating one or more customized bots for the determined one or more cloud infrastructure issues based on one or more user parameters by using a rule engine based AI model and deploying the created one or more customized bots on the one or more cloud infrastructure resources. Further, the method includes managing the cloud infrastructure via the deployed one or more customized bots.

Methods, systems, and devices for pipeline fusion of a plurality of kernels. In some implementations, a first batch of a first kernel is executed on a first processing device to generate a first output of the first kernel based on an input. A first batch of a second kernel is executed on a second processing device to generate a first output of the second kernel based on the first output of the first kernel. A second batch of the first kernel is executed on the first processing device to generate a second output of the first kernel based on the input. The execution of the second batch of the first kernel overlaps at least partially in time with executing the first batch of the second kernel.

A method and an apparatus that schedule a plurality of executables in a schedule queue for execution in one or more physical compute devices such as CPUs or GPUs concurrently are described. One or more executables are compiled online from a source having an existing executable for a type of physical compute devices different from the one or more physical compute devices. Dependency relations among elements corresponding to scheduled executables are determined to select an executable to be executed by a plurality of threads concurrently in more than one of the physical compute devices. A thread initialized for executing an executable in a GPU of the physical compute devices are initialized for execution in another CPU of the physical compute devices if the GPU is busy with graphics processing threads. Sources and existing executables for an API function are stored in an API library to execute a plurality of executables in a plurality of physical compute devices, including the existing executables and online compiled executables from the sources.