A method and apparatus for providing support for Secure Objects on a data processing system including providing a Secure Object comprising code and data that is protected on the data processing system on a first processor which is a first type of processor, wherein the data processing system includes a plurality of processors of different types, responsive to a portion of the Secure Object being needed to be executed on a second processor which is a second type of processor different than the first type of processor, by the first processor calling the second processor in a special interprocessor call, returning information by the second processor to the first processor, and retrieving, by the first processor, the information from the second processor.

One or more embodiments of the present disclosure relate to monitoring execution of runnables that may be executed by a computing system, the executing begin based at least on a schedule. The monitoring may include one or more of: monitoring timing of execution of the runnables, monitoring one or more sequences of execution of the runnables, or monitoring health of at least a portion of the computing system executing the runnables. Additionally or alternatively, one or more embodiments may relate to determining compliance with respect to one or more execution constraints based at least in part on the monitoring.

Systems, methods, and apparatuses for accelerating streaming data-transformation operations are described. In one example, a system on a chip (SoC) includes a hardware processor core comprising a decoder circuit to decode an instruction comprising an opcode into a decoded instruction, the opcode to indicate an execution circuit is to generate a single descriptor and cause the single descriptor to be sent to an accelerator circuit coupled to the hardware processor core, and the execution circuit to execute the decoded instruction according to the opcode; and the accelerator circuit comprising a work dispatcher circuit and one or more work execution circuits to, in response to the single descriptor sent from the hardware processor core: when a field of the single descriptor is a first value, cause a single job to be sent by the work dispatcher circuit to a single work execution circuit of the one or more work execution circuits to perform an operation indicated in the single descriptor to generate an output, and when the field of the single descriptor is a second different value, cause a plurality of jobs to be sent by the work dispatcher circuit to the one or more work execution circuits to perform the operation indicated in the single descriptor to generate the output as a single stream.

Various aspects related to methods, systems, and computer readable media for scalable software deployment on autonomous mobile robots are described herein. A mobile robotics system can include a storage component configured to store a containerized software package, a server in operative communication with the storage component, and, an autonomous mobile robot (AMR) in operative communication with the server. The containerized software installation package is configured to direct the AMR to maneuver to perform at least one robotic task, monitor computational resource usage of resources of the AMR associated with the at least one robotic task, and, responsive to a determination that computational resource usage at the AMR is or will be above a threshold, sending a request to the server to perform a portion of processing tasks such that resource usage at the AMR is reduced to below the threshold or maintained below the threshold.

Examples are disclosed that relate to methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service comprises a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.

A gateway for use in a computing system to interface a host with the subsystem for acting as a work accelerator to the host, the gateway having: an accelerator interface for connection to the subsystem to enable transfer of batches of data between the subsystem and the gateway; a data connection interface for connection to external storage for exchanging data between the gateway and storage; a gateway interface for connection to at least one second gateway; a memory interface connected to a local memory associated with the gateway; and a streaming engine for controlling the streaming of batches of data into and out of the gateway in response to pre-compiled data exchange synchronisation points attained by the subsystem, wherein the streaming of batches of data are selectively via at least one of the accelerator interface, data connection interface, gateway interface and memory interface.

Technologies for providing hardware resources as a service with direct resource addressability are disclosed. According to one embodiment of the present disclosure, a device receives a request to access a destination accelerator device in an edge network, the request specifying a destination address assigned to the destination accelerator device. The device determines, as a function of the destination address, a location of the destination accelerator device and sends the request to the destination accelerator device.

An information handling system may include at least one central processing unit (CPU); and a plurality of special-purpose processing units. The information handling system may be configured to: receive information regarding cooling characteristics of the plurality of special-purpose processing units; and assign identification (ID) numbers to each of the plurality of special-purpose processing units in an order that is determined based at least in part on the cooling characteristics.

Methods and systems for distributing and concurrently executing various portions of a linearly programmed computing task in multiple cloud instances in cloud computing platforms are described herein. Upon receiving a request to execute the linearly programmed computing task, the requested task is added to a task queue. Various portions of the task may be determined based on the data structure of the data to be processed during the execution of the task. Then the portions may be distributed to multiple cloud instances for concurrent executions of the portions. Alternately, the task may be distributed to a cloud instance, which may determine the various portions based on the data structure of the data to be processed by the task, execute one or more portions, and then add requests for the other portions to the task queue such that the other portions can be distributed to other cloud instances for execution.

The current document is directed to methods and systems that automatically deploy and manage applications that are associated with hardware dependencies. As one example, many machine-learning-based applications use specialized hardware accelerators during training phases since, in many cases, training of machine-learning-based applications and systems would be computationally intractable without the increased computational bandwidth provided by hardware accelerators. However, such hardware dependencies may prevent machine-learning-based applications from being deployed and managed effectively by widely used automated orchestration systems, and manual deployment of applications with hardware dependencies may suffer significant inefficiencies and problems related to maintenance downtime within distributed computer systems. The currently disclosed methods and systems provide centralized maintenance-and-hardware-dependency scheduling information along with an asynchronous protocol for access to the maintenance-and-hardware-dependency scheduling information by automated orchestration systems and managers and administrators of distributed computer systems to facilitate efficient deployment of machine-learning-based applications with hardware dependencies.