Apparatuses, systems, and techniques to configure processor partitioning for a multi-process service. In at least one embodiment, a multi-process service configures a set of streaming multiprocessors of one or more parallel processing units to perform one or more threads based on one or more user-defined data values accessible to a parallel processing library, such as compute uniform device architecture (CUDA).

A system can train a neural network model at a first edge device regarding respective amounts of time to process data at the first edge device compared to corresponding amounts of time to process the data at cloud computing equipment that is connected to the first edge device via a communications network, wherein the data is generated at the first edge device. The system can update the neural network model to produce an updated neural network model based on information received from a second edge device regarding a performance of the cloud computing equipment in processing the data, wherein the first edge device and the second edge device having respective different processing capabilities. The system can determine whether to process first data, generated at the first edge device, locally at the first edge device.

Embodiments of systems and methods for telemetry of Artificial Intelligence (AI)/Machine Learning (ML) workloads are described. In some embodiments, a High Performance Computing (HPC) platform may include: a head node and a plurality of accelerator resources coupled to the head node, where each of the accelerator resources is configured to provide telemetry data to a collector, where the collector is configured to transmit the telemetry data to an allocator, and where the allocator is configured to assign a workload to a selected one of the plurality of accelerator resources based, at least in part, upon telemetry data.

A resource use method, an electronic device, and a computer program product are provided in embodiments of the present disclosure. The method includes determining a plurality of jobs requesting to use accelerator resources to accelerate data processing. The plurality of jobs are initiated by at least one virtual machine. The method further includes allocating available accelerator resources to the plurality of jobs based on job types of the plurality of jobs. The method further includes causing the plurality of jobs to be executed using the allocated accelerator resources. With the embodiments of the present disclosure, accelerator resources can be dynamically allocated, thereby improving the overall performance of a system.

Embodiments of a software defined automation system that provides a reference architecture for designing, managing and maintaining a highly available, scalable and flexible automation system. In some embodiments, an SDA system can include a localized subsystem including a system controller node and multiple compute nodes. The multiple compute nodes can be communicatively coupled to the system controller node via a first communication network. The system controller node can manage the multiple compute nodes and virtualization of a control system on a compute node via the first communication network. The virtualized control system includes virtualized control system elements connected to a virtual network that is connected to a second communication network to enable the virtualized control system elements to control a physical control system element via the second communication network connected to the virtual network.

Compound processing of an upscaler operation using platform resources includes: identifying a plurality of platform resources available to perform an upscaling operation, wherein the plurality of platform resources includes one or more graphics processor units (GPUs) and one or more accelerated processing units (APUs); and dynamically assigning workloads of the upscaling operation to one or more of the platform resources based on a modality of the upscaling operation; and processing the workloads of the upscaling operation by the platform resources to which the workloads are assigned.

Examples described herein relate to a network interface device that includes packet processing circuitry and circuitry. In some examples, the circuitry is to execute a first process of partitioned processes to provide a remote procedure call (RPC) interface for a second process. In some examples, the second process of the partitioned processes includes a business logic. In some examples, the partitioned processes comprise resource and deployment definition are based on an Interface Description Language (IDL) and a memory allocation.

Disclosed are a system and method of integrating an on-demand compute environment into a local compute environment. The method includes receiving a request from an administrator to integrate an on-demand compute environment into a local compute environment and, in response to the request, automatically integrating local compute environment information with on-demand compute environment information to make available resources from the on-demand compute environment to requesters of resources in the local compute environment such that policies of the local environment are maintained for workload that consumes on-demand compute resources.

Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes at least one memory; instructions; and processor circuitry to execute the instructions. The processor circuitry executes the instructions to extract static and dynamic data from a packet associated with a request for service by an edge device, the static data to change less frequently than the dynamic data. The processor circuitry executes the instructions to generate a first plurality of probability distributions using the static data. The processor circuitry executes the instructions to generate a second plurality of probability distributions using the dynamic data. The processor circuitry executes the instructions to calculate a confidence value for a first helper compute unit of a plurality of helper compute units, the confidence value. The processor circuitry executes the instructions to assign the first helper compute unit the request for service based on the confidence value.

Methods and apparatus to implement always-on context sensor hubs for processing multiple different types of data inputs are disclosed. An examples apparatus includes a first processor core to implement a host controller, and a second processor core to implement an offload engine. The host controller includes first logic to process sensor data associated with an electronic device when the electronic device is in a low power mode. The host controller is to offload a computational task associated with the sensor data to the offload engine. The offload engine includes second logic to execute the computational task.