Techniques for operating a computing system to perform neural network operations are disclosed. In one example, a method comprises receiving a neural network model, determining a sequence of neural network operations based on data dependency in the neural network model, and determining a set of instructions to map the sequence of neural network operations to the processing resources of the neural network processor. The method further comprises determining, based on a set of memory access operations included in the set of instructions, a first set of memory references associated with a first location of an external memory to store the input data and a second set of memory references associated with a second location of the external memory to store the output data, and generating an instruction file including the set of instructions, the first set of memory references and the second set of memory references.

A method, system, and computer program product to plan and schedule executions of various utility tasks of a utility command during a maintain window, the method including receiving a utility command. The method may also include identifying possible utility tasks used to execute the utility command. The method may also include determining preferred utility tasks. The method may also include calculating a degree of parallelism for the preferred utility tasks. The method may also include generating a utility execution plan for the utility command. The method may also include analyzing the utility execution plan against resource constraints of a time window and sub time windows of the time window. The method may also include generating a time window execution plan for each sub time window of the sub time windows. The method may also include updating the utility execution plan with the time window execution plans.

A computer-implemented method, a computer program product, and a computer system for placements of workloads in a system of multiple platforms as a service. A computer detects a triggering event for modifying a matrix that pairs respective workloads on respective platforms and includes attributes of running respective workloads on respective platforms. The computer recalculates the attributes in the matrix, in response to the triggering event being detected. The computer determines optimal placements of the respective workloads on the respective platforms, based on information in the matrix. The computer places the respective workloads on the respective platforms, based on the optimal placements.

Embodiments of the present invention provide a system for facilitating creation, verification, and management of digital resources. The system is configured for receiving a digital content for upload to a distributed register from a user, via a user interface, receiving one or more instructions associated with distribution of the digital content from the user, via the user interface, creating one or more digital resources from the digital content via the user interface based on the one or more instructions received from the user, and storing the one or more digital resources on the distributed register.

Methods, systems, and apparatuses for managing and selecting virtual warehouses for execution of queries on one or more data warehouses are described herein. A request to execute a query may be received. An execution plan, for the query, may be identified. A processing complexity for the query may be predicted based on the query and the execution plan. A plurality of virtual warehouses may be identified. An operating status and processing capabilities of the plurality of virtual warehouses may be determined. A subset of the plurality of virtual warehouses may be selected based on the processing complexity, the operating status of the plurality of virtual warehouses, and the processing capabilities of the plurality of virtual warehouses. The query may be executed on one of the subset of the plurality of virtual warehouses.

Multiple work requests from different applications are queued to be processed subsequently without interruption by a crypto device. A prediction table is generated for each application to be processed by the crypto device. An initial credit value is determined for each incoming work request. The work request is an entry in an ordered queue in the order of time using respective time stamps. The next work request to be processed is selected from the entries in the queue by using the first entry in the queue for which the credit values for the corresponding application is greater than or equal to the predicted execution time for the corresponding request type in the prediction table. The selected next work request is processed.

A system for provisioning resources includes a processor and a memory. The processor is configured to receive a time series of past usage data. The past usage data comprises process usage data and instance usage data. The processor is further configured to determine an upcoming usage data based at least in part on the time series of the past usage data, and provision a computing system according to the upcoming usage data.

To dynamically allow chaining of logical processing units comprising endpoints, at least a type of an endpoint, and address information whereto connect the endpoint is configured, wherein the type of the endpoint is either a host port type or a logical processing unit type. During offloading from a central processing unit one or more functions to be performed by at least one further processing unit, the central processing unit is interacting with the one or more logical processing units via endpoints of the host port type and logical processing units are interacting via endpoints of the logical processing unit port type, the interaction using the address information.

An example operation may include one or more of storing, in memory, a cloud architecture document of a cloud computing environment, transforming the cloud architecture document into infrastructure as code (IaC) based on predefined code and storing the IaC in a machine-readable file, deploying the cloud computing environment via a host platform, and executing the machine-readable file and automatically configuring cloud resources of the cloud computing environment based on the IaC included in the machine-readable file.

A request to perform a compute task is received. A plurality of compute processor resources eligible to perform the compute task is identified, wherein the plurality of compute processor resources includes two or more of the following: a field-programmable gate array, an application-specific integrated circuit, a graphics processing unit, or a central processing unit. Based on an optimization metric, one of the compute processor resources is dynamically selected to perform the compute task.