Micro-architecture designs and methods are provided. A computer processing architecture may include an instruction cache for storing producer instructions, a half-instruction cache for storing half instructions, and eager shelves for storing a result of a first producer instruction. The computer processing architecture may fetch the first producer instruction and a first half instruction; send the first half instruction to the eager shelves; based on execution of the first producer instruction, send a second half instruction to the eager shelves; assemble the first producer instruction in the eager shelves based on the first half instruction and the second half instruction; and dispatch the first producer instruction for execution.

Disclosed examples to detect and annotate backedges in data-flow graphs include: a characteristic detector to store a node characteristic identifier in memory in association with a first node of a dataflow graph; a characteristic comparator to compare the node characteristic identifier with a reference criterion; and a backedge identifier generator to generate a backedge identifier indicative of a backedge between the first node and a second node of the dataflow graph based on the comparison, the memory to store the backedge identifier in association with a connection arc between the first and second nodes.

An information processing method for determining a pattern that indicates an arrangement order of the plurality of tasks from upstream to downstream of a stream is performed by a computer. The method includes acquiring a plurality of patterns to be candidates of an arrangement order of the plurality of tasks from upstream to downstream of the stream in a case of executing the plurality of tasks using a stream processing format; specifying, for each pattern of the plurality of acquired patterns, an amount of data to be reintroduced from one task of the plurality of tasks to another task located upstream side of the stream with respect to the one task; and determining the pattern from among the plurality of patterns based on the specified amount of data to be reintroduced for the each pattern.

A method for generating and updating a live dashboard of a building management system for a building includes generating a dashboard designer interface and causing the dashboard designer interface to be displayed on a user device of a user, receiving a graphic element from the user, wherein the graphic file supports animation and user interaction, generating a widget by binding the graphic element received from the user to a widget of the live dashboard, binding a data point to the widget based on a user selection via the dashboard designer interface, wherein the data point being a data point of building equipment of the building, receiving a value for the data point from the building equipment, and displaying the widget in the live dashboard, the widget including an indication of the value for the data point.

Technologies for allocating resources across data centers include a compute device to obtain resource utilization data indicative of a utilization of resources for a managed node to execute a workload. The compute device is also to determine whether a set of resources presently available to the managed node in a data center in which the compute device is located satisfies the resource utilization data. Additionally, the compute device is to allocate, in response to a determination that the set of resources presently available to the managed node does not satisfy the resource utilization data, a supplemental set of resources to the managed node. The supplemental set of resources are located in an off-premises data center that is different from the data center in which the compute device is located. Other embodiments are also described and claimed.

Apparatus for implementing a data processing pipeline for machine condition monitoring and other applications is provided. The apparatus comprises data processing modules communicatively coupled in series, including plug-in modules configured to receive input data, and produce output data, at least some of which is used by at least one downstream improvement system to carry out remedial actions. The apparatus also comprises a data access layer configured to receive data and make it available in a unified data format to downstream data processing modules and the at least one downstream improvement system. The data access layer comprises an enterprise service bus, and a data unification processor to convert the input data to unified data objects and make these accessible to the plug-in modules and the at least one downstream improvement system via the bus.

A method is described for processing keyed data items that are each associated with a value of a key, the keyed data items being from a plurality of distinct data streams, the processing including collecting the keyed data items, determining, based on contents of at least one of the keyed data items, satisfaction of one or more specified conditions for execution of one or more actions and causing execution of at least one of the one or more actions responsive to the determining.

A data integration device, including input interfaces to which a device which delivers runtime data to the input interface is connectable in each case. The runtime data of the particular device are characterized with the aid of at least one aspect model that is associated with the particular input interface and that characterizes in each case an aspect of the runtime data. The particular input interface is associated with the particular aspect model.

A data processing system comprises memory, compile time logic, runtime logic, and instrumentation profiling logic. The memory stores a dataflow graph for an application. The dataflow graph has a plurality of compute nodes that are configured to be producers to produce data for execution of the application, and to be consumers to consume the data for execution of the application. The compile time logic partitions execution of the dataflow graph into stages. Each of the stages has one or more compute nodes, one or more producers, and one or more consumers. The runtime logic determines a processing latency for each of the stages by calculating time elapsed between producers of a particular stage receiving input data and consumers of the particular stage receiving output data. The instrumentation profiling logic generates performance statistics for the dataflow graph based on the processing latency determined for each of the stages.

A compiler is capable of compiling instructions that do or do not supply specialization information for a generic type. The generic type is compiled into an unspecialized type. If specialization information was supplied, the unspecialized type is adorned with information indicating type restrictions for application programming interface (API) points associated with the unspecialized type, which becomes a specialized type. A runtime environment is capable of executing calls to a same API point that do or do not indicate a specialized type, and is capable of executing calls to a same API point of objects of an unspecialized type or of objects of a specialized type. When the call to an API point indicates a specialized type, and the specialized type matches that of the object (if the API point belongs to an object), then a runtime environment may perform optimized accesses based on type restrictions derived from the specialized type.