A method for specifying and executing an application including multiple microservices on 5G slices within a multi-tiered 5G infrastructure is presented. The method includes managing compute requirements and network requirements of the application simultaneously by determining end-to-end application characteristics by employing an application slice specification including an application ID component, an application name component, an application metadata component, a function dependencies component, a function instances component, and an instance connections component, specifying a function slice specification including a function network slice specification and a function compute slice specification, and employing a runtime component including a resource manager, an application slice controller, and an application slice monitor, wherein the resource manager maintains a database and manages starting, stopping, updating, and deleting application instances.

One embodiment sets forth a technique for performing one or more matrix multiplication operations based on a first matrix and a second matrix. The technique includes receiving data associated with the first matrix from a first traversal engine that accesses nonzero elements included in the first matrix via a first tree structure. The technique also includes performing one or more computations on the data associated with the first matrix and the data associated with the second matrix to produce a plurality of partial results. The technique further includes combining the plurality of partial results into one or more intermediate results and storing the one or more intermediate results in a first buffer memory.

A container management device including a processor, wherein the processor is configured to acquire, for a service including interconnected respective microservices installed with containers for executing processing, a workload relating to the service, connection information that is information relating to how the microservices are interconnected, and a service chain for propagation of respective processing related to the workload across the microservices; employ a prediction model expressing a relationship between a workload of each of the microservices and a resource usage to find a resource usage of each of the microservices from the acquired workload, the acquired connection information, and the acquired service chain, and to predict a number of containers; and control container installation at a same moment for the respective microservices by installation with the predicted number of containers for each of the microservices.

Methods, systems and apparatuses for graph processing are disclosed. One graph streaming processor includes a thread manager, wherein the thread manager is operative to dispatch operation of the plurality of threads of a plurality of thread processors before dependencies of the dependent threads have been resolved, maintain a scorecard of operation of the plurality of threads of the plurality of thread processors, and provide an indication to at least one of the plurality of thread processors when a dependency between the at least one of the plurality of threads that a request has or has not been satisfied. Further, a producer thread provides a response to the dependency when the dependency has been satisfied, and each of the plurality of thread processors is operative to provide processing updates to the thread manager, and provide queries to the thread manager upon reaching a dependency.

An arithmetic processing apparatus includes: a plurality of nodes (N nodes) capable of communicating with each other, each of the plurality of nodes including a memory and a processor, the memory being configured to store a value and an operation result, the processor being configured to execute first processing when N is a natural number of 2 or more, n is a natural number of 1 or more, and N≠2.sup.n, wherein the first processing is configured to divide by 2 a value held by a first node, the first node being any of the plurality of nodes and a last node in an order of counting, obtain one or more node pairs by pairing remaining nodes among the plurality of nodes exception for the first node, and calculate repeatedly an average value of values held by each node pair of the one or more node pairs.

Systems and methods for virtually partitioning an integrated circuit may include identifying dimensional attributes of a target input dataset and selecting a data partitioning scheme from a plurality of distinct data partitioning schemes for the target input dataset based on the dimensional attributes of the target dataset and architectural attributes of an integrated circuit. The methods described herein may also include disintegrating the target dataset into a plurality of distinct subsets of data based on the selected data partitioning scheme and identifying a virtual processing core partitioning scheme from a plurality of distinct processing core partitioning schemes for an architecture of the integrated circuit based on the disintegration of the target input dataset. Additionally, the architecture of the integrated circuit may be virtually partitioned into a plurality of distinct partitions of processing cores and each of the plurality of distinct subsets of data may be mapped to one of the plurality of distinct partitions of processing cores.

Certain aspects of the present disclosure provide a method for performing parallel data processing, including: receiving data for parallel processing from a data processing requestor; generating a plurality of data sub-blocks; determining a plurality of data portions in each data sub-block of the plurality of data sub-blocks; changing an order of the plurality of data portions in at least one data sub-block of the plurality of data sub-blocks; providing the plurality of data sub-blocks, including the at least one data sub-block comprising the changed order of the plurality of data portions, to a plurality of processing units for parallel processing; and receiving processed data associated with the plurality of data sub-blocks from the plurality of processing units.

The disclosure relates to a parallel computing system comprising a plurality of processing devices for performing an application. Each processing device is configured to obtain a local result, wherein a global result of a collective operation depends on the local results of the plurality of processing devices, and to distribute the local result of the processing device to one or more of the other processing devices, in response to determining that the global result is based only on the local result of the processing device, that is a likelihood that the global result is based only on the local result of the processing device is greater than a likelihood threshold value, or that the global result is based only on the local result of the processing device and a further local result of a further processing device of the plurality of processing devices.

One embodiment provides a method, including: producing, for each of a plurality of containers, a resource profile for each thread in each of the plurality of containers; identifying, for each of the plurality of containers and from, at least in part, the resource profiles, container dependencies between threads on a single of the plurality of containers; determining service dependencies between threads across different of the plurality of containers; scheduling, based upon the container dependencies and the service dependencies, threads to cores, wherein the scheduling is based upon minimizing thread processing times; and publishing the container dependencies and the service dependencies on a registry of the node clusters.

A method for execution by a computing device of a storage network begins by obtaining scoring information for a rebuilding encoded data slices for one or more storage units of a set of storage units of the storage network, where the scoring information includes two or more of a plurality of rebuilding rates, a plurality of input/output rates, a plurality of scores, and a plurality of selection rates. The method continues with determining a rebuilding rate of the plurality of rebuilding rates to utilize for the rebuilding based on the scoring information. The method continues by implementing the rebuilding of the encoded data slices in accordance with the rebuilding rate.