Systems, methods, and non-transitory, machine-readable media to facilitate updating media devices in a local network with a client-server architecture are disclosed. A primary media device may be configured to operate as a server in a local network, receive audio/video (A/V) content via an Internet connection and/or a satellite network connection, serve the A/V content to a set of one or more secondary media devices for display with at least one television of a set of one or more televisions, and receive a set of one or more software updates via the Internet connection, a Universal Serial Bus (USB) connection, and/or the satellite network connection. One or more software updates of the set of one or more software updates may be specified for, and may be transferred to, the set of one or more secondary media devices.

Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

Systems, methods, and non-transitory, machine-readable media to facilitate streaming in a local network are disclosed. A primary media device may be configured to: operate as a server in a local network, receive audio/video (A/V) content, and provide the A/V content to a first display. A secondary media device may be communicatively connected to the primary media device and may be configured to: operate as a client with respect to the primary media device in the local network, receive the A/V content from the primary media device, and provide the A/V content to a second display. The primary media device and the secondary media device may use multiple subnets in the local network. The primary media device and/or the secondary media device may select a first subnet of the multiple subnets to use based at least in part on a type of content to communicate via the first subnet.

Methods and systems relating to the field of parallel computing are disclosed herein. The methods and systems disclosed include approaches for sparsity uniformity enforcement for a set of computational nodes which are used to execute a complex computation. A disclosed method includes determining a sparsity distribution in a set of operand data, and generating, using a compiler, a set of instructions for executing, using the set of operand data and a set of processing cores, a complex computation. Alternatively, the method includes altering the operand data. The method also includes distributing the set of operand data to the set of processing cores for use in executing the complex computation in accordance with the set of instructions. Either the altering is conducted to, or the compiler is programmed to, balance the sparsity distribution among the set of processing cores.

A system, method, and computer-readable medium for synthesizing zero-copy sparse matrix factorization operations in heterogeneous compute systems are provided. The system includes a host and an accelerator device. The host device is configured to divide an input matrix into a plurality of blocks which are transferred to a memory of the accelerator device. The host device is also configured to generate at least one index buffer that includes pointers to the block in the accelerator's memory, where each index buffer represents a frontal matrix associated with a matrix decomposition algorithm. The host processor is configured to receive one or more kernels configured to process the index buffer(s) on an accelerator device. The index buffers are processed by the accelerator device and the modified block data is written back to a memory of the host device to generate a factorized output matrix.

Techniques herein minimally communicate between computers to repartition a graph. In embodiments, each computer receives a partition of edges and vertices of the graph. For each of its edges or vertices, each computer stores an intermediate representation into an edge table (ET) or vertex table. Different edges of a vertex may be loaded by different computers, which may cause a conflict. Each computer announces that a vertex resides on the computer to a respective tracking computer. Each tracking computer makes assignments of vertices to computers and publicizes those assignments. Each computer that loaded conflicted vertices transfers those vertices to computers of the respective assignments. Each computer stores a materialized representation of a partition based on: the ET and vertex table of the computer, and the vertices and edges that were transferred to the computer. Edges stored in the materialized representation are stored differently than edges stored in the ET.

Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

Methods and systems relating to the field of parallel computing are disclosed herein. The methods and systems disclosed include approaches for sparsity uniformity enforcement for a set of computational nodes which are used to execute a complex computation. A disclosed method includes determining a sparsity distribution in a set of operand data, and generating, using a compiler, a set of instructions for executing, using the set of operand data and a set of processing cores, a complex computation. Alternatively, the method includes altering the operand data. The method also includes distributing the set of operand data to the set of processing cores for use in executing the complex computation in accordance with the set of instructions. Either the altering is conducted to, or the compiler is programmed to, balance the sparsity distribution among the set of processing cores.

A method and system for thread aware, class aware, and topology aware memory allocations. Embodiments include a compiler configured to generate compiled code (e.g., for a runtime) that when executed allocates memory on a per class per thread basis that is system topology (e.g., for non-uniform memory architecture (NUMA)) aware. Embodiments can further include an executable configured to allocate a respective memory pool during runtime for each instance of a class for each thread. The memory pools are local to a respective processor, core, etc., where each thread executes.

Various arrangements for facilitating smart television content receivers in a local network are provided. A primary television receiver executing a first operating system can receive audio data including human voice from a voice enabled remote control. The primary television receiver can transmit the audio data to a secondary television receiver executing a second operating system and that includes a voice command component. The secondary television receiver can convert the audio data into voice command data and transmit the voice command data to the primary television receiver. The primary television receiver can transmit the voice command data to a voice processing server via the Internet and receive, in response, a command generated based on the voice command data. The primary television receiver can transmit the command to the secondary television receiver. The voice command component can then control an operation of the secondary television receiver based on the command.