International Business Machines Corporation

Minwei Feng

Wei Zhang

Li Feng Zhang

Yandong Wang

Yufei Ren

Mark Alan Lavin

Rui Zhang

Tarun Kumar

Kevin Wilson Warren

Ildar Khabibrakhmanov

A machine receives a first set of global parameters from a global parameter server. The first set of global parameters includes data that weights one or more operands used in an algorithm that models an entity type. Multiple learner processors in the machine execute the algorithm using the first set of global parameters and a mini-batch of data known to describe the entity type. The machine generates a consolidated set of gradients that describes a direction for the first set of global parameters in order to improve an accuracy of the algorithm in modeling the entity type when using the first set of global parameters and the mini-batch of data. The machine transmits the consolidated set of gradients to the global parameter server. The machine then receives a second set of global parameters from the global parameter server, where the second set of global parameters is a modification of the first set of global parameters based on the consolidated set of gradients.

Parameter data sharing for multi-learner training of machine learning applications

A storage controller of a machine receives training data associated with a neural network model. The neural network model includes a plurality of layers, and the machine further including at least one graphics processing unit. The storage controller trains at least one layer of the plurality of layers of the neural network model using the training data to generate processed training data. A size of the processed data is less than a size of the training data. Training of the at least one layer includes adjusting one or more weights of the at least one layer using the training data. The storage controller sends the processed training data to at least one graphics processing unit of the machine. The at least one graphics processing unit is configured to store the processed training data and train one or more remaining layers of the plurality of layers using the processed training data.

Storage controller acceleration for neural network training and inference

A method for providing a graphical visualization of a neural network to a user is provided. The method includes generating the graphical visualization of the neural network at least in part by: representing layers of the neural network as respective three-dimensional blocks, wherein at least a first dimension of a given block is proportional to a computational complexity of a layer of the neural network represented by the given block; and representing data flows between the layers of the neural network as respective three-dimensional structures connecting blocks representing the layers of the neural network, wherein a first dimension of a given structure is proportional to each of a first dimension and a second dimension of a data flow represented by the given structure. The method also includes displaying the graphical visualization of the neural network to the user.

Method for visualizing neural network models

A processing unit topology of a neural network including a plurality of processing units is determined. The neural network includes at least one machine in which each machine includes a plurality of nodes, and wherein each node includes at least one of the plurality of processing units. One or more of the processing units are grouped into a first group according to a first affinity. The first group is configured, using a processor and a memory, to use a first aggregation procedure for exchanging model parameters of a model of the neural network between the processing units of the first group. One or more of the processing units are grouped into a second group according to a second affinity. The second group is configured to use a second aggregation procedure for exchanging the model parameters between the processing units of the second group.

Hybrid aggregation for deep learning neural networks

A method, computer system, and computer program product. Weather forecast data is generated with respect to an area encompassing a location of a solar farm by a computer system. Solar power output by the solar farm is forecasted by the computer system based on the generated weather forecast data. Forecasted solar power output data is generated by the computer system based on the forecasted solar power output by the solar farm. A power grid operation, including one or both of a power grid balancing operation and a power grid optimization operation, is performed based on the forecasted solar power output data.

Forecasting solar power output

A network interface controller of a machine receives a packet including at least one model parameter of a neural network model from a server. The packet includes a virtual address associated with the network interface controller, and the machine further includes a plurality of graphics processing units coupled to the network interface controller by a bus. The network interface controller translates the virtual address to a memory address associated with each of the plurality of graphics processing units. The network interface controller broadcasts the at least one model parameter to the memory address associated with each of the plurality of graphics processing units.

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Minwei Feng