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    Systems and methods for memory efficient parallel tensor decompositions
    2.
    发明授权
    Systems and methods for memory efficient parallel tensor decompositions 有权

    公开(公告)号：US11755684B1

    公开(公告)日：2023-09-12

    申请号：US17205741

    申请日：2021-03-18

    申请人： Reservoir Labs, Inc.

    发明人： Muthu Manikandan Baskaran

    IPC分类号： G06F17/16 ,  G06F16/174

    CPC分类号： G06F17/16 ,  G06F16/1744

    摘要： In a system for improving performance of tensor-based computations and for minimizing the associated memory usage, computations associated with different non-zero tensor values are performed while exploiting an overlap between the respective index tuples of those non-zero values. While performing computations associated with a selected mode, when an index corresponding to a particular mode in a current index tuple is the same as the corresponding index from another, previously processed index tuple, the value already stored in a buffer corresponding to that particular mode is reused either wholly or in part, minimizing the processor usage and improving performance. Certain matrix operations may be iterated more than once so as to avoid the need to store a large partial result obtained from those operations. The performance overhead of the repeated operations is not significant, but the reduction in memory usage is.

    Systems and methods for memory efficient parallel tensor decompositions
    7.
    发明授权
    Systems and methods for memory efficient parallel tensor decompositions 有权

    公开(公告)号：US11086968B1

    公开(公告)日：2021-08-10

    申请号：US16000486

    申请日：2018-06-05

    申请人： Reservoir Labs, Inc.

    发明人： Muthu Manikandan Baskaran

    IPC分类号： G06F17/16 ,  G06F16/174

    摘要： In a system for improving performance of tensor-based computations and for minimizing the associated memory usage, computations associated with different non-zero tensor values are performed while exploiting an overlap between the respective index tuples of those non-zero values. While performing computations associated with a selected mode, when an index corresponding to a particular mode in a current index tuple is the same as the corresponding index from another, previously processed index tuple, the value already stored in a buffer corresponding to that particular mode is reused either wholly or in part, minimizing the processor usage and improving performance. Certain matrix operations may be iterated more than once so as to avoid the need to store a large partial result obtained from those operations. The performance overhead of the repeated operations is not significant, but the reduction in memory usage is.

    Systems and methods for fast detection of elephant flows in network traffic
    8.
    发明授权
    Systems and methods for fast detection of elephant flows in network traffic 有权

    公开(公告)号：US10924418B1

    公开(公告)日：2021-02-16

    申请号：US16270089

    申请日：2019-02-07

    申请人： Reservoir Labs, Inc.

    发明人： Aditya Gudibanda ,  Jordi Ros-Giralt

    IPC分类号： G06F15/16 ,  H04L12/851 ,  H04L12/26

    摘要： In a system for efficiently detecting large/elephant flows in a network, the rate at which the received packets are sampled is adjusted according to a top flow detection likelihood computed for a cache of flows identified in the arriving network traffic. After observing packets sampled from the network, Dirichlet-Categorical inference is employed to calculate a posterior distribution that captures uncertainty about the sizes of each flow, yielding a top flow detection likelihood. The posterior distribution is used to find the most likely subset of elephant flows. The technique rapidly converges to the optimal sampling rate at a speed O(1/n), where n is the number of packet samples received, and the only hyperparameter required is the targeted detection likelihood.

    Systems and methods for selective expansive recursive tensor analysis
    9.
    发明授权
    Systems and methods for selective expansive recursive tensor analysis 有权

    公开(公告)号：US10824693B2

    公开(公告)日：2020-11-03

    申请号：US15375620

    申请日：2016-12-12

    申请人： Reservoir Labs, Inc.

    发明人： Muthu M. Baskaran ,  David Bruns-Smith ,  James Ezick ,  Richard A. Lethin

    IPC分类号： G06F17/16

    摘要： A system for performing tensor decomposition in a selective expansive and/or recursive manner, a tensor is decomposed into a specified number of components, and one or more tensor components are selected for further decomposition. For each selected component, the significant elements thereof are identified, and using the indices of the significant elements a sub-tensor is formed. In a subsequent iteration, each sub-tensor is decomposed into a respective specified number of components. Additional sub-tensors corresponding to the components generated in the subsequent iteration are formed, and these additional sub-tensors may be decomposed further in yet another iteration, until no additional components are selected. The mode of a sub-tensor can be decreased or increased prior to decomposition thereof. Components likely to reveal information about the data stored in the tensor can be selected for decomposition.