Computation modification by amplification of stencil including stencil points

    公开(公告)号:US11567746B2

    公开(公告)日:2023-01-31

    申请号:US16927016

    申请日:2020-07-13

    Abstract: In a sequence of major computational steps or in an iterative computation, a stencil amplifier can increase the number of data elements accessed from one or more data structures in a single major step or iteration, thereby decreasing the total number of computations and/or communication operations in the overall sequence or the iterative computation. Stencil amplification, which can be optimized according to a specified parameter such as compile time, rune time, code size, etc., can improve the performance of a computing system executing the sequence or the iterative computation in terms of run time, memory load, energy consumption, etc. The stencil amplifier typically determines boundaries, to avoid erroneously accessing data elements not present in the one or more data structures.

    Efficient and scalable storage of sparse tensors

    公开(公告)号:US11573945B1

    公开(公告)日:2023-02-07

    申请号:US17033592

    申请日:2020-09-25

    Abstract: In a system for storing in memory a tensor that includes at least three modes, elements of the tensor are stored in a mode-based order for improving locality of references when the elements are accessed during an operation on the tensor. To facilitate efficient data reuse in a tensor transform that includes several iterations, on a tensor that includes at least three modes, a system performs a first iteration that includes a first operation on the tensor to obtain a first intermediate result, and the first intermediate result includes a first intermediate-tensor. The first intermediate result is stored in memory, and a second iteration is performed in which a second operation on the first intermediate result accessed from the memory is performed, so as to avoid a third operation, that would be required if the first intermediate result were not accessed from the memory.

    SYSTEMS AND METHODS FOR MULTIRESOLUTION PARSING

    公开(公告)号:US20220263840A1

    公开(公告)日:2022-08-18

    申请号:US17645890

    申请日:2021-12-23

    Abstract: A multiresolution parser (MRP) can selectively extract one or more information units from a dataset based on the available processing capacity and/or the arrival rate of the dataset. Should any of these parameters change, the MRP can adaptively change the information units to be extracted such that the benefit or value of the extracted information is maximized while minimizing the cost of extraction. This tradeoff is facilitated, at least in part, by an analysis of the spectral energy of the datasets expected to be processed by the MRP. The MRP can also determine its state after a processing iteration and use that state information in subsequent iterations to minimize the required computations in such subsequent iterations, so as to improve processing efficiency.

    Systems and methods for multiresolution parsing

    公开(公告)号:US11770386B2

    公开(公告)日:2023-09-26

    申请号:US17645890

    申请日:2021-12-23

    CPC classification number: H04L63/1408 G06F16/90344

    Abstract: A multiresolution parser (MRP) can selectively extract one or more information units from a dataset based on the available processing capacity and/or the arrival rate of the dataset. Should any of these parameters change, the MRP can adaptively change the information units to be extracted such that the benefit or value of the extracted information is maximized while minimizing the cost of extraction. This tradeoff is facilitated, at least in part, by an analysis of the spectral energy of the datasets expected to be processed by the MRP. The MRP can also determine its state after a processing iteration and use that state information in subsequent iterations to minimize the required computations in such subsequent iterations, so as to improve processing efficiency.

    Systems and methods for selective expansive recursive tensor analysis

    公开(公告)号:US11520856B2

    公开(公告)日:2022-12-06

    申请号:US17086772

    申请日:2020-11-02

    Abstract: 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.

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