公开(公告)号：US20240232284A9

公开(公告)日：2024-07-11

申请号：US17971486

申请日：2022-10-21

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： DANIEL DAUWE ,  Avinash Kethineedi ,  Darel Neal Emmot ,  Ponnamanda Bhaskar Sampath Sai Sriram

IPC: G06F17/16

CPC classification number: G06F17/16

Abstract: Systems and methods implement a column-partition sparse matrix (CPSM) format that provides enhanced/efficient matrix operations, e.g., sparse matrix vector multiplication (SpMV). The CPSM format is an enhanced layout, the data being arranged by column-partitioning the sparse matrix, and partitioning the dense matrix in a manner that improves scalability, computational efficiency, and leverages distributed computing architecture in performing SpMV operations. For example, data can be arranged by partitioning, by column, one or more contiguous columns of a sparse matrix of data into a plurality of column partitions, where the sparse matrix is associated with a sparse matrix multiplication operation. A plurality of column partition groups is formed. Each of the plurality of column partition groups are then distributed to a respective processor from a plurality of processors such that a portion of the sparse matrix multiplication operation is independently performed by each processor of the plurality of processors.

公开(公告)号：US20240134929A1

公开(公告)日：2024-04-25

申请号：US17971486

申请日：2022-10-20

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： DANIEL DAUWE ,  Avinash Kethineedi ,  Darel Neal Emmot ,  Ponnamanda Bhaskar Sampath Sai Sriram

IPC: G06F17/16

CPC classification number: G06F17/16

Abstract: Systems and methods implement a column-partition sparse matrix (CPSM) format that provides enhanced/efficient matrix operations, e.g., sparse matrix vector multiplication (SpMV). The CPSM format is an enhanced layout, the data being arranged by column-partitioning the sparse matrix, and partitioning the dense matrix in a manner that improves scalability, computational efficiency, and leverages distributed computing architecture in performing SpMV operations. For example, data can be arranged by partitioning, by column, one or more contiguous columns of a sparse matrix of data into a plurality of column partitions, where the sparse matrix is associated with a sparse matrix multiplication operation. A plurality of column partition groups is formed. Each of the plurality of column partition groups are then distributed to a respective processor from a plurality of processors such that a portion of the sparse matrix multiplication operation is independently performed by each processor of the plurality of processors.