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Chen et al., 2022 - Google Patents

Exploiting hierarchical parallelism and reusability in tensor kernel processing on heterogeneous HPC systems

Chen et al., 2022

Document ID
38452485182481226
Author
Chen Y
Xiao G
Özsu M
Tang Z
Zomaya A
Li K
Publication year
Publication venue
2022 IEEE 38th International Conference on Data Engineering (ICDE)

External Links

Snippet

Canonical Polyadic Decomposition (CPD) of sparse tensors is an effective tool in various machine learning and data analytics applications, in which sparse Matricized Tensor Times Khatri-Rao Product (MTTKRP) is the major performance bottleneck. To overcome this …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

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    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386—Retrieval requests
    • G06F17/30424—Query processing
    • G06F17/30442—Query optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06F9/00—Arrangements for programme control, e.g. control unit
    • G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
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    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
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    • G06—COMPUTING; CALCULATING; COUNTING
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