High-Performance Static and Streaming Tensor Factorization Algorithms

Abstract

The major goals of this project are the following: - Develop efficient and scalable serial and parallel algorithms for factorizing large, irregular, and sparse tensors. The research will address issues related to memory and operation-efficient computations, streaming data and computations, dynamic sparsity, and cache- and locality friendly tensor reorderings and partitionings. - Develop efficient and scalable serial and parallel algorithms for irregular computations arising when operating on large, sparse, and unstructured static and dynamic graphs. Examples of such computations arise in various network-science-related fundamental operations (e.g., triangle counting, truss-decomposition, clustering) and in computations arising in applications involving graph signal processing and graph neural network.

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Document Details

Document Type
Technical Report
Publication Date
Feb 04, 2023
Accession Number
AD1210713

Entities

People

  • George Karypis

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automated Text Summarization
  • Big Data
  • Computations
  • Computer Languages
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Mining
  • Data Processing
  • Information Processing
  • Information Systems
  • Machine Learning
  • Network Science
  • Neural Networks
  • Signal Processing

Fields of Study

  • Computer science

Readers

  • Linear Algebra
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms