pyDRESCALk

Software · Tue Dec 07 00:00:00 EST 2021

DOI:https://doi.org/10.11578/dc.20220106.2· OSTI ID:code-64375 · Code ID:64375

Bhattarai, Manish; Kharat, Namita; Skau, Erik; Alexandrov, Boian; Vangara, Raviteja; Smith, James; Tierney, Thomas

Modern data scientists are tasked to analyze ever-growing data sets with increasingly complex relationships. Tensor decompositions have come to play a central role in identifying underlying latent structures in higher-order data. The problem of fitting tensor models to different distributions is complicated by the combinations of size, dimensionality, and sparsity present in real world data. The situation demands efficient algorithms designed for shared-memory and distributed systems. This work will present new research that tackles these challenges on several different fronts, leveraging optimizations in numerical algorithms and sparse tensor representations in heterogeneous high performance computing environments.

• Site Accession Number: C21067
• Software Type: Scientific
• License(s): BSD 3-clause "New" or "Revised" License
• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program

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  Primary Award/Contract Number:

  AC52-06NA25396
• DOE Contract Number: AC52-06NA25396
• Code ID: 64375
• OSTI ID: code-64375
• Country of Origin: United States