Multi-Level Memory Algorithmics for Large, Sparse Problems

Technical Report · 01 November 2019

DOI:https://doi.org/10.2172/1574408· OSTI ID:1574408

Berry, Jonathan W. ^[1]; Butcher, Neil ^[2]; Catalyurek, Umit ^[3]; Kogge, Peter ^[2]; Lin, Paul ^[4]; Olivier, Stephen Lecler ^[1]; Phillips, Cynthia A. ^[1]; Rajamanickam, Sivasankaran ^[1]; Slota, George M. ^[5]; Voskuilen, Gwendolyn Renae ^[1]; Yasar, Abdurrahman ^[3]; Young, Jeffrey S. ^[3]

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1. Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
2. University of Notre Dame, IN (United States)
3. Georgia Institute of Technology, Atlanta, GA (United States)
4. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
5. Rensselaer Polytechnic Institute, Troy, NY (United States)

In this report, we abstract eleven papers published during the project and describe preliminary unpublished results that warrant follow-up work. The topic is multi-level memory algorithmics, or how to effectively use multiple layers of main memory. Modern compute nodes all have this feature in some form.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1574408
• Report Number(s): SAND-2019-13871; 681427
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING