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pnnl/LAP

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/LAP
https://doi.org/10.11578/dc.20241111.1

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Abstract

A software framework to study, from the performance and energy perspective, the efficacy of GPU-resident parallel Conjugate Gradient (CG) linear solver with different preconditioner options, including Gauss-Seidel, Jacobi, and incomplete Cholesky. We also propose a novel GPU-based preconditioner, in which the triangular solves are approximated by an iterative process
Developers:
Swirydowicz, Kasia ;  Firoz, Jesun Sahariar [1] ;  Swirydowicz, Kasia [2]
  1. Pacific Northwest National Laboratory
  2. Advanced Micro Devices (AMD)
Release Date:
2024-11-11
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Version:
LAP v 2
Licenses:
BSD 3-clause "New" or "Revised" License
Sponsoring Org.:
Code ID:
147360
Site Accession Number:
Battelle IPID 33233-E
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/LAP
https://doi.org/10.11578/dc.20241111.1

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PNNL Badge

Citation Formats

Swirydowicz, Kasia, Firoz, Jesun Sahariar, and Swirydowicz, Kasia. pnnl/LAP. Computer Software. https://github.com/pnnl/LAP. USDOE. 11 Nov. 2024. Web. doi:10.11578/dc.20241111.1.
Swirydowicz, Kasia, Firoz, Jesun Sahariar, & Swirydowicz, Kasia. (2024, November 11). pnnl/LAP. [Computer software]. https://github.com/pnnl/LAP. https://doi.org/10.11578/dc.20241111.1.
Swirydowicz, Kasia, Firoz, Jesun Sahariar, and Swirydowicz, Kasia. "pnnl/LAP." Computer software. November 11, 2024. https://github.com/pnnl/LAP. https://doi.org/10.11578/dc.20241111.1.
@misc{ doecode_147360,
title = {pnnl/LAP},
author = {Swirydowicz, Kasia and Firoz, Jesun Sahariar and Swirydowicz, Kasia},
abstractNote = {A software framework to study, from the performance and energy perspective, the efficacy of GPU-resident parallel Conjugate Gradient (CG) linear solver with different preconditioner options, including Gauss-Seidel, Jacobi, and incomplete Cholesky. We also propose a novel GPU-based preconditioner, in which the triangular solves are approximated by an iterative process},
doi = {10.11578/dc.20241111.1},
url = {https://doi.org/10.11578/dc.20241111.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20241111.1}},
year = {2024},
month = {nov}
}