期刊
ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
卷 38, 期 1, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2049662.2049663
关键词
Algorithms; Experimentation; Performance; Graph drawing; performance evaluation; multilevel algorithms; sparse matrices
资金
- National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1115297] Funding Source: National Science Foundation
We describe the University of Florida Sparse Matrix Collection, a large and actively growing set of sparse matrices that arise in real applications. The Collection is widely used by the numerical linear algebra community for the development and performance evaluation of sparse matrix algorithms. It allows for robust and repeatable experiments: robust because performance results with artificially generated matrices can be misleading, and repeatable because matrices are curated and made publicly available in many formats. Its matrices cover a wide spectrum of domains, include those arising from problems with underlying 2D or 3D geometry (as structural engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics, computer graphics/vision, robotics/kinematics, and other discretizations) and those that typically do not have such geometry (optimization, circuit simulation, economic and financial modeling, theoretical and quantum chemistry, chemical process simulation, mathematics and statistics, power networks, and other networks and graphs). We provide software for accessing and managing the Collection, from MATLAB(TM), Mathematica(TM), Fortran, and C, as well as an online search capability. Graph visualization of the matrices is provided, and a new multilevel coarsening scheme is proposed to facilitate this task.
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