LINPACK is a software library for performing numerical linear algebra on digital computers.
It was written in Fortran by Jack Dongarra, Jim Bunch, Cleve Moler, and Gilbert Stewart, and was intended for use on supercomputers in the 1970s and early 1980s. It has been largely superseded by LAPACK, which runs more efficiently on modern architectures.
LINPACK makes use of the BLAS (Basic Linear Algebra Subprograms) libraries for performing basic vector and matrix operations.
The LINPACK benchmarks appeared initially as part of the LINPACK user's manual. The parallel LINPACK benchmark implementation called HPL (High Performance Linpack) is used to benchmark and rank supercomputers for the TOP500 list.
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The TOP500 project ranks and details the 500 most powerful non-distributed computer systems in the world. The project was started in 1993 and publishes an updated list of the supercomputers twice a year. The first of these updates always coincides with the International Supercomputing Conference in June, and the second is presented at the ACM/IEEE Supercomputing Conference in November.
LAPACK ("Linear Algebra Package") is a standard software library for numerical linear algebra. It provides routines for solving systems of linear equations and linear least squares, eigenvalue problems, and singular value decomposition. It also includes routines to implement the associated matrix factorizations such as LU, QR, Cholesky and Schur decomposition. LAPACK was originally written in FORTRAN 77, but moved to Fortran 90 in version 3.2 (2008). The routines handle both real and complex matrices in both single and double precision.
Fugaku 富岳 is a petascale supercomputer at the Riken Center for Computational Science in Kobe, Japan. It started development in 2014 as the successor to the K computer and made its debut in 2020. It is named after an alternative name for Mount Fuji. It became the fastest supercomputer in the world in the June 2020 TOP500 list as well as becoming the first ARM architecture-based computer to achieve this. At this time it also achieved 1.42 exaFLOPS using the mixed fp16/fp64 precision HPL-AI benchmark.
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