- How to run linpack benchmark on linux how to#
- How to run linpack benchmark on linux full#
- How to run linpack benchmark on linux portable#
- How to run linpack benchmark on linux download#
LAlib = $(LAdir)/libcblas.a $(LAdir)/libatlas.a The variable LAdir is only used for defining LAinc and LAlib. # header files, LAlib is defined to be the name of the library to be # LAinc tells the C compiler where to find the Linear Algebra library # - Linear Algebra library (BLAS or VSIPL). The variable MPdir is only used for defining MPinc and MPlib. # header files, MPlib is defined to be the name of the library to be # MPinc tells the C compiler where to find the Message Passing library # - HPL Directory Structure / HPL library.
Edit Make.Linux_PII_CBLAS file # vim ~/hpl-2.1/Make.Linux_PII_CBLAS #. Copy Make.Linux_PII_CBLAS file from $(HOME)/hpl-2.1/setup/ to $(HOME)/hpl-2.1/ĥ.
How to run linpack benchmark on linux download#
Download the latest HPL ( hpl-2.1.tar.gz) from Ĥ. Building LAPACK 3.4 with Intel and GNU Compilerģ.Building BLAS Library using Intel and GNU Compiler.Installing BLAS, LAPACK and OpenMPI, do look at The algorithm used by HPL can be summarized by the following keywords: Two-dimensional block-cyclic data distribution – Right-looking variant of the LU factorization with row partial pivoting featuring multiple look-ahead depths – Recursive panel factorization with pivot search and column broadcast combined – Various virtual panel broadcast topologies – bandwidth reducing swap-broadcast algorithm – backward substitution with look-ahead of depth 1.
How to run linpack benchmark on linux portable#
It can thus be regarded as a portable as well as freely available implementation of the High Performance Computing Linpack Benchmark.
How to run linpack benchmark on linux full#
This excludes the use of a fast matrix multiply algorithm like "Strassen's Method" or algorithms which compute a solution in a precision lower than full precision (64 bit floating point arithmetic) and refine the solution using an iterative approach.HPL is a software package that solves a (random) dense linear system in double precision (64 bits) arithmetic on distributed-memory computers. In particular, the operation count for the algorithm must be 2/3 n^3 + O(n^2) double precision floating point operations. In an attempt to obtain uniformity across all computers in performance reporting, the algorithm used in solving the system of equations in the benchmark procedure must conform to LU factorization with partial pivoting.
These numbers together with the theoretical peak performance Rpeak are the numbers given in the TOP500. It does, however, reflect the performance of a dedicated system for solving a dense system of linear equations. Since the problem is very regular, the performance achieved is quite high, and the performance numbers give a good correction of peak performance.īy measuring the actual performance for different problem sizes n, a user can get not only the maximal achieved performance Rmax for the problem size Nmax but also the problem size N1/2 where half of the performance Rmax is achieved. This performance does not reflect the overall performance of a given system, as no single number ever can. For the TOP500, we used that version of the benchmark that allows the user to scale the size of the problem and to optimize the software in order to achieve the best performance for a given machine. The benchmark used in the LINPACK Benchmark is to solve a dense system of linear equations.
How to run linpack benchmark on linux how to#
A detailed description and frequently asked questions can be found: Ī parallel implementation of the Linpack benchmark and instructions on how to run it can be found at. The LINPACK Benchmark was introduced by Jack Dongarra. LINPACK was chosen because it is widely used and performance numbers are available for almost all relevant systems. As a yardstick of performance we are using the `best' performance as measured by the LINPACK Benchmark.