This is done to provide a comparable set of performance numbers across all computers.
This excludes the use of a fast matrix multiply algorithms like 'Strassian's Method'. In particular, the operation count for the algorithm must be 2/3 N*N*N + O(N*N) 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 the standard operation count for LU factorization with partial pivoting. These numbers together with the theoretical peak performance Rpeak are the numbers given in the TOP500. Since the problem is very regular, the performance achieved is quite high, and the performance numbers give a good correction of peak performance.By 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 N_1/2 where half of the performance Rmax is achieved. It does, however, reflect the performance of a dedicated system for solving a dense system of linear equations. This performance does not reflect the overall performance of a given system, as no single number ever can.
#Linpack benchmark download software#
The version of the benchmark for TOP500 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. A detailed description as well as a list of performance results on a wide variety of machines is available in PostScript(TM) form from Netlib: test used in the LINPACK Benchmark is to solve a dense system of linear equations. It is a yardstick of performance because it is widely used and performance numbers are available for almost all relevant systems.The LINPACK Benchmark was introduced by Jack Dongarra. Machine-specific as well as generic implementations of MPI, the BLAS and VSIPL are available for a large variety of systems.Īcknowledgements: This work was supported in part by a grant from the Department of Energy's Lawrence Livermore National Laboratory and Los Alamos National Laboratory as part of the ASCI Projects contract numbers B50397-001-00 4R.LINPACK BenchmarkVersion 2.0Presented by University of Tennessee Knoxville and Innovative Computing Laboratory.Implementation: Piotr LuszczekThis an optimized implementation of the LINPACK Benchmark. An implementation of either the Basic Linear Algebra Subprograms BLAS or the Vector Signal Image Processing Library VSIPL is also needed. The HPL software package requires the availibility on your system of an implementation of the Message Passing Interface MPI (1.1 compliant).
Nonetheless, with some restrictive assumptions on the interconnection network, the algorithm described here and its attached implementation are scalable in the sense that their parallel efficiency is maintained constant with respect to the per processor memory usage. The best performance achievable by this software on your system depends on a large variety of factors. The HPL package provides a testing and timing program to quantify the accuracy of the obtained solution as well as the time it took to compute it. 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.
#Linpack benchmark download portable#
High Performance Linpack (HPL) is a software package that solves a (random) dense linear system in double precision (64 bits) arithmetic on distributed-memory computers. It can thus be regarded as a portable as well as freely available implementation of the High Performance Computing Linpack Benchmark.
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