Rmax linpack benchmark update#
Rmax linpack benchmark full#
Rmax linpack benchmark software#

For these systems we ask the supplier of the information to establish direct contact between the installation site and us to verify the given information. In addition to cross checking different sources of information, we select randomly a statistical representative sample of the first 500 systems of our database.

Rmax linpack benchmark update#

If there should be any changes in the performances given in the following table we will update them. For models where we did not receive the requested data, the performance of the next smaller system measured is used. In a few cases we interpolated between two measured system sizes or we scaled by cycle times. If Rmax from Table 3В of the LINPACK Report is not available, we use the TPP performance given in Table 1В of the LINPACK Report for solving a system of 1000 equations. N1/2 - Problem size for achieving half of Rmax

Rmax - Maximal LINPACK performance achieved Year - Year of installation/last major update Nworld - Position within the TOP500 rankingД¬omputer - Type indicated by manufacturer or vendor To keep the list as compact as possible, we show only a part of our information here: : 1.The TOP500 table shows the 500 most powerful commercially available computer systems known to us. The results for this is below (11.73 GFlops) VVV-VVV-VVV-VVV-VVV-VVV-VVV-VVV-VVV-VVV-VVV-VVV-VVV-VVV-VVV. This could also be one of those weird situations where native 64-bit runs slower than 32-bit compatibility mode.Д¬ode: Select all =

Rmax linpack benchmark software#

It would be nice to know whether the differences in our timing results are related to the 8GB versus 2GB hardware, the matrix size, or some other software difference such as using the version 10.1 gcc compiler. The result is still greater than 10 GFLOPS. PASSEDДЇor some reason the version of MPICH I installed doesn't allow me to specify a rank file, so I can't check if that makes a difference. HPL_pdgesv() end time Fri Jun 5 04:54:15 2020ДЄlex, since I can't run the large matrix size with my 2GB Pi, would it be possible for you to try N=8000 and compare?Д¬ode: Select all $ export OPENBLAS_NUM_THREADS=1 I'll try to optimize the build and then post more details in case anyone wants to continue improving things.

Rmax linpack benchmark full#

Likely the smaller matrix size gives slightly faster results than using the full memory size on the 8GB model. Note that MPICH was installed to use the slower ch3 network device rather than shared memory, but I think the run was using OpenBLAS threads rather than MPI anyway.

PASSEDДЇinished 1 tests with the following results:ДЎ tests completed and passed residual checks,Д tests completed and failed residual checks,Д tests skipped because of illegal input values. Computational tests pass if scaled residuals are less than 16.0 The relative machine precision (eps) is taken to be 1.110223e-16 The following scaled residual check will be computed:

The matrix A is randomly generated for each test. The following parameter values will be used: Gflops : Rate of execution for solving the linear system. Time : Time in seconds to solve the linear system. N : The order of the coefficient matrix A. Modified by Julien Langou, University of Colorado DenverДЄn explanation of the input/output parameters follows: Modified by Piotr Luszczek, Innovative Computing Laboratory, UTK Clint Whaley, Innovative Computing Laboratory, UTK HPLinpack 2.3 - High-Performance Linpack benchmark - December 2, 2018