What will the the sum of the level of performance (in exaFLOPS) of the all 500 supercomputers in the TOP500 be according to their June 2021 list? This is an implementation of the High Performance Computing Linpack Benchmark. The TOP500 ranks high-performance computing (HPC) by recording how fast a computer system solves a dense n by n system of linear equations in double precision (64 bits) arithmetic on distributed-memory computers ( TOP500, 2019). 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. The project was started in 1993 and publishes an updated list of the supercomputers twice a year.
The TOP500 project collects and ranks system performance metrics of the most powerful non-distributed computer systems in the world. In the seven decades since the invention of the point-contact transistor at Bell Labs, relentless progress in the development of semiconductor devices - Moore’s law - has been achieved despite regular warnings from industry observers about impending limits. You can view all other questions in this round here. : 1.This question is part of the Maximum Likelihood Round of the Forecasting AI Progress Tournament. 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.Ĭode: Select all =
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. PASSEDįor 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 2020Īlex, 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?Ĭode: 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.
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. PASSEDįinished 1 tests with the following results:ġ tests completed and passed residual checks,Ġ tests completed and failed residual checks,Ġ 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 DenverĪn 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