Skip to main content

Linpack FLOPS Benchmark

The numbers I've been reporting for Javascript and Perl performance need a context. Are they running on a Commodore 64, or a super-computer? Nope, it's my laptop, and here's what Linpack has to report about it: 4 core, 8 thread (Intel core i7) at 3.389 GHz. Peak performance is 46 GFLOPS.

Javascript and Perl are both single-threaded, so you only get 1/8 peak performance, 5 3/4 GFLOPS. That's still 1000 times the performance we actually achieve on more convenient languages. You have to sacrifice some things to gain benefits. On the optimistic side, a moderately fast modern computer sunning Perl or Javascript is almost as fast as  Sparc-10 running LINPACK.

Intel(R) LINPACK data

Current date/time: Fri Jun 12 01:19:06 2015

CPU frequency:    3.389 GHz
Number of CPUs: 1
Number of cores: 4
Number of threads: 8

Parameters are set to:

Number of tests                             : 15
Number of equations to solve (problem size) : 1000  2000  5000  10000 15000 18000 20000 22000 25000 26000 27000 30000 35000 40000 45000
Leading dimension of array                  : 1000  2000  5008  10000 15000 18008 20016 22008 25000 26000 27000 30000 35000 40000 45000
Number of trials to run                     : 4     2     2     2     2     2     2     2     2     2     1     1     1     1     1    
Data alignment value (in Kbytes)            : 4     4     4     4     4     4     4     4     4     4     4     1     1     1     1    

Maximum memory requested that can be used = 16200901024, at the size = 45000

============= Timing linear equation system solver =================

Size   LDA    Align. Time(s)    GFlops   Residual     Residual(norm)
1000   1000   4      0.019      35.5628  1.029343e-12 3.510325e-02
1000   1000   4      0.018      36.2016  1.029343e-12 3.510325e-02
1000   1000   4      0.018      36.1857  1.029343e-12 3.510325e-02
1000   1000   4      0.018      36.2075  1.029343e-12 3.510325e-02
2000   2000   4      0.136      39.2932  4.298950e-12 3.739560e-02
2000   2000   4      0.135      39.4407  4.298950e-12 3.739560e-02
5000   5008   4      1.923      43.3503  2.581643e-11 3.599893e-02
5000   5008   4      1.925      43.3232  2.581643e-11 3.599893e-02
10000  10000  4      14.781     45.1176  9.603002e-11 3.386116e-02
10000  10000  4      14.825     44.9833  9.603002e-11 3.386116e-02
15000  15000  4      49.465     45.4959  2.042799e-10 3.217442e-02
15000  15000  4      49.541     45.4258  2.042799e-10 3.217442e-02
18000  18008  4      85.142     45.6727  2.894987e-10 3.170367e-02
18000  18008  4      85.978     45.2284  2.894987e-10 3.170367e-02
20000  20016  4      115.975    45.9938  4.097986e-10 3.627616e-02
20000  20016  4      116.138    45.9292  4.097986e-10 3.627616e-02
22000  22008  4      156.203    45.4512  4.548092e-10 3.331299e-02
22000  22008  4      156.091    45.4840  4.548092e-10 3.331299e-02
25000  25000  4      227.453    45.8025  6.089565e-10 3.462917e-02
25000  25000  4      227.769    45.7390  6.089565e-10 3.462917e-02
26000  26000  4      254.249    46.0913  6.669421e-10 3.506981e-02
26000  26000  4      254.840    45.9845  6.669421e-10 3.506981e-02
27000  27000  4      284.795    46.0804  6.672171e-10 3.253690e-02
30000  30000  1      414.983    43.3796  8.421348e-10 3.319704e-02
35000  35000  1      732.317    39.0347  1.085509e-09 3.151068e-02
40000  40000  1      1479.396   28.8428  1.466774e-09 3.262155e-02
45000  45000  1      2764.282   21.9782  1.711494e-09 3.011194e-02

Performance Summary (GFlops)

Size   LDA    Align.  Average  Maximal
1000   1000   4       36.0394  36.2075 
2000   2000   4       39.3669  39.4407 
5000   5008   4       43.3367  43.3503 
10000  10000  4       45.0504  45.1176 
15000  15000  4       45.4608  45.4959 
18000  18008  4       45.4506  45.6727 
20000  20016  4       45.9615  45.9938 
22000  22008  4       45.4676  45.4840 
25000  25000  4       45.7708  45.8025 
26000  26000  4       46.0379  46.0913 
27000  27000  4       46.0804  46.0804 
30000  30000  1       43.3796  43.3796 
35000  35000  1       39.0347  39.0347 
40000  40000  1       28.8428  28.8428 
45000  45000  1       21.9782  21.9782 

End of tests



Comments

Popular posts from this blog

BASH Matrix Multiplication

tl;dr Bash is not the language for math-intensive operations. REPS=$1; FILE_1=$2; FILE_2=$3 OUTFILENAME=$4; readonly COLS=`head -1 $FILE_1 | wc -w`; readonly ROWS=`cat $FILE_1 | wc -l`; # echo "rows is $ROWS; cols is $COLS" if [[ $ROWS != $COLS ]]; then echo "Expecting square matrices, " \ "but rows = $ROWS, cols = $COLS\n"; exit 1; fi # -------------------------------------------------- # SUBROUTINES # function outputMatrix() { local matrixname=$1; local matrix; local elem; echo "matrix is '$matrixname'."; eval matrix=\( \${${matrixname}[@]} \); local i=0; for elem in "${matrix[@]}"; do echo -n "$elem "; if (( ++i == $COLS )); then echo ''; i=0; fi done } function multiply() { declare -a product; local M=$1 N=$2; local i j k idx1 idx2 idx3; for ((i=0; i < $ROWS; i++ )); do for ((j=0; j<$COLS; j++)); do

Perl5, Moxie and Enumurated Data Types

Moxie - a new object system for Perl5 Stevan Little created the Moose multiverse to upgrade the Perl 5 programming language's object-oriented system more in line with the wonderfull world of Perl 6. Unfortunately, it's grown into a bloated giant, which has inspired light-weight alternatives Moos, Moo, Mo, and others. Now he's trying to create a modern, efficient OO system that can become built into the language. I've seen a few of his presentations at YAPC (Yet Another Perl Conference, now known as TPC, The Perl Conference), among them ‎p5 mop final final v5 this is the last one i promise tar gz <. So I was delighted to recently see an announcement of the module Moxie, and decided to try implementing a card game. While the package provides some POD documentation about the main module, Moxie, it doesn't actually explain the enum package, Moxie::Enum. But delving into the tests directory reveals its secrets. Creating an Enum package Ranks { use

Creating Perl5 Objects with Moxie

Having in the previous article prepared data types for car suits and card ranks, I can now combine them to provide a playing card class, using Stevan Little's Moxie module (version 0.04, so definitely early days.) The goal is to provide an object-oriented paradigm to the Perl 5 programming language which is more sophisticated, more powerful and less verbose than manually bless() -ing hashes. To achieve that goal it needs to be faster and light-weight compared to Moose. Currently, Moxie.pm and and MOP.pm are add-on modules, but eventually, when they are more complete, when the wrinkles have been ironed out, and when they have gained acceptance and a community of users, they might be merged into the Perl core. One significant feature of Moxie is that it reduces boilerplate code. You don't have to specify warnigns or strict . As well, the features or the perl you are using are enabled, among them say , state , signatures , and post_deref . A Simple Moxie Class packag