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How do you compare the performace of valarrays vs built-in arrays?


How do you compare the performace of valarrays vs built-in arrays?

By : sqlquestion
Date : January 10 2021, 01:57 PM
wish helps you valarray was intended to improve the chances of getting good optimization. For better or worse, it's become something of the forgotten step-child of the standard library; I don't know of any implementations that do much to take advantage of what it provides. Worse, the design was really designed for vector processors, and doesn't work very well with processors that use caching heavily -- which nearly everything anymore.
I don't know of any really serious benchmarks, but in my (admittedly, quite informal) testing, it's about even with a built-in array (or std::vector) as long as you're dealing with small amounts of data, but if you have enough data that it doesn't all fit in the cache, reasonably careful use of a built-in array or std::vector will usually be faster.
code :


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How to Compare two exe files One built from the old code now and the existing exe that was built a few months ago?

How to Compare two exe files One built from the old code now and the existing exe that was built a few months ago?


By : Better Wakeupnow
Date : March 29 2020, 07:55 AM
To fix this issue If all you're looking to do is verify the binaries are identical, then you could use a utility like md5sum or sha1sum which will effectively hashes the binary. If the hashes for each binary are the same, then the chances are the files are identical.
In Perl, is there a built in way to compare two arrays for equality?

In Perl, is there a built in way to compare two arrays for equality?


By : Anurag Chauhan
Date : March 29 2020, 07:55 AM
around this issue There is the new smart match operator:
code :
#!/usr/bin/perl

use 5.010;
use strict;
use warnings;

my @x = (1, 2, 3);
my @y = qw(1 2 3);

say "[@x] and [@y] match" if @x ~~ @y;
#!/usr/bin/perl

use strict;
use warnings;

use List::AllUtils qw( each_arrayref );

my @x = qw(1 2 3);
my @y = (1, 2, 3);

print "[@x] and [@y] match\n" if elementwise_eq( \(@x, @y) );

sub elementwise_eq {
    my ($xref, $yref) = @_;
    return unless  @$xref == @$yref;

    my $it = each_arrayref($xref, $yref);
    while ( my ($x, $y) = $it->() ) {
        return unless $x eq $y;
    }
    return 1;
}
#!/usr/bin/perl

use strict;
use warnings;

use Array::Compare;
use Benchmark qw( cmpthese );
use List::AllUtils qw( each_arrayref );

my @x = 1 .. 1_000;
my @y = map { "$_" } 1 .. 1_000;

my $comp = Array::Compare->new;

cmpthese -5, {
    iterator => sub { my $r = elementwise_eq(\(@x, @y)) },
    array_comp => sub { my $r = $comp->compare(\(@x, @y)) },
};
             Rate   iterator array_comp
iterator    246/s         --       -75%
array_comp 1002/s       308%         --
my @x = map { rand } 1 .. 1_000;
my @y = map { rand } 1 .. 1_000;
              Rate array_comp   iterator
array_comp   919/s         --       -98%
iterator   52600/s      5622%         --
my @x = 1 .. 20, map { rand } 1 .. 1_000;
my @y = 1 .. 20, map { rand } 1 .. 1_000;
              Rate   iterator array_comp
iterator   10014/s         --       -23%
array_comp 13071/s        31%         --
Performace of large 3D arrays: contiguous 1D storage vs T***

Performace of large 3D arrays: contiguous 1D storage vs T***


By : 박성운
Date : March 29 2020, 07:55 AM
This might help you These are not apples-to-apples comparisons: a flat array is just that - a flat array, which your code partitions into segments according to some logic of linearizing a rectangular 3D array. You access an element of an array with a single dereference, plus a handful of math operations.
float***, on the other hand, lets you keep a "jagged" array of arrays or arrays, so the structure that you can represent inside such an array is a lot more flexible. Naturally, you need to pay for that flexibility with additional CPU cycles required for dereferencing pointers to pointers to pointers, then a pointer to pointer, and finally a pointer (the three pairs of square brackets in the code).
Associative Arrays: Python vs Perl vs Awk Performace

Associative Arrays: Python vs Perl vs Awk Performace


By : Seong-Min Kang
Date : March 29 2020, 07:55 AM
To fix the issue you can do When you have huge data files and you want to work with them efficiently, you're probably best off throwing them into a SQLite database, indexing them, and then querying them. See my answer about CSV vs SQLite performance for more on that.
Create a table for the data (stuff is a terrible name, but I don't know what this data is, so it's "stuff").
code :
create table stuff (
    key1 text,
    key2 text,
    value real
);
sqlite> .mode csv
sqlite> .import file1 stuff
sqlite> .import file2 stuff
sqlite> .import file3 stuff
create index stuff_key on stuff (key1, key2);
select value
from stuff
where key1 = "NM_001011874.1" and
      key2 = "XR_104537.1"

-0.929524370261122
-0.11254525414
-0.41254525414
insert into stuff (key1, key2, value)
values ("NM_204958293.2", "XR_29238498.3", -239.2),
       ("NM_2904892.3", "XR_3093.0", 9482.39);
how to compare mmap and read performace

how to compare mmap and read performace


By : Balaji
Date : March 29 2020, 07:55 AM
it helps some times This is somewhat speculation because I probably haven't thought of all the implications:
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