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Changes for 0.004 - 2024-03-06

  • Update to AppBase::Sort 0.003.
  • [doc] Fix example.


Collection of CLI utilities for Sah and Data::Sah

Changes for 0.484 - 2024-03-06

  • Rename scripts: list-sah-schemas-modules -> list-sah-schemabundle-modules, list-sah-pschemas-modules -> list-sah-pschemabundle-modules.
  • Tweak examaple summary.


Audit CPAN distributions for known vulnerabilities

Changes for 20240414.001 - 2024-04-15T00:01:30Z

  • data update for 2024-04-14



Cross-platform executor for parallel tasks executed in forked processes

Changes for 0.02 - 2024-04-14

  • Fix a deletion order bug



Prior releases of the 6.x line relied on Lexical::Types, which was a major performance pessimisation over the 5.x releases.

6.0.4 relies on a simple source filter instead, which restores performance levels back to expected levels.

More benchmarks added to the test suite validate the dependency changes.

submitted by /u/joesuf4
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HTTP/2 Dynamic Benchmarks (PHP vs. ModPerl2), 2024 edition.

I ram these about four years ago, and the time differentials were about the same then as now. Monolithic POSIX-threaded server architectures like mp2 + mpm_event will always dominate in low-latency/scalability HTTP/2 benchmarks because they leverage zero-copy in the runtime.

Anyways, sexy terminal graphs with smag to enjoy!

submitted by /u/joesuf4
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Some basic stat computations with Perl , Python and RLessons learned:
A) Performance freaks to stop using #rstat 's runif for random generation. The Hoshiro random number generator arxiv.org/abs/1805.01407 is 10x faster.
Implementations in #perl 's #PDL, #rstats (dqrng) and #python #numpy are within 20% of each other

B) But does it make a difference in applications? To get to the bottom of this, I coded a truncated random variate generator in #rstats and #perl using #pdl (as well as standard u/perl) using the #GSL packages metacpan.org/pod/PDL::GSL::CDF & metacpan.org/pod/Math::GSL for accessing the CDF & quantile functions. In this context, it's the calculation of the #CDF that is the computationally intensive part, not the drawing of the random number itself.
Well even in these case, the choice of the generator did matter. Note that the fully vectorized #PDL #perl versions were faster than #rstats

C) I should probably blog about these experiments at some point. Note that #pdl (but not base #perl) are rather competitive choices for large array processing with numerical operations. I mostly stay away of #python , but would not surprise me that for compute intensive stuff (where the heavy duty work is done in C/C++), it does not matter (much) which high level language one uses to build data applications

preview.redd.it/qn00sx78gbuc1.…

preview.redd.it/4by4jbh9gbuc1.…

submitted by /u/ReplacementSlight413
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