magicl
criterium
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magicl | criterium | |
---|---|---|
14 | 8 | |
225 | 1,160 | |
0.4% | - | |
5.4 | 0.0 | |
6 months ago | over 1 year ago | |
Common Lisp | Clojure | |
BSD 3-clause "New" or "Revised" License | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
magicl
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A tutorial quantum interpreter in 150 lines of Lisp
(Link didn't work for me)
https://github.com/quil-lang/magicl/blob/master/src/high-lev...
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Why Lisp?
use MAGICL. [1] It is optionally and transparently accelerated by BLAS/LAPACK.
[1] https://github.com/quil-lang/magicl/blob/master/doc/high-lev...
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How fast can you multiply matrices using only common lisp?
Maybe have a look at how magicl does this?
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A software engineer's circuitous journey to calculate eigenvalues
This is essentially the first option, which is already supported by MAGICL by loading MAGICL/EXT-LAPACK [1].
[1] https://github.com/quil-lang/magicl#extensions
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Uncle Stats Wants You
I think what the magicl team has done is brilliant - allowing multiple implementations is awesome.
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Good Lisp libraries for math
Second up is magicl, especially useful if performance is a concern. This might not be as extensive as numcl, but it's been battle tested in the industry over the last decade or so. Because this uses generic functions, so long as you are using not-very-small arrays, performance should not be a concern for you. And even if you are, you could write your own functions that use the low-level functions that magicl's backends define. Otherwise performance can be at par with numpy.
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Why is python numpy *so* much faster than lisp in this example?
This Dev How-To describes (I hope in enough detail) how to add these specialized routines to MAGICL.
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CL-AUTOWRAP generated (C)BLAS wrapper in QUICKLISP
I agree... and I do don't want be the person who has not rallied. I just took a look at guicho's issue from 2019. And here, you yourself have admitted that the high level interface is less than ideal and needs more work. However, the very point that magicl is an industry standard could imply that potentially radical backward-incompatible changes can be hard. But, honestly, I want to discuss this, time permitting!
- Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
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Anybody using Common Lisp or clojure for data science
Common Lisp is a great language to build new tools for data science, but currently has pretty awful library support existing data science workflows. Common Lisp is sorely lacking in high-quality statistics, plotting, and sparse arrays. There’s been a long work-in-progress library to bring flexible and high-performance linear algebra to Lisp, but it needs more contributors.
criterium
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Noob has simple program problem.
(criterium does not work here yet b.t.w., but it probably will be working soon)
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Question about high execution time
criterium, specifically the quick-bench function, will actually run multiple samples an provide a mean runtime (as well as other useful stats) so you can get an idea of what a jit'd warmed up performance looks like. time is great in a pinch, but you end up needing to run it multiple times to ensure optimizations are kicking and and other artifacts (like gc) aren't throwing the results.
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Logging in Clojure: jar tidiness
I'm going to leave tooling out of this and run everything through a repl on the command line right from the jar. One other thing I want to do is include the incredible criterium library so we can profile. I'm deliberately including criterium separately like this because you shouldn't have a dev-time tool like criterium in an uberjar. And knowing how to easily combine other jars with your real production jar can be very helpful. I grabbed the jar from my .m2 cache.
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Notes on Optimizing Clojure Code: Overview
I am just going to leave this here - https://github.com/hugoduncan/criterium
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"The Genuine Sieve of Eratosthenes"
where crit is criterium. As you can see, you're spending most of your time in the seq transformation part.
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A casual Clojure / Common Lisp code/performance comparison
It's better to benchmark with something like criterium. time is a bit inaccurate. Though, if it's really 15 seconds, I guess will not be that big of a difference
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Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
>>> One of Clojure's biggest weaknesses in practice is that breaking in to those functional structures to figure out where the time is being spent or to debug them is harder than in other languages. This is a natural trade-off of developing a terse and powerful language.
Not that hard if you use something like YourKit. There's also a quite good Clojure library https://github.com/hugoduncan/criterium .
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Clojure, Faster
Criterium (the benchmarking library used here) uses multiple runs to obtain tighter bounds on amortized performance, as well as techniques to amortize the effects of garbage collection and JIT compilation. See https://github.com/hugoduncan/criterium for a brief overview, as well as links to the pitfalls and statistical techniques involved in JVM benchmarking.
What are some alternatives?
lisp-matrix - A matrix package for common lisp building on work by Mark Hoemmen, Evan Monroig, Tamas Papp and Rif.
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
py4cl - Call python from Common Lisp
clojure - The Clojure programming language
Petalisp - Elegant High Performance Computing
skiko - Kotlin MPP bindings to Skia
hash-array-mapped-trie - A hash array mapped trie implementation in c.
JWM - Cross-platform window management and OS integration library for Java
april - The APL programming language (a subset thereof) compiling to Common Lisp.
quilc - The optimizing Quil compiler.