cl-cuda
criterium
Our great sponsors
cl-cuda | criterium | |
---|---|---|
5 | 8 | |
270 | 1,160 | |
- | - | |
0.0 | 0.0 | |
almost 3 years ago | over 1 year ago | |
Common Lisp | Clojure | |
MIT 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.
cl-cuda
-
Why Lisp? (2015)
> You can write a lot of macrology to get around it, but there's a point where you want actual compiler writers to be doing this
this is not the job of compiler writers (although writing macros is akin to writing a compiler but i do not think that this is what you mean). in julia the numerical programming packages are not part of the standard library and a lot of it is wrappers around C++ code especially when the drivers to the underlining hardware are closed-source [0]. also here is the similar library in common lisp [1]
[0] https://github.com/JuliaGPU/CUDA.jl
[1] https://github.com/takagi/cl-cuda
- Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
-
Hacker News top posts: Aug 14, 2021
A Common Lisp Library to Use Nvidia CUDA\ (0 comments)
- A Common Lisp Library to Use Nvidia CUDA
-
Machine Learning in Lisp
Personally, I've been relying on the stream-based method using py4cl/2, mostly because I did not - and perhaps do not - have the knowledge and time to dig into the CFFI based method. The limitation is that this would get you less than 10000 python interactions per second. That is sufficient if you will be running a long running python task - and I have successfully run trivial ML programs using it, but any intensive array processing gets in the way. For this later task, there are a few emerging libraries like numcl and array-operations without SIMD (yet), and numericals using SIMD. For reasons mentioned on the readme, I recently cooked up dense-arrays. This has interchangeable backends and can also use cl-cuda. But barring that, the developer overhead of actually setting up native-CFFI ecosystem is still too high, and I'm back to py4cl/2 for tasks beyond array processing.
criterium
-
Noob has simple program problem.
(criterium does not work here yet b.t.w., but it probably will be working soon)
-
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.
-
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.
-
Notes on Optimizing Clojure Code: Overview
I am just going to leave this here - https://github.com/hugoduncan/criterium
-
"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.
-
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
-
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 .
-
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?
numcl - Numpy clone in Common Lisp
clojure - The Clojure programming language
numericals - CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental]
skiko - Kotlin MPP bindings to Skia
py4cl - Call python from Common Lisp
JWM - Cross-platform window management and OS integration library for Java
hash-array-mapped-trie - A hash array mapped trie implementation in c.
magicl - Matrix Algebra proGrams In Common Lisp.
rewrite - Automated mass refactoring of source code.
LoopVectorization.jl - Macro(s) for vectorizing loops.
quilc - The optimizing Quil compiler.