femtolisp
Petalisp
femtolisp | Petalisp | |
---|---|---|
10 | 17 | |
1,550 | 425 | |
- | - | |
0.0 | 8.5 | |
about 4 years ago | about 2 months ago | |
Scheme | Common Lisp | |
BSD 3-clause "New" or "Revised" License | GNU Affero General Public License v3.0 |
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femtolisp
- Petalisp: Elegant High Performance Computing
- fe: A tiny, embeddable language implemented in ANSI C
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From Common Lisp to Julia
> In short, Julia is very similar to Common Lisp, but brings a lot of extra niceties to the table
This probably because Jeff Bezanson, the creator of Julia, created a Lisp prior to Julia, which I think still exists inside Julia in some fashion
https://github.com/JeffBezanson/femtolisp
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Modern Python Performance Considerations
Well let's flip this around: do you think you could write a performant minimal Python in a weekend? Scheme is a very simple and elegant idea. Its power derives from the fact that smart people went to considerable pains to distill computation to limited set of things. "Complete" (i.e. rXrs) schemes build quite a lot of themselves... in scheme, from a pretty tiny core. I suspect Jeff Bezanson spent more than a weekend writing femtolisp, but that isn't really important. He's one guy who wrote a pretty darned performant lisp that does useful computation as a passion project. Check out his readme; it's fascinating: https://github.com/JeffBezanson/femtolisp
You simply can't say these things about Python (and I generally like Python!). It's truer for PyPy, but PyPy is pretty big and complex itself. Take a look at the source for the scheme or scheme-derived language of your choice sometime. I can't claim to be an expert in any of what's going on in there, but I think you'll be surprised how far down those parens go.
The claim I was responding to asserted that lisps and smalltalks can only be fast because of complex JIT compiling. That is trueish in practice for Smalltalk and certainly modern Javascript... but it simply isn't true for every lisp. Certainly JIT-ed lisps can be extremely fast, but it's not the only path to a performant lisp. In these benchmarks you'll see a diversity of approaches even among the top performers: https://ecraven.github.io/r7rs-benchmarks/
Given how many performant implementations of Scheme there are, I just don't think you can claim it's because of complex implementations by well-resourced groups. To me, I think the logical conclusion is that Scheme (and other lisps for the most part) are intrinsically pretty optimizable compared to Python. If we look at Common Lisp, there are also multiple performant implementations, some approximately competitive with Java which has had enormous resources poured into making it performant.
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CppCast: Julia
While it uses an Algol inspired syntax, it has the same approach to OOP programing as CLOS(Common Lisp Object System), with multi-methods and protocols, it has a quite powerfull macro system like Lisp, similar REPL experience, and underneath it is powerered by femtolisp.
- Julia and the Incarceration of Lisp
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What is the smallest x86 lisp?
For a real answer, other replies have already mentioned KiloLisp, but there's also femtolisp. Also, not exactly what you're asking for, but Maru is a very compact and elegant self-hosting lisp (compiles to x86).
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lisp but small and low level?Does it make sense?
Take a look at femtolisp It has some low level features and is quite small. There is also a maintenance fork at lambdaconservatory
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Lispsyntax.jl: A Clojure-like Lisp syntax for julia
A fun Julia easter egg I recently discovered.
Running 'julia --lisp' launches a femtolisp (https://github.com/JeffBezanson/femtolisp) interpreter.
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Wisp: A light Lisp written in C++
Reminds me of the femtolisp README :)
Almost everybody has their own lisp implementation. Some programmers' dogs and cats probably have their own lisp implementations as well. This is great, but too often I see people omit some of the obscure but critical features that make lisp uniquely wonderful. These include read macros like #. and backreferences, gensyms, and properly escaped symbol names. If you're going to waste everybody's time with yet another lisp, at least do it right damnit.
https://github.com/JeffBezanson/femtolisp
Petalisp
- Petalisp: Elegant High Performance Computing
- Is there a tutorial for automatic differentiation with petalisp?
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Is there a language with lisp syntax but C semantics?
While not "as fast as C" (C is not the absolute pinnacle of performance), Common Lisp is incredibly fast compared to the majority of programming languages around today. There is even a huge amount of ongoing work being done to make it faster still. We are seeing many interesting projects that make better use of the hardware in your computer (e.g. https://github.com/marcoheisig/Petalisp).
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Common Lisp Implementations in 2023
i think lisp-stat library is actually being developed. however one numerical cl library that doesnt get enough mention and is being constantly developed is petalisp for HPC
https://github.com/marcoheisig/Petalisp
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numericals - Performance of NumPy with the goodness of Common Lisp
However, if you have a lisp library that puts those semantics to use, then you could get it to employ magicl/ext-blas and cl-bmas to speed it up. (petalisp looks relevant, but I lack the background to compare it with APL.)
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New Lisp-Stat Release
> his means cl pagckages can be "done".
this is true if there is nothing functional that can be added to a package. however its very much not true for ml frameworks right now. new things are being added all the time in the field. however even in the package i linked you have the necessary ingredients for any deep learning model: cuda and back propagation. the other person mentioned convolution which i think is pretty trivial to implement but still, if you expect everything for you to be ready made then you should probably stick to tf and pytorch. if you want to explore the cutting edge and push the boundaries then i think common lisp is a good tool. as an aside it might also be interesting to note that a common lisp package (Petalisp) is being used for high performance computing by a german university
https://github.com/marcoheisig/Petalisp
- The Julia language has a number of correctness flaws
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When a young programmer who has been using C for several years is convinced that C is the best possible programming language and that people who don't prefer it just haven't use it enough, what is the best argument for Lisp vs C, given that they're already convinced in favor of C?
One trick is that Common Lisp can generate and compile code at runtime, whereas static languages typically do not have a compiler available at runtime. This lets you make your own lazy person's JIT/staged compiler, which is useful if some part of the problem is not known at compile-time. Such an approach has been used at least for array munging, type munging and regular expression munging.
What are some alternatives?
small-lisp - A very small lisp interpreter, that I may one day get working on my 8-bit AVR microcontroller.
awesome-cl - A curated list of awesome Common Lisp frameworks, libraries and other shiny stuff.
julia - The Julia Programming Language
JWM - Cross-platform window management and OS integration library for Java
Carp - A statically typed lisp, without a GC, for real-time applications.
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
Fennel - Lua Lisp Language
magicl - Matrix Algebra proGrams In Common Lisp.
sectorlisp - Bootstrapping LISP in a Boot Sector
lish - Lisp Shell
hissp - It's Python with a Lissp.
StatsBase.jl - Basic statistics for Julia