flexible-vectors
julia
flexible-vectors | julia | |
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4 | 351 | |
43 | 44,622 | |
- | 0.7% | |
2.8 | 10.0 | |
about 1 month ago | 1 day ago | |
WebAssembly | Julia | |
GNU General Public License v3.0 or later | MIT License |
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flexible-vectors
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Mojo – a new programming language for all AI developers
Wonderful language. Only complaint (so far) : SIMD should be named Vector and dispatched to whatever SIMD/vector pipeline the host offers, similar to Flexible Vectors proposal in WASM: https://github.com/WebAssembly/flexible-vectors/blob/main/pr...
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AVX 512 will be the future
Abstract vectorization instructions in wasm will make life a lot easier
https://github.com/WebAssembly/flexible-vectors/blob/main/pr... great proposal!
Mapping to whatever hardware is available as some sort of micro library
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Take More Screenshots
I think SIMD was a distraction to our conversation, most code doesn't use it and in the future the length agnostic, flexible vectors; https://github.com/WebAssembly/flexible-vectors/blob/master/... are a better solution. They are a lot like RVV; https://github.com/riscv/riscv-v-spec, research around vector processing is why RISC-V exists in the first place!
I was trying to find the smallest Rust Wasm interpreters I could find, I should have read the source first, I only really use wasmtime, but this one looks very interesting, zero deps, zero unsafe.
16.5kloc of Rust https://github.com/rhysd/wain
The most complete wasm env for small devices is wasm3
20kloc of C https://github.com/wasm3/wasm3
I get what you are saying as to be so small that there isn't a place of bugs to hide.
> “There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies, and the other way is to make it so complicated that there are no obvious deficiencies. The first method is far more difficult.” CAR Hoare
Even a 100 line program can't be guaranteed to be free of bugs. These programs need embedded tests to ensure that the layer below them is functioning as intended. They cannot and should not run open loop. Speaking of 300+ reimplementations, I am sure that RISC-V has already exceeded that. The smallest readable implementation is like 200 lines of code; https://github.com/BrunoLevy/learn-fpga/blob/master/FemtoRV/...
I don't think Wasm suffers from the base extension issue you bring up. It will get larger, but 1.0 has the right algebraic properties to be useful forever. Wasm does require an environment, for archival purposes that environment should be written in Wasm, with api for instantiating more envs passed into the first env. There are two solutions to the Wasm generating and calling Wasm problem. First would be a trampoline, where one returns Wasm from the first Wasm program which is then re-instantiated by the outer env. The other would be to pass in the api to create new Wasm envs over existing memory buffers.
See, https://copy.sh/v86/
MS-DOS, NES or C64 are useful for archival purposes because they are dead, frozen in time along with a large corpus of software. But there is a ton of complexity in implementing those systems with enough fidelity to run software.
Lua, Typed Assembly; https://en.wikipedia.org/wiki/Typed_assembly_language and Sector Lisp; https://github.com/jart/sectorlisp seem to have the right minimalism and compactness for archival purposes. Maybe it is sectorlisp+rv32+wasm.
If there are directions you would like Wasm to go, I really recommend attending the Wasm CG meetings.
https://github.com/WebAssembly/meetings
When it comes to an archival system, I'd like it to be able to run anything from an era, not just specially crafted binaries. I think Wasm meets that goal.
https://gist.github.com/dabeaz/7d8838b54dba5006c58a40fc28da9...
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Exploring SIMD performance improvements in WebAssembly
Thanks! Good points, I think in general the fixed-width "packed" SIMD ISAs have the downsides that you mentioned.
But it seems that WebAssembly doesn't have length-agnostic SIMD instructions yet. There is an open proposal to add this though: https://github.com/WebAssembly/flexible-vectors
julia
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Top Paying Programming Technologies 2024
34. Julia - $74,963
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Optimize sgemm on RISC-V platform
I don't believe there is any official documentation on this, but https://github.com/JuliaLang/julia/pull/49430 for example added prefetching to the marking phase of a GC which saw speedups on x86, but not on M1.
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Dart 3.3
3. dispatch on all the arguments
the first solution is clean, but people really like dispatch.
the second makes calling functions in the function call syntax weird, because the first argument is privileged semantically but not syntactically.
the third makes calling functions in the method call syntax weird because the first argument is privileged syntactically but not semantically.
the closest things to this i can think of off the top of my head in remotely popular programming languages are: nim, lisp dialects, and julia.
nim navigates the dispatch conundrum by providing different ways to define free functions for different dispatch-ness. the tutorial gives a good overview: https://nim-lang.org/docs/tut2.html
lisps of course lack UFCS.
see here for a discussion on the lack of UFCS in julia: https://github.com/JuliaLang/julia/issues/31779
so to sum up the answer to the original question: because it's only obvious how to make it nice and tidy like you're wanting if you sacrifice function dispatch, which is ubiquitous for good reason!
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Julia 1.10 Highlights
https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md
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Best Programming languages for Data Analysis📊
Visit official site: https://julialang.org/
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Potential of the Julia programming language for high energy physics computing
No. It runs natively on ARM.
julia> versioninfo() Julia Version 1.9.3 Commit bed2cd540a1 (2023-08-24 14:43 UTC) Build Info: Official https://julialang.org/ release
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Rust std:fs slower than Python
https://github.com/JuliaLang/julia/issues/51086#issuecomment...
So while this "fixes" the issue, it'll introduce a confusing time delay between you freeing the memory and you observing that in `htop`.
But according to https://jemalloc.net/jemalloc.3.html you can set `opt.muzzy_decay_ms = 0` to remove the delay.
Still, the musl author has some reservations against making `jemalloc` the default:
https://www.openwall.com/lists/musl/2018/04/23/2
> It's got serious bloat problems, problems with undermining ASLR, and is optimized pretty much only for being as fast as possible without caring how much memory you use.
With the above-mentioned tunables, this should be mitigated to some extent, but the general "theme" (focusing on e.g. performance vs memory usage) will likely still mean "it's a tradeoff" or "it's no tradeoff, but only if you set tunables to what you need".
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Eleven strategies for making reproducible research the norm
I have asked about Julia's reproducibility story on the Guix mailing list in the past, and at the time Simon Tournier didn't think it was promising. I seem to recall Julia itself didnt have a reproducible build. All I know now is that github issue is still not closed.
https://github.com/JuliaLang/julia/issues/34753
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Julia as a unifying end-to-end workflow language on the Frontier exascale system
I don't really know what kind of rebuttal you're looking for, but I will link my HN comments from when this was first posted for some thoughts: https://news.ycombinator.com/item?id=31396861#31398796. As I said, in the linked post, I'm quite skeptical of the business of trying to assess relative buginess of programming in different systems, because that has strong dependencies on what you consider core vs packages and what exactly you're trying to do.
However, bugs in general suck and we've been thinking a fair bit about what additional tooling the language could provide to help people avoid the classes of bugs that Yuri encountered in the post.
The biggest class of problems in the blog post, is that it's pretty clear that `@inbounds` (and I will extend this to `@assume_effects`, even though that wasn't around when Yuri wrote his post) is problematic, because it's too hard to write. My proposal for what to do instead is at https://github.com/JuliaLang/julia/pull/50641.
Another common theme is that while Julia is great at composition, it's not clear what's expected to work and what isn't, because the interfaces are informal and not checked. This is a hard design problem, because it's quite close to the reasons why Julia works well. My current thoughts on that are here: https://github.com/Keno/InterfaceSpecs.jl but there's other proposals also.
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Getaddrinfo() on glibc calls getenv(), oh boy
Doesn't musl have the same issue? https://github.com/JuliaLang/julia/issues/34726#issuecomment...
I also wonder about OSX's libc. Newer versions seem to have some sort of locking https://github.com/apple-open-source-mirror/Libc/blob/master...
but older versions (from 10.9) don't have any lockign: https://github.com/apple-oss-distributions/Libc/blob/Libc-99...
What are some alternatives?
wain - WebAssembly implementation from scratch in Safe Rust with zero dependencies
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
rust-wasm - A simple and spec-compliant WebAssembly interpreter
NetworkX - Network Analysis in Python
wai - a wasm interpreter written by rust
Lua - Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description.
tropy - Research photo management
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
WasmCert-Isabelle - A mechanisation of Wasm in Isabelle.
Numba - NumPy aware dynamic Python compiler using LLVM
simd-wasm-profiling - Exploring SIMD performance improvements in WebAssembly
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp