…but with a more pythonic syntax: Mojo 🔥: Programming language for all of AI

[image] Akatz: LV was re-written in Cpp So LoopModels is written in C++, while LoopVectorization was in Julia? What happened? That seems opposite to the general trend of moving things into Julia. Does this have to do with fundamental weaknesses in Julia? I’m quite surprised. Edit: I guess ac…

[image] Akatz: Actually, something better than LoopVectorization. I discussed this very thing with Chris on the discord. LV was re-written in Cpp, no need to do that given Mojo’s extensive parametric zig like comptime system. Coud you give more details on this “something”? What would the Mojo…

In my opinion, the main thing i’m looking for in a next-gen language that solves the 2 language problem is a compiler that assures that type stable code has top notch performance. For example, I think it is too hard to write differentiable code using arrays that doesn’t allocate in the forward and …

Related article: fast.ai - Mojo may be the biggest programming language advance in decades

[image] jt1: But other than that, It doesn’t seem that Mojo is improving on anything else in a significant way. I don’t really know what the significance is, but Mojo apparently has a similar relationship to MLIR as Julia has to LLVM. If someone can explain the ramifications of this, I’d be i…

MLIR is a layer over LLVM. It’s literally hosted on the LLVM website: https://mlir.llvm.org/ It’s worth understanding Modular in the context of Chris Lattner’s life: He created LLVM in grad school with his advisor. This work led to the creation of Clang after he joined Apple. Later he worked at A…

That’s interesting. But does it give Mojo some advantage over Julia? Can (or should) Julia ‘transition’ to MLIR? Is Julia ‘stuck with a lesser engine’? Or is this overblown, and Julia can reap the benefits of not being shackled by the Python legacy? (I must admit I’m seeing this a bit in a competit…

Let’s split this into two points: MLIR is meant to simplify optimizations that hard to do in LLVM (e.g. LLVM doesn’t have constructs for nd-array optimizations built in). Perhaps it’s useful to Julia, but Julia could always use it if it wanted since it’s just an additional stage between user code …

[image] DNF: if Mojo delivers on its promises, we will all be stuck with Python and its awkward syntax forever We’ve seen that people love Python so much they will bear any development hardship to keep using it. If Mojo delivers some part of some of it’s promises, the Python world will be in …

Did Modular just reinvent Julia?

Offtopic

stevengj May 4, 2023, 12:01pm 27

You can use cache-oblivious tiling instead: Julia matrix-multiplication performance - #12 by stevengj

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Did Modular just reinvent Julia?

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