Cerebras’ new monster AI chip adds 1.4T transistors

Re:Cerebras’ new monster AI chip adds 1.4T transistors by BadInformatics on Friday April 23, 2021 @06:55PM (#26920200)

I'm not sure that's necessarily the domain of a low-level package like CUDA.jl though (which I assume you're referring to). That kind of interface is more the domain of higher-level packages like https://github.com/JuliaGPU/DaggerGPU.jl and to a lesser extent https://juliagpu.github.io/KernelAbstractions.jl/stable/. Moreover, the jury is still out on whether the built-in Distributed module is an ideal abstraction for every use-case (clusters, heterogeneous compute, etc.)

WRT Nx, my biggest question is how they'll crack the problem of still needing big balls of C++ and the shims everywhere to get acceleration. Creating a compiler that generates efficient GPU or other accelerator code is a massive research project with no clear winners, never mind the challenge of reconciling the very mutation-heavy needs of GPU compute with a mostly immutable language model.

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Re:Cerebras’ new monster AI chip adds 1.4T transistors by dnautics on Friday April 23, 2021 @08:10PM (#26920786)

There's plenty of mutability escape hatches in the erlang vm, and basically everyone who works in the language long enough is familiar with the idea of holding on to an immutable reference to a mutable thing. Like a database connection. Or a connection to a remote microservice. Or an ETS table.

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