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0 pointspjmlp9mo ago0 comments

Lisp Machines from Xerox PARC, Texas Instruments and Genera proved their point, even if the market wasn't up to their price point versus UNIX graphical workstations.

Dylan was going to be Newton's system programming language, and while the language group lost the the C++ team (Apple had two competing teams for the Newton OS), it was still NewtonScript for everything userspace, and it was getting a JIT by the time the project was canceled.

Objective-C is dynamically typed beyond the common subset with C, and was used even to write NeXTSTEP drivers.

I don't know how much of a chance Julia has against CUDA/ROCm/C++, especially now that everyone on the GPU space has decided to give feature parity to Python on their hardware, via day one bindings to the compute libraries and JIT DSLs, so that makes Mojo even less of a chance than Julia has.

Julia has an established ecosystem, and presence on the scientific community with ties to MIT.

Python is the champion, and most folks writing CUDA/ROCm/C++ are already using it.

So who would be reaching out to Mojo, instead of Python JIT DSLs/bindings or Julia, when having Fortran, C, C++ allergy?

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MohamedMabrouk9mo ago

Creating DSL/Bindings for Julia or python to underlaying platform like CUDA is not really replacing it, it is yet adding another layer on top of the existing platforms or creating a prototyping envirnoment for research. The question is not can Julia interface with CUDA, it is can Julia replace C++/CUDA/ROCm in an end-to-end scenarios (preferably with portability across GPUs vendors)? if not then there is no comparison between the stated goals of Julia and Mojo. they are different langauges targeting completly different use cases.

ChrisRackauckas9mo ago

Julia does not just have bindings to CUDA. Native Julia code can compile to build .ptx kernels https://cuda.juliagpu.org/stable/development/kernel/. This same code can also generate kernels for AMD GPUs, Intel GPUs, and Metal.

We for example built software that generates kernels on-demand that embed user functions for all 4 of these systems and showed it's much faster than just CUDA bindings for array functions for certain nonlinear systems (https://www.sciencedirect.com/science/article/abs/pii/S00457...)

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