Recent submissions

Comment · 3 years ago
Comment · 3 years ago
We are free to come up with myths about the name origin. My favorite one is that it comes from Julia character of George Orwell's 1984 novel :D
Comment · 5 years ago
This presentation is only a few days old.
Comment · 5 years ago
Comment · 6 years ago
This is very good news for people like me who would like to play with GPU kernels and arrays but usually have no access to NVIDIA/AMD hardware. (Although specific Intel hardware and Linux is being required for now.) I'm waiting for KernelAbstractions.jl integration.
Comment · 6 years ago
Comment · 6 years ago
Comment · 6 years ago
""" [...] it is possible to perform kernel-like operations without actually writing your own GPU kernels: a = CUDA.zeros(1024) b = CUDA.ones(1024) a.^2 .+ sin.(b) """ [https://juliagpu.gitlab.io/CUDA.jl/usage/overview/#The-CuArr...]

As far as I know, that example code creates an ad-hoc kernel that performs the computation in a single pass.

I honestly don't know if that is possible in other frameworks.

Comment · 7 years ago
Or maybe it should be "When on an online discussion someone mentions Julia, the probability of that discussion turning into a 0-based versus 1-based indexing discussion is 1"
Comment · 7 years ago
There seems to be a kind of Godwin's law for Julia that states that "When on an online discussion someone mentions Julia, the probability of a complaint about 1-based indexing is 1".
Comment · 8 years ago
That is pretty amazing! I wonder how would it look like if it was written in C. Anyone has some links to "Blobs" implementations in C?
Comment · 8 years ago
In part, the fact that there are major problems moving the "giant" to where one would like (speed), e.g., unladen swallow, pypy, pyston, ...

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