I saw this article earlier and thought it was funny that the author cited the enthusiasm for Julia at a convention for Julia as evidence that it is "emerging as a tool of choice". Anecdotally,...
I saw this article earlier and thought it was funny that the author cited the enthusiasm for Julia at a convention for Julia as evidence that it is "emerging as a tool of choice". Anecdotally, almost no one I know uses Julia seriously in a scientific context, and pretty much all large scale numerical work I am aware of is in C, C++, or Fortran, with smatterings of accelerated Python depending on the definition of "large". I would be interested of course in actual numbers of scientific papers based on Julia code.
None of this is to imply that Julia isn't good or anything, but I think the framing here gives the impression it is much more used than it is in reality.
I use it everyday, and while the need to call Python packages using PyCall.jl is a constant, as a tool to manipulate arrays and dataframes, for linear algebra computations, and for plotting I...
I use it everyday, and while the need to call Python packages using PyCall.jl is a constant, as a tool to manipulate arrays and dataframes, for linear algebra computations, and for plotting I prefer it over Python. It also appears that the language is designed in a way that packages written by people that never talked to each other can work together nicely with little to no work from the user.
But yeah, I agree that programming languages are marketed almost through hype alone, and that can have detrimental consequences.
Here's some random publications that used Julia (Julia's papers have about 2000 citations, but probably there's a lot of "method papers" in there) :...
Here's some random publications that used Julia (Julia's papers have about 2000 citations, but probably there's a lot of "method papers" in there) :
In this work, we infer an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia. Using over 1.3 million threads on 650,000 Intel Xeon Phi cores of the Cori Phase II supercomputer, Celeste achieves a peak rate of 1.54 DP PFLOP/s. Celeste is able to jointly optimize parameters for 188 M stars and galaxies, loading and processing 178 TB across 8192 nodes in 14.6 min. To achieve this, Celeste exploits parallelism at multiple levels (cluster, node, and thread) and accelerates I/O through Cori’s burst buffer. Julia’s native performance enables Celeste to employ high-level constructs without resorting to hand-written or generated low-level code (C/C++/Fortran) while still achieving petascale performance.
If you're doing serious scientific computing and you're not using Julia you're doing it wrong imo.
I saw this article earlier and thought it was funny that the author cited the enthusiasm for Julia at a convention for Julia as evidence that it is "emerging as a tool of choice". Anecdotally, almost no one I know uses Julia seriously in a scientific context, and pretty much all large scale numerical work I am aware of is in C, C++, or Fortran, with smatterings of accelerated Python depending on the definition of "large". I would be interested of course in actual numbers of scientific papers based on Julia code.
None of this is to imply that Julia isn't good or anything, but I think the framing here gives the impression it is much more used than it is in reality.
I use it everyday, and while the need to call Python packages using
PyCall.jlis a constant, as a tool to manipulate arrays and dataframes, for linear algebra computations, and for plotting I prefer it over Python. It also appears that the language is designed in a way that packages written by people that never talked to each other can work together nicely with little to no work from the user.But yeah, I agree that programming languages are marketed almost through hype alone, and that can have detrimental consequences.
Here's some random publications that used Julia (Julia's papers have about 2000 citations, but probably there's a lot of "method papers" in there) :
https://www.nature.com/articles/s41566-017-0089-9/
https://www.nature.com/articles/s41534-017-0017-3?linkId=37249498
https://www.sciencedirect.com/science/article/pii/S2405471217303861
https://www.sciencedirect.com/science/article/abs/pii/S0743731518304672
From the last one :
If you're doing serious scientific computing and you're not using Julia you're doing it wrong imo.