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I dont understand the binary distribution requirement. For me julia is replacing python applications, and i distribute those as packages via poetry build and py

I dont understand the binary distribution requirement. For me julia is replacing python applications, and i distribute those as packages via poetry build and pypi.

ubj 3y ago
AoT compiled binaries and libraries are fundamental building blocks for distributing software and running it quickly without latency. Julia itself calls AoT binaries, for example when calling Linear Algebra functions [1]. Chris Rackauckas's team had to leverage AoT compilation (i.e. system images) to reduce TTFX on DifferentialEquations.jl [2]. PyTorch and Tensorflow run without TTFX because they use AoT compiled binaries. What's more, most end users don't have to compile anything on their computer--they can simply download the binaries and libraries. For small scripts, yes you can simply distribute the package and have the user recompile on the fly. But imagine if your operating system had to recompile every time you turned the computer off. Or if an autonomous vehicle had to recompile an unseen patch of code while in motion. [1] https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/#Standard-functions https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/#Stand... [2] https://sciml.ai/news/2022/09/21/compile_time/ https://sciml.ai/news/2022/09/21/compile_time/
- ChrisRackauckas 3y ago
  > Chris Rackauckas's team had to leverage AoT compilation (i.e. system images) to reduce TTFX on DifferentialEquations.jl [2]. In 2022 with Julia v1.8. See the edit from September. With Julia v1.10 it's mostly there without system image building since v1.9 added AoT compilation of packages during precompilation time and then v1.10 did a major improvement to that system. So as you said, AoT is required, though Julia now includes AoT as part of its package build system. I tend to not build system images anymore because of that, though there are a few more steps to get it to fully AoT (i.e. some load time reductions).

I dont understand the binary distribution requirement. For me julia is replacing python applications, and i distribute those as packages via poetry build and pypi.

ubj 3y ago

AoT compiled binaries and libraries are fundamental building blocks for distributing software and running it quickly without latency. Julia itself calls AoT binaries, for example when calling Linear Algebra functions [1]. Chris Rackauckas's team had to leverage AoT compilation (i.e. system images) to reduce TTFX on DifferentialEquations.jl [2]. PyTorch and Tensorflow run without TTFX because they use AoT compiled binaries. What's more, most end users don't have to compile anything on their computer--they can simply download the binaries and libraries. For small scripts, yes you can simply distribute the package and have the user recompile on the fly. But imagine if your operating system had to recompile every time you turned the computer off. Or if an autonomous vehicle had to recompile an unseen patch of code while in motion. [1] https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/#Standard-functions https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/#Stand... [2] https://sciml.ai/news/2022/09/21/compile_time/ https://sciml.ai/news/2022/09/21/compile_time/

ChrisRackauckas 3y ago
> Chris Rackauckas's team had to leverage AoT compilation (i.e. system images) to reduce TTFX on DifferentialEquations.jl [2]. In 2022 with Julia v1.8. See the edit from September. With Julia v1.10 it's mostly there without system image building since v1.9 added AoT compilation of packages during precompilation time and then v1.10 did a major improvement to that system. So as you said, AoT is required, though Julia now includes AoT as part of its package build system. I tend to not build system images anymore because of that, though there are a few more steps to get it to fully AoT (i.e. some load time reductions).