Pkg.jl
parca
Pkg.jl | parca | |
---|---|---|
5 | 18 | |
603 | 3,848 | |
1.0% | 1.9% | |
9.0 | 9.9 | |
3 days ago | 4 days ago | |
Julia | TypeScript | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Pkg.jl
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Julia 1.9 Highlights
There was a "bug" (or just unhandled caching case) that effected the Pluto notebook system that required precompilation each time. This is because Pluto notebooks kept a manifest (so they always instantiated with the same packages every time for full reproducibility) and the instantiation of that manifest triggered not just package running but also precompilation. That was fixed in https://github.com/JuliaLang/Pkg.jl/pull/3378, with a larger discussion in https://discourse.julialang.org/t/first-pluto-notebook-launc.... That should largely remove this issue as in included in the v1.9 release (it was first in v1.9-RC2 IIRC).
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Unable to load PDMats package.
The closest thing I got to is this and I don't even understand what they are saying.
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Why Fortran is easy to learn
Julia's compiler is made to be extendable. GPUCompiler.jl which adds the .ptx compilation output for example is a package (https://github.com/JuliaGPU/GPUCompiler.jl). The package manager of Julia itself... is an external package (https://github.com/JuliaLang/Pkg.jl). The built in SuiteSparse usage? That's a package too (https://github.com/JuliaLang/SuiteSparse.jl). It's fairly arbitrary what is "external" and "internal" in a language that allows that kind of extendability. Literally the only thing that makes these packages a standard library is that they are built into and shipped with the standard system image. Do you want to make your own distribution of Julia that changes what the "internal" packages are? Here's a tutorial that shows how to add plotting to the system image (https://julialang.github.io/PackageCompiler.jl/dev/examples/...). You could setup a binary server for that and now the first time to plot is 0.4 seconds.
Julia's arrays system is built so that most arrays that are used are not the simple Base.Array. Instead Julia has an AbstractArray interface definition (https://docs.julialang.org/en/v1/manual/interfaces/#man-inte...) which the Base.Array conforms to, and many effectively standard library packages like StaticArrays.jl, OffsetArrays.jl, etc. conform to, and thus they can be used in any other Julia package, like the differential equation solvers, solving nonlinear systems, optimization libraries, etc. There is a higher chance that packages depend on these packages then that they do not. They are only not part of the Julia distribution because the core idea is to move everything possible out to packages. There's not only a plan to make SuiteSparse and sparse matrix support be a package in 2.0, but also ideas about making the rest of linear algebra and arrays themselves into packages where Julia just defines memory buffer intrinsic (with likely the Arrays.jl package still shipped with the default image). At that point, are arrays not built into the language? I can understand using such a narrow definition for systems like Fortran or C where the standard library is essentially a fixed concept, but that just does not make sense with Julia. It's inherently fuzzy.
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MlJ.jl: A Julia Machine Learning Framework
This is exacerbated by the fact that Julia's Pkg.jl does not yet support conditional/optional dependencies [0]. A lot of these meta packages tend to pull everything but the kitchen sink.
[0]: https://github.com/JuliaLang/Pkg.jl/issues/1285
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Adding packages in Julia extremely painful
The LTS release is over two years old, and Julia has received a lot of developer attention since then, resulting in new features and performance improvements that tutorial authors don't want to do without. You can safely use the latest stable release (v1.5.3), although you may also want to apply the Git registry fix (https://github.com/JuliaLang/Pkg.jl/issues/2014#issuecomment-730676631) for further improvements in download/setup speed.
parca
- Seeing what a Go process does (like `set -x`)
- Julia 1.9 Highlights
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Track Code Efficiency during Development
Continuous profiling tools such as parca may be worth looking into for your use case.
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Hi everyone, How could you find the lines executed for a particular method call in any language (java, go..) using eBPF?
They were bought by Elastic, maybe they'll open source it. There's also https://github.com/parca-dev/parca
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How do you monitor your Go apps?
an alternative option to pyroscope to do continuos profiling in production could be parca.dev check and here
- Go garbage collector doesn't release memory
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How to observe an http web application in real time with pprof?
+1 to Parca.dev https://github.com/parca-dev/parca as continuos profiling tool in production
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Continuous Profiling in Kubernetes Using Pyroscope
Parca collects, stores and makes profiles available to be queried over time. It is open source and can be deployed on production environments as Parca focuses on sampling profiling two main types of profiles: tracing and sampling.
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Launch HN: ContainIQ (YC S21) – Kubernetes Native Monitoring with eBPF
Polar signals develops Parca [0] which is another eBPF observability tool, and Isovalent develops Cilium [1] which is built on eBPF as well. Genuinely curious if there are differences, or if eBPF only allows for specific observability functionality and each tool has it all.
[0]: https://github.com/parca-dev/parca
[1]: https://github.com/cilium/cilium
- Parca: Continuous profiling for analysis of CPU and memory usage over time
What are some alternatives?
Pluto.jl - 🎈 Simple reactive notebooks for Julia
pyroscope - Continuous Profiling Platform. Debug performance issues down to a single line of code [Moved to: https://github.com/grafana/pyroscope]
TriangularSolve.jl - rdiv!(::AbstractMatrix, ::UpperTriangular) and ldiv!(::LowerTriangular, ::AbstractMatrix)
pyroscope - Continuous Profiling Platform. Debug performance issues down to a single line of code
maptrace - Produce watertight polygonal vector maps by tracing raster images
pixie - Instant Kubernetes-Native Application Observability
AutoMLPipeline.jl - A package that makes it trivial to create and evaluate machine learning pipeline architectures.
pprof - pprof is a tool for visualization and analysis of profiling data
parca-demo - A collection of languages and frameworks profiled by Parca and Parca agent
profefe - Continuous profiling for long-term postmortem analysis
Fortran-code-on-GitHub - Directory of Fortran codes on GitHub, arranged by topic
grafana-operator - An operator for Grafana that installs and manages Grafana instances, Dashboards and Datasources through Kubernetes/OpenShift CRs