parca
julia
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parca | julia | |
---|---|---|
18 | 350 | |
3,833 | 44,510 | |
3.2% | 0.9% | |
9.9 | 10.0 | |
6 days ago | 6 days ago | |
TypeScript | Julia | |
Apache License 2.0 | MIT License |
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.
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
julia
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Top Paying Programming Technologies 2024
34. Julia - $74,963
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Optimize sgemm on RISC-V platform
I don't believe there is any official documentation on this, but https://github.com/JuliaLang/julia/pull/49430 for example added prefetching to the marking phase of a GC which saw speedups on x86, but not on M1.
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Dart 3.3
3. dispatch on all the arguments
the first solution is clean, but people really like dispatch.
the second makes calling functions in the function call syntax weird, because the first argument is privileged semantically but not syntactically.
the third makes calling functions in the method call syntax weird because the first argument is privileged syntactically but not semantically.
the closest things to this i can think of off the top of my head in remotely popular programming languages are: nim, lisp dialects, and julia.
nim navigates the dispatch conundrum by providing different ways to define free functions for different dispatch-ness. the tutorial gives a good overview: https://nim-lang.org/docs/tut2.html
lisps of course lack UFCS.
see here for a discussion on the lack of UFCS in julia: https://github.com/JuliaLang/julia/issues/31779
so to sum up the answer to the original question: because it's only obvious how to make it nice and tidy like you're wanting if you sacrifice function dispatch, which is ubiquitous for good reason!
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Julia 1.10 Highlights
https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md
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Best Programming languages for Data Analysis📊
Visit official site: https://julialang.org/
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Potential of the Julia programming language for high energy physics computing
No. It runs natively on ARM.
julia> versioninfo() Julia Version 1.9.3 Commit bed2cd540a1 (2023-08-24 14:43 UTC) Build Info: Official https://julialang.org/ release
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Rust std:fs slower than Python
https://github.com/JuliaLang/julia/issues/51086#issuecomment...
So while this "fixes" the issue, it'll introduce a confusing time delay between you freeing the memory and you observing that in `htop`.
But according to https://jemalloc.net/jemalloc.3.html you can set `opt.muzzy_decay_ms = 0` to remove the delay.
Still, the musl author has some reservations against making `jemalloc` the default:
https://www.openwall.com/lists/musl/2018/04/23/2
> It's got serious bloat problems, problems with undermining ASLR, and is optimized pretty much only for being as fast as possible without caring how much memory you use.
With the above-mentioned tunables, this should be mitigated to some extent, but the general "theme" (focusing on e.g. performance vs memory usage) will likely still mean "it's a tradeoff" or "it's no tradeoff, but only if you set tunables to what you need".
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Eleven strategies for making reproducible research the norm
I have asked about Julia's reproducibility story on the Guix mailing list in the past, and at the time Simon Tournier didn't think it was promising. I seem to recall Julia itself didnt have a reproducible build. All I know now is that github issue is still not closed.
https://github.com/JuliaLang/julia/issues/34753
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Julia as a unifying end-to-end workflow language on the Frontier exascale system
I don't really know what kind of rebuttal you're looking for, but I will link my HN comments from when this was first posted for some thoughts: https://news.ycombinator.com/item?id=31396861#31398796. As I said, in the linked post, I'm quite skeptical of the business of trying to assess relative buginess of programming in different systems, because that has strong dependencies on what you consider core vs packages and what exactly you're trying to do.
However, bugs in general suck and we've been thinking a fair bit about what additional tooling the language could provide to help people avoid the classes of bugs that Yuri encountered in the post.
The biggest class of problems in the blog post, is that it's pretty clear that `@inbounds` (and I will extend this to `@assume_effects`, even though that wasn't around when Yuri wrote his post) is problematic, because it's too hard to write. My proposal for what to do instead is at https://github.com/JuliaLang/julia/pull/50641.
Another common theme is that while Julia is great at composition, it's not clear what's expected to work and what isn't, because the interfaces are informal and not checked. This is a hard design problem, because it's quite close to the reasons why Julia works well. My current thoughts on that are here: https://github.com/Keno/InterfaceSpecs.jl but there's other proposals also.
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Getaddrinfo() on glibc calls getenv(), oh boy
Doesn't musl have the same issue? https://github.com/JuliaLang/julia/issues/34726#issuecomment...
I also wonder about OSX's libc. Newer versions seem to have some sort of locking https://github.com/apple-open-source-mirror/Libc/blob/master...
but older versions (from 10.9) don't have any lockign: https://github.com/apple-oss-distributions/Libc/blob/Libc-99...
What are some alternatives?
pyroscope - Continuous Profiling Platform. Debug performance issues down to a single line of code [Moved to: https://github.com/grafana/pyroscope]
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
pyroscope - Continuous Profiling Platform. Debug performance issues down to a single line of code
NetworkX - Network Analysis in Python
pixie - Instant Kubernetes-Native Application Observability
Lua - Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description.
pprof - pprof is a tool for visualization and analysis of profiling data
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
profefe - Continuous profiling for long-term postmortem analysis
Numba - NumPy aware dynamic Python compiler using LLVM
grafana-operator - An operator for Grafana that installs and manages Grafana instances, Dashboards and Datasources through Kubernetes/OpenShift CRs
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp