zlib-ng
NumPy
zlib-ng | NumPy | |
---|---|---|
13 | 272 | |
1,445 | 26,413 | |
1.2% | 1.1% | |
9.3 | 10.0 | |
11 days ago | 3 days ago | |
C | Python | |
zlib License | GNU General Public License v3.0 or later |
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.
zlib-ng
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Show HN: Pzip- blazing fast concurrent zip archiver and extractor
Please note that allowing for 2% bigger resulting file could mean huge speedup in these circumstances even with the same compression routines, seeing these benchmarks of zlib and zlib-ng for different compression levels:
https://github.com/zlib-ng/zlib-ng/discussions/871
IMO the fair comparison of the real speed improvement brought by a new program is only between the almost identical resulting compressed sizes.
- Intel QuickAssist Technology Zstandard Plugin for Zstandard
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Introducing zune-inflate: The fastest Rust implementation of gzip/Zlib/DEFLATE
It is much faster than miniz_oxide and all other safe-Rust implementations, and consistently beats even Zlib. The performance is roughly on par with zlib-ng - sometimes faster, sometimes slower. It is not (yet) as fast as the original libdeflate in C.
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Zlib Critical Vulnerability
Zlib-ng doesn't contain the same code, but it appears that their equivalent inflate() when used with their inflateGetHeader() implementation was affected by a similar problem: https://github.com/zlib-ng/zlib-ng/pull/1328
Also similarly, most client code will be unaffected because `state->head` will be NULL, because they (most client code) won't have used inflateGetHeader() at all.
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Git’s database internals II: commit history queries
I wonder if zlib-ng would make a difference, since it has a lot of optimizations for modern hardware.
https://github.com/zlib-ng/zlib-ng/discussions/871
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Computing Adler32 Checksums at 41 GB/s
zlib-ng also has adler32 implementations optimized for various architectures: https://github.com/zlib-ng/zlib-ng
Might be interesting to benchmark their implementation too to see how it compares.
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Convenient CPU feature detection and dispatch in the Magnum Engine
zlib-ng: https://github.com/zlib-ng/zlib-ng/blob/develop/functable.c
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games-emulation/dolphin-9999 is failing to build because devs switched to minizip-ng and zlib uses minizip. I'm not sure how to get it to build now, details in post.
(2) There are many packages that rely upon zlib and minizip and switching those underlying dependencies is easier said than done. We can't drop zlib completely and switch: "The idea of zlib-ng is not to replace zlib, but to co-exist as a drop-in replacement with a lower threshold for code change." - https://github.com/zlib-ng/zlib-ng
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Re: Zlib memory corruption on deflate (i.e. compress)
There are already active zlib forks (e.g. https://github.com/zlib-ng/zlib-ng), the problem is with having people move to them. It takes a lot of effort to move mindshare from the original version to a fork, there's some historical examples of it happening, but not a ton.
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
zstd - Zstandard - Fast real-time compression algorithm
SymPy - A computer algebra system written in pure Python
ZLib - A massively spiffy yet delicately unobtrusive compression library.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Minizip-ng - Fork of the popular zip manipulation library found in the zlib distribution.
blaze - NumPy and Pandas interface to Big Data
libdeflate - Heavily optimized library for DEFLATE/zlib/gzip compression and decompression
SciPy - SciPy library main repository
brotli - Brotli compression format
Numba - NumPy aware dynamic Python compiler using LLVM
uzlib - Radically unbloated DEFLATE/zlib/gzip compression/decompression library. Can decompress any gzip/zlib data, and offers simplified compressor which produces gzip-compatible output, while requiring much less resources (and providing less compression ratio of course).
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).