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LZ4 | Snappy | |
---|---|---|
21 | 5 | |
9,192 | 5,977 | |
1.7% | 0.6% | |
9.5 | 2.6 | |
7 days ago | 11 days ago | |
C | C++ | |
GNU General Public License v3.0 or later | 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.
LZ4
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Number sizes for LZ77 compression
LZ4 is a bit more complicated, but seems faster: https://github.com/lz4/lz4/blob/dev/doc/lz4_Block_format.md
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Rsyncing 20TB locally
According to these https://github.com/lz4/lz4 values you need around ten (10) quite modern cores in parallel to accomplish around 8GB/s.
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An Intro to Data Compression
The popular NoSQL database Cassandra utilizes a compression algorithm called LZ4 to reduce the footprint of data at rest. LZ4 is characterized by very fast compression speed at the cost of a higher compression ratio. This is a design choice that allows Cassandra to maintain high write throughput while also benefiting from compression in some capacity.
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Micron Unveils 24GB and 48GB DDR5 Memory Modules | AMD EXPO and Intel XMP 3.0 compatible
Yeah, sure, when you have monster core counts. on regular systems, not so much, here's from their own github page. it achieves, eh, 5GB/s on memory to memory transfers, i.e. best case scenario. so, uh, no? i'm not even sure it's any better than the CPU decompressor one Nvidia used.
- Cerbios Xbox Bios V2.2.0 BETA Released (1.0 - 1.6)
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zstd
> The downside of lz4 is that it can’t be configured to run at higher & slower compression ratios.
lz4 has some level of configurability? https://github.com/lz4/lz4/blob/v1.9.4/lib/lz4frame.h#L194
There's also LZ4_HC.
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Best archival/compression format for whole hard drives
Since nobody mentioned it, I'll add lz4 (https://github.com/lz4/lz4).
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I'm new to this
Get your bootloader unlocked via Download mode and then obtain your stock firmware, preferably for your current region https://samfw.com (Download mode: CARRIER_CODE). Get the boot image from AP with 7zip, unpack from LZ4 with https://github.com/lz4/lz4/releases (drag and drop), patch with Magisk https://github.com/topjohnwu/magisk/releases/latest, grab the new image, name it "boot.img" and pack it into a .tar with 7zip and flash to AP with odin https://odindownload.com
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An efficient image format for SDL
After some investigations and experiments, I found out that it was the PNG compression (well, decompression I should say) that took a while. So I've made some experiments using the LZ4 compression library, which is focused on decompression speed, and it turned out to be an excellent solution!
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how to root Samsung galaxy note 10 plus 5g(SM-N976B
Root with magisk: whether you use OneUI ≤3 or 4, patch the specific image needed for it (pre 4: boot, after 4: recovery) and flash it to the device. Boot it and enjoy root. https://github.com/lz4/lz4/releases can help extracting it from the AP tarball.
Snappy
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Why I enjoy using the Nim programming language at Reddit.
Another example of Nim being really fast is the supersnappy library. This library benchmarks faster than Google’s C or C++ Snappy implementation.
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Stretch iPhone to Its Limit: 2GiB Stable Diffusion Model Runs Locally on Device
It doesn't destroy performance for the simple reason that nowadays memory access has higher latency than pure compute. If you need to use compute to produce some data to be stored in memory, your overall throughput could very well be faster than without compression.
There have been a large amount of innovation on fast compression in recent years. Traditional compression tools like gzip or xz are geared towards higher compression ratio, but memory compression tends to favor speed. Check out those algorithms:
* lz4: https://lz4.github.io/lz4/
* Google's snappy: https://github.com/google/snappy
* Facebook's zstd in fast mode: http://facebook.github.io/zstd/#benchmarks
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Compression with best ratio and fast decompression
Google released Snappy, which is extremely fast and robust (both at compression and decompression), but it's definitely not nearly as good (in terms of compression ratio). Google mostly uses it for real-time compression, for example of network messages - not for long-term storage.
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How to store item info?
Just compress it! Of course if you will you ZIP, players will able to just open this zip file and change whatever they want. But you can use less popular compression algorithms which are not supported by default Windows File Explorer. Snappy for example.
- What's the best way to compress strings?
What are some alternatives?
zstd - Zstandard - Fast real-time compression algorithm
brotli - Brotli compression format
LZMA - (Unofficial) Git mirror of LZMA SDK releases
ZLib - A massively spiffy yet delicately unobtrusive compression library.
7-Zip-zstd - 7-Zip with support for Brotli, Fast-LZMA2, Lizard, LZ4, LZ5 and Zstandard
zlib-ng - zlib replacement with optimizations for "next generation" systems.
LZFSE - LZFSE compression library and command line tool
tiny_jpeg.h - Single header lib for JPEG encoding. Public domain. C99. stb style.