LZ4 VS zstd

According to these https://github.com/lz4/lz4 values you need around ten (10) quite modern cores in parallel to accomplish around 8GB/s.

> 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.

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

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!

If anyone just cares for speed instead of compression I’d recommend lz4 [1]. I only recently started using it. Its speed is almost comparable to memcpy.

[1] https://github.com/lz4/lz4

E: Realtime compression (A good compression library like Zstandard can make a game less than half the size while taking a tiny amount of CPU power when loading stuff. I think thats a pretty worthwhile trade.) (ZSTD github) (LZ4 github)

lz4 for maximum decompression speed, for data that is often read but rarely written

Getting lost in material models is tempting. But, at this point you are overdue for working on your own asset pipeline. glTF is great. But, you should learn how to do it yourself. The hardest part will be reading source asset files. The FBX SDK is painful. Assimp isn't great either. Writing your own exporter to your own intermediate text format from Maya or Blender would be good if you are up for it. From whatever source, make your own archive format and binary formats for meshes, animations, textures and scenes. Use https://github.com/lz4/lz4 for compression. You should be able to decompress a list of assets into a big linear array and use them right there with just a bit of pointer fix-up. Minimize the amount of memory you have to touch from start to finish. Data that is going to the GPU (textures, vertex/index buffers) should decompress straight into mapped buffers for fast uploads.

I'm not a fan of the stacked bar charts, I like the table of data for "Benchmarks" on the github source page: https://github.com/lz4/lz4

It makes it very clear where LZ4 fits into comparisons with compression speed, decompression speed and compression ratio

If you want this functionality in zstd itself, check this out: https://github.com/facebook/zstd/pull/2349

Interesting to match with the observations from the practice of using ClickHouse[1][2] for time series:

1. Reordering to SOA helps a lot - this is the whole point of column-oriented databases.

2. Specialized codecs like Gorilla[3], DoubleDelta[4], and FPC[5] lose to simply using ZSTD[6] compression in most cases, both in compression ratio and in performance.

3. Specialized time-series DBMS like InfluxDB or TimescaleDB lose to general-purpose relational OLAP DBMS like ClickHouse [7][8][9].

[1] https://clickhouse.com/blog/optimize-clickhouse-codecs-compr...

[2] https://github.com/ClickHouse/ClickHouse

[3] https://clickhouse.com/docs/en/sql-reference/statements/crea...

[4] https://clickhouse.com/docs/en/sql-reference/statements/crea...

[5] https://clickhouse.com/docs/en/sql-reference/statements/crea...

[6] https://github.com/facebook/zstd/

[7] https://arxiv.org/pdf/2204.09795.pdf "SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things" (2022)

[8] https://gitlab.com/gitlab-org/incubation-engineering/apm/apm... https://gitlab.com/gitlab-org/incubation-engineering/apm/apm...

[9] https://www.sciencedirect.com/science/article/pii/S187705091...

zstd has a long range mode, which lets it find redundancies a gigabyte away. Try --long and --long=31 for very long range mode.

zstd has delta / patch mode, which creates a file that stores the "patch" to create a new file from an old (reference) file. See https://github.com/facebook/zstd/wiki/Zstandard-as-a-patchin...

See the man page: https://github.com/facebook/zstd/blob/dev/programs/zstd.1.md

So I downloaded the Facebook tool https://github.com/facebook/zstd

2.97.

https://github.com/facebook/zstd/issues/1087#issuecomment-44...

They have a nice table on that page: https://github.com/facebook/zstd#benchmarks

Looking at that table, I think LZ4 is a winner. The compression ratio is not too far, compression speed is slightly faster, decompression speed is significantly faster, the code is much simpler so the compiled binary is smaller, and the project is unrelated to Facebook.

In some cases, zstd can offer up to a 30% reduction in compressed storage as compared to other compression mechanisms. Learn more about zstd here.