Lz4 multithreaded

Lz4 multithreaded

This is especially true when comparing the ever-increasing CPU speed to the more-or-less stagnant mechanical disk performance SSDs are another matter, of course.

While compression algorithms and programs varies, basically we can distinguish to main categories: generic lossless compressors and specialized, lossy compressors. If the last categories include compressors with quite spectacular compression factor, they can typically be used only when you want to preserve the general information as a whole, and you are not interested in a true bit-wise precise representation of the original data.

So, for the general use case, lossless compressors are the way to go. But what compressor to use from the many available? Sometime different programs use the same underlying algorithm or even the same library implementation, so using one or another is a relatively low-important choice. However, when comparing compressors using different compression algorithms, the choice must be a weighted one: you want to privilege high compression ratio or speed? In other word, you need a fast and low-compression algorithm or a slow but more effective one?

In this article, we are going to examine many different compressors based on few different compressing libraries:. A lossless compression algorithms is a mathematical algorithms that define how to reduce compress a specific dataset in a smaller one, without losing information. In other word, it involves encoding information using fewer bit that the original version, with no information loss.

To be useful, a compression algorithms must be reversible — it should enable us to re-expand the compressed dataset, obtaining an exact copy of the original source. The next step is the algorithm implementation — in short, the real code used to express the mathematical behavior of the compression alg. This is another critical step: for example, vectorized or multithreaded code is way faster than plain, single-threaded code.

When a code implementation is considered good enough, often it is packetized in a standalone manner, creating a compression library. The advantage to spin-off the alg implementation in a standalone library is that you can write many different compressing programs without reimplement the basic alg multiple times.

Finally, we have the compression program itself. Sometime the alg, library and program have the same name eg: zip. While this is slightly confusing, what written above still apply. To summarize, our benchmarks will cover the alg, libraries and programs illustrated below:.

When possible, I tried to separate single-threaded results from multi-threaded ones. However, it appear that 7-zip has no thread-selection options, and by default it spawn as many threads as the hardware threads the CPU provide.

I experimented with these flags also, where applicable.LZ4 is one of the fastest compressors around, and like all LZtype compressors, decompression is even faster. From version 0. To decompress the generated compressed file again you can do:. A nice feature of data. That means that we can easily feed our raw vector to fread :. This effectively reads your data.

This is accomplished by dividing the data into at maximum 48 blocks, which are then processed in parallel. This increases the compression and decompression speeds significantly at a small cost to compression ratio:.

With more cores, you can do more parallel compression work. When we do the compression and decompression measurements above for a range of thread and compression level settings, we find the following dependency between speed and parallelism:.

Figure 1: Compression and decompression speeds vs the number of cores used for computation. The code that was used to obtain these results is given in the last paragraph. As can be expected, the compression speed is highest for lower compression level settings. But interesting enough, decompression speeds actually increase with higher compression settings! This relation is depicted below. Figure 2: Compression ratio for different settings of the compression level. The highlighted point at a 20 percent ZSTD compression level corresponds to the measurement that we did earlier.

There are many use cases where you compress your data only once but decompress it much more often. For example, you can compress and store a file that will need to be read many times in the future.

It will give you higher decompression speeds during reads and the compressed data will occupy less space. Also, when operating from a disk that has a lower speed than the de- compression algorithm, compression can really help. For those cases, compression will actually increase the total transfer speed because much less data has to be moved to or from the disk. This is also the main reason why fst is able to serialize a data set at higher speeds than the physical limits of a drive.

Please take a look at this post to get an idea of how that works exactly. Below is the benchmark script that was used to obtain the dependency graph for the number of cores and compression level. Version 0. The package is now multi-threaded allowing for even faster serialization of data frames to disk.

lz4 multithreaded

The fst package uses the xxHash algorithm for internal hashing of meta- data. Mark Klik Data scientist and creator of tools and algorithms for analysis of large data sets.

lz4 multithreaded

That means that we can easily feed our raw vector to fread : library data. Bring on the cores With more cores, you can do more parallel compression work. When we do the compression and decompression measurements above for a range of thread and compression level settings, we find the following dependency between speed and parallelism: Figure 1: Compression and decompression speeds vs the number of cores used for computation The code that was used to obtain these results is given in the last paragraph.

Francesc Alted - Blosc: Sending Data From Memory to CPU (and back) Faster Than memcpy()

The case for high compression levels There are many use cases where you compress your data only once but decompress it much more often. Please take a look at this post to get an idea of how that works exactly Benchmark code Below is the benchmark script that was used to obtain the dependency graph for the number of cores and compression level. This post is also available on R-bloggers. Share on Facebook Share on Twitter. Data scientist and creator of tools and algorithms for analysis of large data sets.Sorry I have not.

It worked so well that I symlinked bzip2 to lbzip2 on all my systems. I made a couple quick notes from that search. Brotli 9 is it throughput sweet spot and brotli 11 is it on disc size sweet spot. Basically you have just done Truth Lies and Statistics. Again this is attempt max out disc compression at the cost of performance. Most cases with compression you are after Throughput with open source compression you don't set them on max.

The insane part is the performance difference between brotli 9 and brotli 10 is massive. Yes it quite a steep lost for quite minor compression gains. Thanks for your feedback oiaohm, although it sounds overly negative and confrontational. I may just do this. I highly doubt any of my conclusions will fundamentally change. All practical open source codecs I'm aware of are now behind the state of the art. At the end of the day, I need to choose a set of settings which are reasonably representative of each codec, otherwise the graphs just get insane.

At the end of the day, users are going to select a single setting usually they just go "max compression" - assuming that setting doesn't take insanely long to compress and run with it. Perhaps these open source codecs should explicitly offer a setting that intelligently balances compression against decompression throughput.

It would be the slowest compressing codec out of all the ones I benchmark much slower than lzham, which is already too slowgreatly increasing how long it takes to complete a benchmark run. I've seen that pdf. The delta between Brotli and Kraken is extremely large, even speeding its decompressor up by 2X! That's the point. I wrote this codec, and all other settings purposely cripple the compressor and aren't intended to explicitly speed up decompression.

If using a lower settings does speed up decompression it's a side effect - it's not explicitly part of lzham's design. Users don't just choose max compression. Users who would be willing to buy a compression product would first generate a pareto frontier using their test data.

Then they might discover their data contains a lot of natural language text and choose a PPM compressor instead of a general purpose oriented compressor. For my data I discovered that if zstd is available, LZ4 is only useful at its fastest setting.

But you're right there are a few black sheep and you've picked several of them for your comparison. Brotli brotli comes with no settings at all, nor even hints as to acceptable values for the 'quality' flag, lol. On a scale of Zopfli zopfli 15 of LZ4 liblz4-tool comes configured for "fastest" compression because speed is the market they are trying to appeal to.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. The base types integerlogicalcharacterfactordouble should be supported supplemented with raw vector compression.

That way we can do:.

LZ4 Compression enabled by default

Hi rdpengfollowing your earlier tweetif you would like to experiment some with the LZ4 and ZSTD compressors, they can now be easily accessed from the dev version of the fst package with methods fstcompress and fstdecompress which have just been added. These methods provide in-memory compression for raw vectors using a block format to enable multi-threaded compression and decompression. To get a feeling of the de- compression speeds, I performed a series of benchmarks on a medium performance laptop i7 HQ 2.

For systems using higher clock speeds and more cores, these results should scale perhaps up to memory bus speeds more benchmarks will follow. In contrast, fstwrite can use column type information to optimize compression for example byte are reordered before compression for integer vectors. No such type information is available for raw vectors can be anything.

Additionally, hashes can be calculated on the compressed data blocks which can be checked during decompression. With hashes you can ensure the validity of the data. For example, if a compressed vector is tampered with, decompression with the ZSTD algorithm might lead to unpredictable results. With hashes, tampering can be detected before decompression is started.

The algorithm uses the xxHash algorithm from the ZSTD library for this functionality, which is very fast and separately available with method fst::fsthash see also Perhaps there will also be a use case for compressing without the block format, so single-core compression so that users can write there own block format.

Anyway, thanks for your interest in the fst package and if you have additional ideas or request, please let me know! Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. New issue. Jump to bottom. Labels feature request. Milestone Fst package v0. Copy link Quote reply.The compression algorithm you will use depends on how fast your processor is, how much disk space you have, and how big the archive can be. All these compression algorithms are implemented in libraries that fsarchiver is using. It means you need these libraries to be installed on your computer to compile fsarchiver with the support for these compression algorithms.

Also zstd is quite recent but very popular and well supported in recent Linux distributions, but it may not have an official package in older distributions. Unlike many compression programs that can use only one cpu core, fsarchiver can use all the power of your system.

It means that it can compress about four times faster on a computer with a quad-core processor for instance. By default, fsarchiver just creates one compression threads, so it just uses one processor core. If you have a processor with multiple cores, you can combine the multi-threading compression with a very high compression level.

That way you will have a very good compression ratio and it should not take too much time to compress. There are two options which you can use to choose the compression level you want to use. These 10 levels correspond to five compression algorithms lz4, lzo, gzip, bzip2, xz. Hence it is recommended to switch to this new compression option.

FSArchiver provides ten legacy compression levels.

lz4 multithreaded

You can choose the compression level to use when you create an archive by doing a savefs or savedir. You just have to use option -z X where X is the level to use.

When you use a low number, the compression will be very quick and less efficient. The memory requirement for compressing and decompressing will be small. The higher the compression level is, the better the compression will be and the smaller the archive will be. But good compression levels will require a lot of time, and also the memory requirement can be very big. FSArchiver 0. It is almost as fast as lz4 with level 1 and it its ratio is almost as good as xz with level Compression methods in the medium of the range gzip and bzip2 have become irrelevant as zstd can provide results which are both better and faster.

You can choose the compression level when you create an archive by doing a savefs or savedir. You just have to use option -Z X where X is the level to use. The xz decompression is faster than bzip2, and the zstd decompression is even faster than xz for similar ratios.

Hence it is recommended to use zstd with a high compression level if you want to minimize the size of archive files. The ratio and compression speed is similar to xz, and the zstd decompression will be much faster than xz.

You must be aware that high xz and zstd compression levels require a lot of memory especially at compression time. These compression levels are recommended on recent computers having multiple cpu cores and large amounts of memory. If the compression fails because lack of memory, the uncompressed version of the data will be written to the archive and an error message will be printed in the terminal the archive will still be valid as long as fsarchiver continues to run.

lz4 multithreaded

If you use multi-threading, there will be several compression-threads running in the same time, each one using some memory. Multi-threading compression will be faster on multi-core processors or systems with more than one cpu in general, but the compression ratio is the same. In our tests, the same fsarchiver savefs command with two threads and compression level -z9 is using MiB of memory instead of MiB when it has only one compression thread. This is because each compression thread requires a large amount of memory when the highest compression level is used -z9.

Usually memory will not be an issue with any recent desktop or server machine if you use compression levels inferior to -z9. The biggest part of the memory requirement is the compression threads.

The more compression threads you have, the more memory you need.Two years ago, Facebook open-sourced Zstandard v1. While some of the improvements, such as faster decompression speed and stronger compression ratios, are included with each upgrade, others need to be explicitly requested.

The initial promise of Zstandard zstd was that it would allow users to replace their existing data compression implementation e. Once it delivered on that promise, we quickly began to replace zlib with zstd. This was no small undertaking, and there is a long tail of projects to get to. However, through creative refactoring of accretion points, notably follywe have converted a significant portion in record time as reported by our internal monitoring system.

Each service chooses to optimize for gains on one of these axes, depending on its priorities. In addition to replacing zlib, zstd has taken over many of the jobs that traditionally relied on fast compression alternatives. With all these use cases combined, zstd now processes a significant amount of data every day at Facebook. Replacing existing codecs is a good place to start, but zstd can do so much more. In this post, we walk through the benefits we found and some of the lessons we learned as we implemented these advanced features.

Backups can be hundreds of gigabytes in size, and unlike most compression use cases, the data is almost never decompressed. That is, backups are write-once and read-never. The priorities for this case are compression efficiency and compressed size — and we can allow increased resources for decompression. With such large files, fbpkg prioritizes compression efficiency and speed. Multithreaded compression can nearly linearly speed up compression per core, with almost no loss of ratio.

Development server backups use two cores to match the speed of the rest of the pipeline -T2and fbpkg uses all the available cores -T0. The next feature tailored to large data is long range mode --long. Long range mode works in tandem with the regular zstd compression levels.

Long range mode is a serial preprocessor which finds large matches and passes the rest to a parallel zstd backend. Adding long range mode reduced the full backup size by 16 percent and the diff backup size by 27 percent.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Multi-threaded LZ4 and ZSTD compression from R

Ask Ubuntu is a question and answer site for Ubuntu users and developers. It only takes a minute to sign up. What compression tools are available in Ubuntu that can benefit from a multi-core CPU. There are two main tools. They're essentially different implementations of bzip2 compressors. I've compared them the output is a tidied up version but you should be able to run the commands. It's slightly less compressed but much quicker. Well, the keyword was parallel. After looking for all compression tools that were also parallel I found the following:.

PXZ - Parallel XZ is a compression utility that takes advantage of running LZMA compression of different parts of an input file on multiple cores and processors simultaneously. Its primary goal is to utilize all resources to speed up compression time with minimal possible influence on compression ratio. Lzip decompresses almost as fast as gzip and compresses better than bzip2, which makes it well suited for software distribution and data archiving.

Plzip is a massively parallel multi-threaded version of lzip using the lzip file format; the files produced by plzip are fully compatible with lzip. On files big enough, plzip can use hundreds of processors.

PIGZ - pigz, which stands for Parallel Implementation of GZip, is a fully functional replacement for gzip that takes advantage of multiple processors and multiple cores when compressing data. PBZIP2 - pbzip2 is a parallel implementation of the bzip2 block-sorting file compressor that uses pthreads and achieves near-linear speedup on SMP machines. The output of this version is fully compatible with bzip2 v1. LRZIP - A multithreaded compression program that can achieve very high compression ratios and speed when used with large files.

It uses the combined compression algorithms of zpaq and lzma for maximum compression, lzo for maximum speed, and the long range redundancy reduction of rzip.

It is designed to scale with increases with RAM size, improving compression further. A choice of either size or speed optimizations allows for either better compression than even lzma can provide, or better speed than gzip, but with bzip2 sized compression levels.

In other words, PIXZ is supposedly more memory and disk efficient, and has an optional indexing feature that speeds up decompression of individual components of compressed tar files. XZ Utils supports multi-threaded compression since v5.

It uses the very fast lempel-ziv-oberhumer compression algorithm which is times faster than gzip in my observation. Note: Although it's not multi-threaded yet, it will probably outperform pigz on core systems. That's why I decided to post this even if it doesn't directly answer your question. I found it often to be a better solution than, e. The LZMA2 compressor of p7zip uses both cores on my system.

Zstandard supports multi-threading since v1. You have to use artful or a newer release, or compile the latest version from source to get these benefits.

Luckily it doesn't pull in a lot of dependencies. It is not really an answer, but I think it is relevant enough to share my benchmarks comparing speed of gzip and pigz on a real HW in a real life scenario. As pigz is the multithreaded evolution I personally have chosen to use from now on.

As a bottomline I would not recommend the zopfli algorithm since the compression took tremendous amount of time for a not-that-significant amount of disk space spared.


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