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Contrasting the Performance of Compression Algorithms on Genomic Data
Contrasting the Performance of Compression Algorithms on Genomic Data Cornel Constantinescu, IBM Research Almaden Outline of the Talk: • Introduction / Motivation • Data used in experiments • General purpose compressors comparison • Simple Improvements • Special purpose compression • Transparent compression – working on compressed data (prototype) • Parallelism / Multithreading • Conclusion Introduction / Motivation • Despite the large number of research papers and compression algorithms proposed for compressing genomic data generated by sequencing machines, by far the most commonly used compression algorithm in the industry for FASTQ data is gzip. • The main drawbacks of the proposed alternative special-purpose compression algorithms are: • slow speed of either compression or decompression or both, and also their • brittleness by making various limiting assumptions about the input FASTQ format (for example, the structure of the headers or fixed lengths of the records [1]) in order to further improve their specialized compression. 1. Ibrahim Numanagic, James K Bonfield, Faraz Hach, Jan Voges, Jorn Ostermann, Claudio Alberti, Marco Mattavelli, and S Cenk Sahinalp. Comparison of high-throughput sequencing data compression tools. Nature Methods, 13(12):1005–1008, October 2016. Fast and Efficient Compression of Next Generation Sequencing Data 2 2 General Purpose Compression of Genomic Data As stated earlier, gzip/zlib compression is the method of choice by the industry for FASTQ genomic data. FASTQ genomic data is a text-based format (ASCII readable text) for storing a biological sequence and the corresponding quality scores. Each sequence letter and quality score is encoded with a single ASCII character. FASTQ data is structured in four fields per record (a “read”). The first field is the SEQUENCE ID or the header of the read. -
Pack, Encrypt, Authenticate Document Revision: 2021 05 02
PEA Pack, Encrypt, Authenticate Document revision: 2021 05 02 Author: Giorgio Tani Translation: Giorgio Tani This document refers to: PEA file format specification version 1 revision 3 (1.3); PEA file format specification version 2.0; PEA 1.01 executable implementation; Present documentation is released under GNU GFDL License. PEA executable implementation is released under GNU LGPL License; please note that all units provided by the Author are released under LGPL, while Wolfgang Ehrhardt’s crypto library units used in PEA are released under zlib/libpng License. PEA file format and PCOMPRESS specifications are hereby released under PUBLIC DOMAIN: the Author neither has, nor is aware of, any patents or pending patents relevant to this technology and do not intend to apply for any patents covering it. As far as the Author knows, PEA file format in all of it’s parts is free and unencumbered for all uses. Pea is on PeaZip project official site: https://peazip.github.io , https://peazip.org , and https://peazip.sourceforge.io For more information about the licenses: GNU GFDL License, see http://www.gnu.org/licenses/fdl.txt GNU LGPL License, see http://www.gnu.org/licenses/lgpl.txt 1 Content: Section 1: PEA file format ..3 Description ..3 PEA 1.3 file format details ..5 Differences between 1.3 and older revisions ..5 PEA 2.0 file format details ..7 PEA file format’s and implementation’s limitations ..8 PCOMPRESS compression scheme ..9 Algorithms used in PEA format ..9 PEA security model .10 Cryptanalysis of PEA format .12 Data recovery from -
Improved Neural Network Based General-Purpose Lossless Compression Mohit Goyal, Kedar Tatwawadi, Shubham Chandak, Idoia Ochoa
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 DZip: improved neural network based general-purpose lossless compression Mohit Goyal, Kedar Tatwawadi, Shubham Chandak, Idoia Ochoa Abstract—We consider lossless compression based on statistical [4], [5] and generative modeling [6]). Neural network based data modeling followed by prediction-based encoding, where an models can typically learn highly complex patterns in the data accurate statistical model for the input data leads to substantial much better than traditional finite context and Markov models, improvements in compression. We propose DZip, a general- purpose compressor for sequential data that exploits the well- leading to significantly lower prediction error (measured as known modeling capabilities of neural networks (NNs) for pre- log-loss or perplexity [4]). This has led to the development of diction, followed by arithmetic coding. DZip uses a novel hybrid several compressors using neural networks as predictors [7]– architecture based on adaptive and semi-adaptive training. Unlike [9], including the recently proposed LSTM-Compress [10], most NN based compressors, DZip does not require additional NNCP [11] and DecMac [12]. Most of the previous works, training data and is not restricted to specific data types. The proposed compressor outperforms general-purpose compressors however, have been tailored for compression of certain data such as Gzip (29% size reduction on average) and 7zip (12% size types (e.g., text [12] [13] or images [14], [15]), where the reduction on average) on a variety of real datasets, achieves near- prediction model is trained in a supervised framework on optimal compression on synthetic datasets, and performs close to separate training data or the model architecture is tuned for specialized compressors for large sequence lengths, without any the specific data type. -
Lossless Compression of Internal Files in Parallel Reservoir Simulation
Lossless Compression of Internal Files in Parallel Reservoir Simulation Suha Kayum Marcin Rogowski Florian Mannuss 9/26/2019 Outline • I/O Challenges in Reservoir Simulation • Evaluation of Compression Algorithms on Reservoir Simulation Data • Real-world application - Constraints - Algorithm - Results • Conclusions 2 Challenge Reservoir simulation 1 3 Reservoir Simulation • Largest field in the world are represented as 50 million – 1 billion grid block models • Each runs takes hours on 500-5000 cores • Calibrating the model requires 100s of runs and sophisticated methods • “History matched” model is only a beginning 4 Files in Reservoir Simulation • Internal Files • Input / Output Files - Interact with pre- & post-processing tools Date Restart/Checkpoint Files 5 Reservoir Simulation in Saudi Aramco • 100’000+ simulations annually • The largest simulation of 10 billion cells • Currently multiple machines in TOP500 • Petabytes of storage required 600x • Resources are Finite • File Compression is one solution 50x 6 Compression algorithm evaluation 2 7 Compression ratio Tested a number of algorithms on a GRID restart file for two models 4 - Model A – 77.3 million active grid blocks 3.5 - Model K – 8.7 million active grid blocks 3 - 15.6 GB and 7.2 GB respectively 2.5 2 Compression ratio is between 1.5 1 compression ratio compression - From 2.27 for snappy (Model A) 0.5 0 - Up to 3.5 for bzip2 -9 (Model K) Model A Model K lz4 snappy gzip -1 gzip -9 bzip2 -1 bzip2 -9 8 Compression speed • LZ4 and Snappy significantly outperformed other algorithms -
User Commands GZIP ( 1 ) Gzip, Gunzip, Gzcat – Compress Or Expand Files Gzip [ –Acdfhllnnrtvv19 ] [–S Suffix] [ Name ... ]
User Commands GZIP ( 1 ) NAME gzip, gunzip, gzcat – compress or expand files SYNOPSIS gzip [–acdfhlLnNrtvV19 ] [– S suffix] [ name ... ] gunzip [–acfhlLnNrtvV ] [– S suffix] [ name ... ] gzcat [–fhLV ] [ name ... ] DESCRIPTION Gzip reduces the size of the named files using Lempel-Ziv coding (LZ77). Whenever possible, each file is replaced by one with the extension .gz, while keeping the same ownership modes, access and modification times. (The default extension is – gz for VMS, z for MSDOS, OS/2 FAT, Windows NT FAT and Atari.) If no files are specified, or if a file name is "-", the standard input is compressed to the standard output. Gzip will only attempt to compress regular files. In particular, it will ignore symbolic links. If the compressed file name is too long for its file system, gzip truncates it. Gzip attempts to truncate only the parts of the file name longer than 3 characters. (A part is delimited by dots.) If the name con- sists of small parts only, the longest parts are truncated. For example, if file names are limited to 14 characters, gzip.msdos.exe is compressed to gzi.msd.exe.gz. Names are not truncated on systems which do not have a limit on file name length. By default, gzip keeps the original file name and timestamp in the compressed file. These are used when decompressing the file with the – N option. This is useful when the compressed file name was truncated or when the time stamp was not preserved after a file transfer. Compressed files can be restored to their original form using gzip -d or gunzip or gzcat. -
The Ark Handbook
The Ark Handbook Matt Johnston Henrique Pinto Ragnar Thomsen The Ark Handbook 2 Contents 1 Introduction 5 2 Using Ark 6 2.1 Opening Archives . .6 2.1.1 Archive Operations . .6 2.1.2 Archive Comments . .6 2.2 Working with Files . .7 2.2.1 Editing Files . .7 2.3 Extracting Files . .7 2.3.1 The Extract dialog . .8 2.4 Creating Archives and Adding Files . .8 2.4.1 Compression . .9 2.4.2 Password Protection . .9 2.4.3 Multi-volume Archive . 10 3 Using Ark in the Filemanager 11 4 Advanced Batch Mode 12 5 Credits and License 13 Abstract Ark is an archive manager by KDE. The Ark Handbook Chapter 1 Introduction Ark is a program for viewing, extracting, creating and modifying archives. Ark can handle vari- ous archive formats such as tar, gzip, bzip2, zip, rar, 7zip, xz, rpm, cab, deb, xar and AppImage (support for certain archive formats depends on the appropriate command-line programs being installed). In order to successfully use Ark, you need KDE Frameworks 5. The library libarchive version 3.1 or above is needed to handle most archive types, including tar, compressed tar, rpm, deb and cab archives. To handle other file formats, you need the appropriate command line programs, such as zipinfo, zip, unzip, rar, unrar, 7z, lsar, unar and lrzip. 5 The Ark Handbook Chapter 2 Using Ark 2.1 Opening Archives To open an archive in Ark, choose Open... (Ctrl+O) from the Archive menu. You can also open archive files by dragging and dropping from Dolphin. -
Bzip2 and Libbzip2, Version 1.0.8 a Program and Library for Data Compression
bzip2 and libbzip2, version 1.0.8 A program and library for data compression Julian Seward, https://sourceware.org/bzip2/ bzip2 and libbzip2, version 1.0.8: A program and library for data compression by Julian Seward Version 1.0.8 of 13 July 2019 Copyright© 1996-2019 Julian Seward This program, bzip2, the associated library libbzip2, and all documentation, are copyright © 1996-2019 Julian Seward. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: • Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. • The origin of this software must not be misrepresented; you must not claim that you wrote the original software. If you use this software in a product, an acknowledgment in the product documentation would be appreciated but is not required. • Altered source versions must be plainly marked as such, and must not be misrepresented as being the original software. • The name of the author may not be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DIS- CLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -
Working with Compressed Archives
Working with compressed archives Archiving a collection of files or folders means creating a single file that groups together those files and directories. Archiving does not manipulate the size of files. They are added to the archive as they are. Compressing a file means shrinking the size of the file. There are software for archiving only, other for compressing only and (as you would expect) other that have the ability to archive and compress. This document will show you how to use a windows application called 7-zip (seven zip) to work with compressed archives. 7-zip integrates in the context menu that pops-up up whenever you right-click your mouse on a selection1. In this how-to we will look in the application interface and how we manipulate archives, their compression and contents. 1. Click the 'start' button and type '7-zip' 2. When the search brings up '7-zip File Manager' (as the best match) press 'Enter' 3. The application will startup and you will be presented with the 7-zip manager interface 4. The program integrates both archiving, de/compression and file-management operations. a. The main area of the window provides a list of the files of the active directory. When not viewing the contents of an archive, the application acts like a file browser window. You can open folders to see their contents by just double-clicking on them b. Above the main area you have an address-like bar showing the name of the active directory (currently c:\ADG.Becom). You can use the 'back' icon on the far left to navigate back from where you are and go one directory up. -
Arrow: Integration to 'Apache' 'Arrow'
Package ‘arrow’ September 5, 2021 Title Integration to 'Apache' 'Arrow' Version 5.0.0.2 Description 'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. This package provides an interface to the 'Arrow C++' library. Depends R (>= 3.3) License Apache License (>= 2.0) URL https://github.com/apache/arrow/, https://arrow.apache.org/docs/r/ BugReports https://issues.apache.org/jira/projects/ARROW/issues Encoding UTF-8 Language en-US SystemRequirements C++11; for AWS S3 support on Linux, libcurl and openssl (optional) Biarch true Imports assertthat, bit64 (>= 0.9-7), methods, purrr, R6, rlang, stats, tidyselect, utils, vctrs RoxygenNote 7.1.1.9001 VignetteBuilder knitr Suggests decor, distro, dplyr, hms, knitr, lubridate, pkgload, reticulate, rmarkdown, stringi, stringr, testthat, tibble, withr Collate 'arrowExports.R' 'enums.R' 'arrow-package.R' 'type.R' 'array-data.R' 'arrow-datum.R' 'array.R' 'arrow-tabular.R' 'buffer.R' 'chunked-array.R' 'io.R' 'compression.R' 'scalar.R' 'compute.R' 'config.R' 'csv.R' 'dataset.R' 'dataset-factory.R' 'dataset-format.R' 'dataset-partition.R' 'dataset-scan.R' 'dataset-write.R' 'deprecated.R' 'dictionary.R' 'dplyr-arrange.R' 'dplyr-collect.R' 'dplyr-eval.R' 'dplyr-filter.R' 'expression.R' 'dplyr-functions.R' 1 2 R topics documented: 'dplyr-group-by.R' 'dplyr-mutate.R' 'dplyr-select.R' 'dplyr-summarize.R' -
Gzip, Bzip2 and Tar EXPERT PACKING
LINUXUSER Command Line: gzip, bzip2, tar gzip, bzip2 and tar EXPERT PACKING A short command is all it takes to pack your data or extract it from an archive. BY HEIKE JURZIK rchiving provides many bene- fits: packed and compressed Afiles occupy less space on your disk and require less bandwidth on the Internet. Linux has both GUI-based pro- grams, such as File Roller or Ark, and www.sxc.hu command-line tools for creating and un- packing various archive types. This arti- cle examines some shell tools for ar- chiving files and demonstrates the kind of expert packing that clever combina- tained by the packing process. If you A gzip file can be unpacked using either tions of Linux commands offer the com- prefer to use a different extension, you gunzip or gzip -d. If the tool discovers a mand line user. can set the -S (suffix) flag to specify your file of the same name in the working di- own instead. For example, the command rectory, it prompts you to make sure that Nicely Packed with “gzip” you know you are overwriting this file: The gzip (GNU Zip) program is the de- gzip -S .z image.bmp fault packer on Linux. Gzip compresses $ gunzip screenie.jpg.gz simple files, but it does not create com- creates a compressed file titled image. gunzip: screenie.jpg U plete directory archives. In its simplest bmp.z. form, the gzip command looks like this: The size of the compressed file de- Listing 1: Compression pends on the distribution of identical Compared gzip file strings in the original file. -
Deduplicating Compressed Contents in Cloud Storage Environment
Deduplicating Compressed Contents in Cloud Storage Environment Zhichao Yan, Hong Jiang Yujuan Tan* Hao Luo University of Texas Arlington Chongqing University University of Nebraska Lincoln [email protected] [email protected] [email protected] [email protected] Corresponding Author Abstract Data compression and deduplication are two common approaches to increasing storage efficiency in the cloud environment. Both users and cloud service providers have economic incentives to compress their data before storing it in the cloud. However, our analysis indicates that compressed packages of different data and differ- ently compressed packages of the same data are usual- ly fundamentally different from one another even when they share a large amount of redundant data. Existing data deduplication systems cannot detect redundant data among them. We propose the X-Ray Dedup approach to extract from these packages the unique metadata, such as the “checksum” and “file length” information, and use it as the compressed file’s content signature to help detect and remove file level data redundancy. X-Ray Dedup is shown by our evaluations to be capable of breaking in the boundaries of compressed packages and significantly Figure 1: A user scenario on cloud storage environment reducing compressed packages’ size requirements, thus further optimizing storage space in the cloud. will generate different compressed data of the same con- tents that render fingerprint-based redundancy identifi- cation difficult. Third, very similar but different digital 1 Introduction contents (e.g., files or data streams), which would other- wise present excellent deduplication opportunities, will Due to the information explosion [1, 3], data reduc- become fundamentally distinct compressed packages af- tion technologies such as compression and deduplica- ter applying even the same compression algorithm. -
Parquet Data Format Performance
Parquet data format performance Jim Pivarski Princeton University { DIANA-HEP February 21, 2018 1 / 22 What is Parquet? 1974 HBOOK tabular rowwise FORTRAN first ntuples in HEP 1983 ZEBRA hierarchical rowwise FORTRAN event records in HEP 1989 PAW CWN tabular columnar FORTRAN faster ntuples in HEP 1995 ROOT hierarchical columnar C++ object persistence in HEP 2001 ProtoBuf hierarchical rowwise many Google's RPC protocol 2002 MonetDB tabular columnar database “first” columnar database 2005 C-Store tabular columnar database also early, became HP's Vertica 2007 Thrift hierarchical rowwise many Facebook's RPC protocol 2009 Avro hierarchical rowwise many Hadoop's object permanance and interchange format 2010 Dremel hierarchical columnar C++, Java Google's nested-object database (closed source), became BigQuery 2013 Parquet hierarchical columnar many open source object persistence, based on Google's Dremel paper 2016 Arrow hierarchical columnar many shared-memory object exchange 2 / 22 What is Parquet? 1974 HBOOK tabular rowwise FORTRAN first ntuples in HEP 1983 ZEBRA hierarchical rowwise FORTRAN event records in HEP 1989 PAW CWN tabular columnar FORTRAN faster ntuples in HEP 1995 ROOT hierarchical columnar C++ object persistence in HEP 2001 ProtoBuf hierarchical rowwise many Google's RPC protocol 2002 MonetDB tabular columnar database “first” columnar database 2005 C-Store tabular columnar database also early, became HP's Vertica 2007 Thrift hierarchical rowwise many Facebook's RPC protocol 2009 Avro hierarchical rowwise many Hadoop's object permanance and interchange format 2010 Dremel hierarchical columnar C++, Java Google's nested-object database (closed source), became BigQuery 2013 Parquet hierarchical columnar many open source object persistence, based on Google's Dremel paper 2016 Arrow hierarchical columnar many shared-memory object exchange 2 / 22 Developed independently to do the same thing Google Dremel authors claimed to be unaware of any precedents, so this is an example of convergent evolution.