Dictionary Based Compression Type Classification Using a CNN
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Schematic Entry
Schematic Entry Copyrights Software, documentation and related materials: Copyright © 2002 Altium Limited This software product is copyrighted and all rights are reserved. The distribution and sale of this product are intended for the use of the original purchaser only per the terms of the License Agreement. This document may not, in whole or part, be copied, photocopied, reproduced, translated, reduced or transferred to any electronic medium or machine-readable form without prior consent in writing from Altium Limited. U.S. Government use, duplication or disclosure is subject to RESTRICTED RIGHTS under applicable government regulations pertaining to trade secret, commercial computer software developed at private expense, including FAR 227-14 subparagraph (g)(3)(i), Alternative III and DFAR 252.227-7013 subparagraph (c)(1)(ii). P-CAD is a registered trademark and P-CAD Schematic, P-CAD Relay, P-CAD PCB, P-CAD ProRoute, P-CAD QuickRoute, P-CAD InterRoute, P-CAD InterRoute Gold, P-CAD Library Manager, P-CAD Library Executive, P-CAD Document Toolbox, P-CAD InterPlace, P-CAD Parametric Constraint Solver, P-CAD Signal Integrity, P-CAD Shape-Based Autorouter, P-CAD DesignFlow, P-CAD ViewCenter, Master Designer and Associate Designer are trademarks of Altium Limited. Other brand names are trademarks of their respective companies. Altium Limited www.altium.com Table of Contents chapter 1 Introducing P-CAD Schematic P-CAD Schematic Features ................................................................................................1 About -
ROOT I/O Compression Improvements for HEP Analysis
EPJ Web of Conferences 245, 02017 (2020) https://doi.org/10.1051/epjconf/202024502017 CHEP 2019 ROOT I/O compression improvements for HEP analysis Oksana Shadura1;∗ Brian Paul Bockelman2;∗∗ Philippe Canal3;∗∗∗ Danilo Piparo4;∗∗∗∗ and Zhe Zhang1;y 1University of Nebraska-Lincoln, 1400 R St, Lincoln, NE 68588, United States 2Morgridge Institute for Research, 330 N Orchard St, Madison, WI 53715, United States 3Fermilab, Kirk Road and Pine St, Batavia, IL 60510, United States 4CERN, Meyrin 1211, Geneve, Switzerland Abstract. We overview recent changes in the ROOT I/O system, enhancing it by improving its performance and interaction with other data analysis ecosys- tems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly improve experiment’s software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically in- creased over the last couple of years. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly, because there are significant trade-offs between the increased CPU cost for reading and writing files and the reduces storage space. 1 Introduction In the past years, Large Hadron Collider (LHC) experiments are managing about an exabyte of storage for analysis purposes, approximately half of which is stored on tape storages for archival purposes, and half is used for traditional disk storage. Meanwhile for High Lumi- nosity Large Hadron Collider (HL-LHC) storage requirements per year are expected to be increased by a factor of 10 [1]. -
Melinda's Marks Merit Main Mantle SYDNEY STRIDERS
SYDNEY STRIDERS ROAD RUNNERS’ CLUB AUSTRALIA EDITION No 108 MAY - AUGUST 2009 Melinda’s marks merit main mantle This is proving a “best-so- she attained through far” year for Melinda. To swimming conflicted with date she has the fastest her transition to running. time in Australia over 3000m. With a smart 2nd Like all top runners she at the State Open 5000m does well over 100k a champs, followed by a week in training, win at the State Open 10k consisting of a variety of Road Champs, another sessions: steady pace, win at the Herald Half medium pace, long slow which doubles as the runs, track work, fartlek, State Half Champs and a hills, gym work and win at the State Cross swimming! country Champs, our Melinda is looking like Springs under her shoes give hot property. Melinda extra lift Melinda began her sports Continued Page 3 career as a swimmer. By 9 years of age she was representing her club at State level. She held numerous records for INSIDE BLISTER 108 Breaststroke and Lisa facing racing pacing Butterfly. Her switch to running came after the McKinney makes most of death of her favourite marvellous mud moment Coach and because she Weather woe means Mo wasn’t growing as big as can’t crow though not slow! her fellow competitors. She managed some pretty fast times at inter-schools Brent takes tumble at Trevi champs and Cross Country before making an impression in the Open category where she has Champion Charles cheered steadily improved. by chance & chase challenge N’Lotsa Uthastuff Melinda credits her swimming background for endurance -
Arxiv:2004.10531V1 [Cs.OH] 8 Apr 2020
ROOT I/O compression improvements for HEP analysis Oksana Shadura1;∗ Brian Paul Bockelman2;∗∗ Philippe Canal3;∗∗∗ Danilo Piparo4;∗∗∗∗ and Zhe Zhang1;y 1University of Nebraska-Lincoln, 1400 R St, Lincoln, NE 68588, United States 2Morgridge Institute for Research, 330 N Orchard St, Madison, WI 53715, United States 3Fermilab, Kirk Road and Pine St, Batavia, IL 60510, United States 4CERN, Meyrin 1211, Geneve, Switzerland Abstract. We overview recent changes in the ROOT I/O system, increasing per- formance and enhancing it and improving its interaction with other data analy- sis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiment’s software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically in- creased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are sig- nificant trade-offs between the increased CPU cost for reading and writing files and the reduce storage space. 1 Introduction In the past years LHC experiments are commissioned and now manages about an exabyte of storage for analysis purposes, approximately half of which is used for archival purposes, and half is used for traditional disk storage. Meanwhile for HL-LHC storage requirements per year are expected to be increased by factor 10 [1]. arXiv:2004.10531v1 [cs.OH] 8 Apr 2020 Looking at these predictions, we would like to state that storage will remain one of the major cost drivers and at the same time the bottlenecks for HEP computing. -
Word-Based Text Compression
WORD-BASED TEXT COMPRESSION Jan Platoš, Jiří Dvorský Department of Computer Science VŠB – Technical University of Ostrava, Czech Republic {jan.platos.fei, jiri.dvorsky}@vsb.cz ABSTRACT Today there are many universal compression algorithms, but in most cases is for specific data better using specific algorithm - JPEG for images, MPEG for movies, etc. For textual documents there are special methods based on PPM algorithm or methods with non-character access, e.g. word-based compression. In the past, several papers describing variants of word- based compression using Huffman encoding or LZW method were published. The subject of this paper is the description of a word-based compression variant based on the LZ77 algorithm. The LZ77 algorithm and its modifications are described in this paper. Moreover, various ways of sliding window implementation and various possibilities of output encoding are described, as well. This paper also includes the implementation of an experimental application, testing of its efficiency and finding the best combination of all parts of the LZ77 coder. This is done to achieve the best compression ratio. In conclusion there is comparison of this implemented application with other word-based compression programs and with other commonly used compression programs. Key Words: LZ77, word-based compression, text compression 1. Introduction Data compression is used more and more the text compression. In the past, word- in these days, because larger amount of based compression methods based on data require to be transferred or backed-up Huffman encoding, LZW or BWT were and capacity of media or speed of network tested. This paper describes word-based lines increase slowly. -
The Basic Principles of Data Compression
The Basic Principles of Data Compression Author: Conrad Chung, 2BrightSparks Introduction Internet users who download or upload files from/to the web, or use email to send or receive attachments will most likely have encountered files in compressed format. In this topic we will cover how compression works, the advantages and disadvantages of compression, as well as types of compression. What is Compression? Compression is the process of encoding data more efficiently to achieve a reduction in file size. One type of compression available is referred to as lossless compression. This means the compressed file will be restored exactly to its original state with no loss of data during the decompression process. This is essential to data compression as the file would be corrupted and unusable should data be lost. Another compression category which will not be covered in this article is “lossy” compression often used in multimedia files for music and images and where data is discarded. Lossless compression algorithms use statistic modeling techniques to reduce repetitive information in a file. Some of the methods may include removal of spacing characters, representing a string of repeated characters with a single character or replacing recurring characters with smaller bit sequences. Advantages/Disadvantages of Compression Compression of files offer many advantages. When compressed, the quantity of bits used to store the information is reduced. Files that are smaller in size will result in shorter transmission times when they are transferred on the Internet. Compressed files also take up less storage space. File compression can zip up several small files into a single file for more convenient email transmission. -
Data Compression for Remote Laboratories
Paper—Data Compression for Remote Laboratories Data Compression for Remote Laboratories https://doi.org/10.3991/ijim.v11i4.6743 Olawale B. Akinwale Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Lawrence O. Kehinde Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Abstract—Remote laboratories on mobile phones have been around for a few years now. This has greatly improved accessibility of these remote labs to students who cannot afford computers but have mobile phones. When money is a factor however (as is often the case with those who can’t afford a computer), the cost of use of these remote laboratories should be minimized. This work ad- dressed this issue of minimizing the cost of use of the remote lab by making use of data compression for data sent between the user and remote lab. Keywords—Data compression; Lempel-Ziv-Welch; remote laboratory; Web- Sockets 1 Introduction The mobile phone is now the de facto computational device of choice. They are quite powerful, connected devices which are almost always with us. This is so much so that about every important online service has a mobile version or at least a version compatible with mobile phones and tablets (for this paper we would adopt the phrase mobile device to represent mobile phones and tablets). This has caused online labora- tory developers to develop online laboratory solutions which are mobile device- friendly [1, 2, 3, 4, 5, 6, 7, 8]. One of the main benefits of online laboratories is the possibility of using fewer re- sources to serve more people i.e. -
Randomized Lempel-Ziv Compression for Anti-Compression Side-Channel Attacks
Randomized Lempel-Ziv Compression for Anti-Compression Side-Channel Attacks by Meng Yang A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Applied Science in Electrical and Computer Engineering Waterloo, Ontario, Canada, 2018 c Meng Yang 2018 I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract Security experts confront new attacks on TLS/SSL every year. Ever since the compres- sion side-channel attacks CRIME and BREACH were presented during security conferences in 2012 and 2013, online users connecting to HTTP servers that run TLS version 1.2 are susceptible of being impersonated. We set up three Randomized Lempel-Ziv Models, which are built on Lempel-Ziv77, to confront this attack. Our three models change the determin- istic characteristic of the compression algorithm: each compression with the same input gives output of different lengths. We implemented SSL/TLS protocol and the Lempel- Ziv77 compression algorithm, and used them as a base for our simulations of compression side-channel attack. After performing the simulations, all three models successfully pre- vented the attack. However, we demonstrate that our randomized models can still be broken by a stronger version of compression side-channel attack that we created. But this latter attack has a greater time complexity and is easily detectable. Finally, from the results, we conclude that our models couldn't compress as well as Lempel-Ziv77, but they can be used against compression side-channel attacks. -
Conversion Principles and Circuits
Interfacing Analog and Digital Worlds: Conversion Principles and Circuits Nimal Skandhakumar Faculty of Technology University of Sri Jayewardenepura 2019 1 From Analog to Digital to Analog 2 Analog Signal ● A continuous signal that contains 1. Continuous time-varying quantities, such as 2. Infinite range of values temperature or speed, with infinite 3. More exact values, but more possible values in between difficult to work with ● Can be used to measure changes in some physical phenomena, such as light, sound, pressure, or temperature. 3 Analog Signal ● Advantages: 1. Major advantages of the analog signal is infinite amount of data. 2. Density is much higher. 3. Easy processing. ● Disadvantages: 1. Unwanted noise in recording. 2. If we transmit data at long distance then unwanted disturbance is there. 3. Generation loss is also a big con of analog signals. 4 Digital Signal ● A type of signal that can take on a 1. Discrete set of discrete values (a quantized 2. Finite range of values signal) 3. Not as exact as analog, but easier ● Can represent a discrete set of to work with values using any discrete set of waveforms; and we can represent it like (0 or 1), (on or off) 5 Difference between analog and digital signals Analog Digital Signalling Continuous signal Discrete time signal Data Subjected to deterioration by noise during Can be noise-immune without deterioration transmissions transmission and write/read cycle. during transmission and write/read cycle. Bandwidth Analog signal processing can be done in There is no guarantee that digital signal real time and consumes less bandwidth. processing can be done in real time and consumes more bandwidth to carry out the same information. -
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 -
Optimal Parsing for Dictionary Text Compression Alessio Langiu
Optimal Parsing for dictionary text compression Alessio Langiu To cite this version: Alessio Langiu. Optimal Parsing for dictionary text compression. Other [cs.OH]. Université Paris-Est, 2012. English. NNT : 2012PEST1091. tel-00804215 HAL Id: tel-00804215 https://tel.archives-ouvertes.fr/tel-00804215 Submitted on 25 Mar 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. UniversitadegliStudidiPalermo` Dipartimento di Matematica e Applicazioni Dottorato di Ricerca in Matematica e Informatica XXIII◦ Ciclo - S.S.D. Inf/01 UniversiteParis-Est´ Ecole´ doctorale MSTIC Th´ese de doctorat en Informatique Optimal Parsing for Dictionary Text Compression Author: LANGIU Alessio Ph.D. Thesis in Computer Science April 3, 2012 PhD Commission: Thesis Directors: Prof. CROCHEMORE Maxime University of Paris-Est Prof. RESTIVO Antonio University of Palermo Examiners: Prof. ILIOPOULOS Costas King’s College London Prof. LECROQ Thierry University of Rouen Prof. MIGNOSI Filippo University of L’Aquila Referees: Prof. GROSSI Roberto University of Pisa Prof. ILIOPOULOS Costas King’s College London Prof. LECROQ Thierry University of Rouen ii Summary Dictionary-based compression algorithms include a parsing strategy to transform the input text into a sequence of dictionary phrases. Given a text, such process usually is not unique and, for compression purposes, it makes sense to find one of the possible parsing that minimize the final compres- sion ratio. -
Dictionary Based Compression for Images
INTERNATIONAL JOURNAL OF COMPUTERS Issue 3, Volume 6, 2012 Dictionary Based Compression for Images Bruno Carpentieri Ziv and Lempel in [1]. Abstract—Lempel-Ziv methods were original introduced to By limiting what could enter the dictionary, LZ2 assures compress one-dimensional data (text, object codes, etc.) but recently that there is at most one instance for each possible pattern in they have been successfully used in image compression. the dictionary. Constantinescu and Storer in [6] introduced a single-pass vector Initially the dictionary is empty. The coding pass consists of quantization algorithm that, with no training or previous knowledge of the digital data was able to achieve better compression results with searching the dictionary for the longest entry that is a prefix of respect to the JPEG standard and had also important computational a string starting at the current coding position. advantages. The index of the match is transmitted to the decoder using We review some of our recent work on LZ-based, single pass, log N bits, where N is the current size of the dictionary. adaptive algorithms for the compression of digital images, taking into 2 account the theoretical optimality of these approach, and we A new pattern is introduced into the dictionary by experimentally analyze the behavior of this algorithm with respect to concatenating the current match with the next character that the local dictionary size and with respect to the compression of bi- has to be encoded. level images. The dictionary of LZ2 continues to grow throughout the coding process. Keywords—Image compression, textual substitution methods.