GNAT User's Guide Supplement for the JVM Platform
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Leveraging Activation Sparsity for Training Deep Neural Networks
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks Minsoo Rhu Mike O’Connor Niladrish Chatterjee Jeff Pool Stephen W. Keckler NVIDIA Santa Clara, CA 95050 fmrhu, moconnor, nchatterjee, jpool, [email protected] Abstract—Popular deep learning frameworks require users to the time needed to copy data back and forth through PCIe is fine-tune their memory usage so that the training data of a deep smaller than the time the GPU spends computing the DNN neural network (DNN) fits within the GPU physical memory. forward and backward propagation operations, vDNN does Prior work tries to address this restriction by virtualizing the memory usage of DNNs, enabling both CPU and GPU memory not affect performance. For networks whose memory copying to be utilized for memory allocations. Despite its merits, vir- operation is bottlenecked by the data transfer bandwidth of tualizing memory can incur significant performance overheads PCIe however, vDNN can incur significant overheads with an when the time needed to copy data back and forth from CPU average 31% performance loss (worst case 52%, Section III). memory is higher than the latency to perform the computations The trend in deep learning is to employ larger and deeper required for DNN forward and backward propagation. We introduce a high-performance virtualization strategy based on networks that leads to large memory footprints that oversub- a “compressing DMA engine” (cDMA) that drastically reduces scribe GPU memory [13, 14, 15]. Therefore, ensuring the the size of the data structures that are targeted for CPU-side performance scalability of the virtualization features offered allocations. -
How Can Java Class Files Be Decompiled?
HOW CAN JAVA CLASS FILES BE DECOMPILED? David Foster Chamblee High School 11th Grade Many computer science courses feature building a compiler as a final project. I have decided to take a spin on the idea and instead develop a decompiler. Specifically, I decided to write a Java decompiler because: one, I am already familiar with the Java language, two, the language syntax does not suffer from feature-bloat, and three, there do not appear to be many Java decompilers currently on the market. I have approached the task of writing a decompiler as a matter of translating between two different languages. In our case, the source language is Java Virtual Machine (VM) Bytecode, and the target language is the Java Language. As such, our decompiler will attempt to analyze the semantics of the bytecode and convert it into Java source code that has equivalent semantics. A nice sideeffect of this approach is that our decompiler will not be dependent upon the syntax of the bytecodes, the compiler used to generate the bytecodes, or the source language (possibly not Java). It will only care whether a class-file’s semantics can be represented in the Java language. Decompilation is divided up into six phases: 1. Read opcodes 2. Interpret opcode behavior 3. Identify Java-language “idioms” 4. Identify patterns of control-flow 5. Generate Java-language statements 6. Format and output to file The first four phases will be discussed in detail within this paper, as they were the primary focus of my research. Further details pertaining to these phases can be gleaned from the accompanying log book. -
Advanced Programming for the Java(TM) 2 Platform
Advanced Programming for the Java(TM) 2 Platform Training Index Advanced Programming for the JavaTM 2 Platform By Calvin Austin and Monica Pawlan November 1999 [CONTENTS] [NEXT>>] [DOWNLOAD] Requires login As an experienced developer on the JavaTM platform, you undoubtedly know how fast moving and comprehensive the Early Access platform is. Its many application programming interfaces (APIs) Downloads provide a wealth of functionality for all aspects of application and system-level programming. Real-world developers never use one Bug Database or two APIs to solve a problem, but bring together key Submit a Bug functionality spanning a number of APIs. Knowing which APIs you View Database need, which parts of which APIs you need, and how the APIs work together to create the best solution can be a daunting task. Newsletters Back Issues To help you navigate the Java APIs and fast-track your project Subscribe development time, this book includes the design, development, test, and deployment phases for an enterprise-worthy auction Learning Centers application. While the example application does not cover every Articles possible programming scenario, it explores many common Bookshelf situations and the discussions leave you with a solid methodology Code Samples for designing and building your own solutions. New to Java Question of the Week This book is for developers with more than a beginning level of Quizzes understanding of writing programs in the Java programming Tech Tips language. The example application is written with the Java® 2 Tutorials platform APIs and explained in terms of functional hows and whys, so if you need help installing the Java platform, setting up your Forums environment, or getting your first application to work, you should first read a more introductory book such as Essentials of the Java Programming Language: A Hands-On Guide or The Java Tutorial. -
Information Retrieval from Java Archive Format
Eötvös Loránd University Faculty of Informatics Department of Programming Languages and Compilers Information retrieval from Java archive format Supervisor: Author: Dr. Zoltán Porkoláb Bálint Kiss Associate Professor Computer Science MSc Budapest, 2017 Abstract During the course of my work, I contributed to CodeCompass, an open source code comprehension tool made for making codebase of software projects written in C, C++ and Java more understandable through navigation and visualization. I was tasked with the development of a module for recovering code information of Java classes in JAR files. This document details background concepts required for reverse- engineering Java bytecode, creating a prototype JAR file reader and how this solu- tion could be integrated to CodeCompass. First, I studied the structure of JAR format and how class files are stored in it. I looked into the Java Class file structure and how bytecode contained in class gets interpreted by the Java Virtual Machine. I also looked at existing decompilers and what bytecode libraries are. I created a proof-of-concept prototype that reads compiled classes from JAR file and extracts code information. I first showcased the use of Java Reflection API, then the use of Apache Commons Byte Code Engineering Library, a third-party bytecode library used for extracting and representing parts of Java class file as Java objects. Finally, I examined how CodeCompass works, how part of the prototype could be integrated into it and demonstrated the integration through parsing of a simple JAR file. i Acknowledgements I would like to thank Dr. Zoltán Porkoláb, Associate Professor of the Department of Programming Languages and Compilers at the Faculty of Informatics for admit- ting me to the CodeCompass project, supplying the thesis topic and helping with the documentation. -
NXP Eiq Machine Learning Software Development Environment For
AN12580 NXP eIQ™ Machine Learning Software Development Environment for QorIQ Layerscape Applications Processors Rev. 3 — 12/2020 Application Note Contents 1 Introduction 1 Introduction......................................1 Machine Learning (ML) is a computer science domain that has its roots in 2 NXP eIQ software introduction........1 3 Building eIQ Components............... 2 the 1960s. ML provides algorithms capable of finding patterns and rules in 4 OpenCV getting started guide.........3 data. ML is a category of algorithm that allows software applications to become 5 Arm Compute Library getting started more accurate in predicting outcomes without being explicitly programmed. guide................................................7 The basic premise of ML is to build algorithms that can receive input data and 6 TensorFlow Lite getting started guide use statistical analysis to predict an output while updating output as new data ........................................................ 8 becomes available. 7 Arm NN getting started guide........10 8 ONNX Runtime getting started guide In 2010, the so-called deep learning started. It is a fast-growing subdomain ...................................................... 16 of ML, based on Neural Networks (NN). Inspired by the human brain, 9 PyTorch getting started guide....... 17 deep learning achieved state-of-the-art results in various tasks; for example, A Revision history.............................17 Computer Vision (CV) and Natural Language Processing (NLP). Neural networks are capable of learning complex patterns from millions of examples. A huge adaptation is expected in the embedded world, where NXP is the leader. NXP created eIQ machine learning software for QorIQ Layerscape applications processors, a set of ML tools which allows developing and deploying ML applications on the QorIQ Layerscape family of devices. -
Oak Park Area Visitor Guide
OAK PARK AREA VISITOR GUIDE COMMUNITIES Bellwood Berkeley Broadview Brookfield Elmwood Park Forest Park Franklin Park Hillside Maywood Melrose Park Northlake North Riverside Oak Park River Forest River Grove Riverside Schiller Park Westchester www.visitoakpark.comvisitoakpark.com | 1 OAK PARK AREA VISITORS GUIDE Table of Contents WELCOME TO THE OAK PARK AREA ..................................... 4 COMMUNITIES ....................................................................... 6 5 WAYS TO EXPERIENCE THE OAK PARK AREA ..................... 8 BEST BETS FOR EVERY SEASON ........................................... 13 OAK PARK’S BUSINESS DISTRICTS ........................................ 15 ATTRACTIONS ...................................................................... 16 ACCOMMODATIONS ............................................................ 20 EATING & DRINKING ............................................................ 22 SHOPPING ............................................................................ 34 ARTS & CULTURE .................................................................. 36 EVENT SPACES & FACILITIES ................................................ 39 LOCAL RESOURCES .............................................................. 41 TRANSPORTATION ............................................................... 46 ADVERTISER INDEX .............................................................. 47 SPRING/SUMMER 2018 EDITION Compiled & Edited By: Kevin Kilbride & Valerie Revelle Medina Visit Oak Park -
Archive and Compressed [Edit]
Archive and compressed [edit] Main article: List of archive formats • .?Q? – files compressed by the SQ program • 7z – 7-Zip compressed file • AAC – Advanced Audio Coding • ace – ACE compressed file • ALZ – ALZip compressed file • APK – Applications installable on Android • AT3 – Sony's UMD Data compression • .bke – BackupEarth.com Data compression • ARC • ARJ – ARJ compressed file • BA – Scifer Archive (.ba), Scifer External Archive Type • big – Special file compression format used by Electronic Arts for compressing the data for many of EA's games • BIK (.bik) – Bink Video file. A video compression system developed by RAD Game Tools • BKF (.bkf) – Microsoft backup created by NTBACKUP.EXE • bzip2 – (.bz2) • bld - Skyscraper Simulator Building • c4 – JEDMICS image files, a DOD system • cab – Microsoft Cabinet • cals – JEDMICS image files, a DOD system • cpt/sea – Compact Pro (Macintosh) • DAA – Closed-format, Windows-only compressed disk image • deb – Debian Linux install package • DMG – an Apple compressed/encrypted format • DDZ – a file which can only be used by the "daydreamer engine" created by "fever-dreamer", a program similar to RAGS, it's mainly used to make somewhat short games. • DPE – Package of AVE documents made with Aquafadas digital publishing tools. • EEA – An encrypted CAB, ostensibly for protecting email attachments • .egg – Alzip Egg Edition compressed file • EGT (.egt) – EGT Universal Document also used to create compressed cabinet files replaces .ecab • ECAB (.ECAB, .ezip) – EGT Compressed Folder used in advanced systems to compress entire system folders, replaced by EGT Universal Document • ESS (.ess) – EGT SmartSense File, detects files compressed using the EGT compression system. • GHO (.gho, .ghs) – Norton Ghost • gzip (.gz) – Compressed file • IPG (.ipg) – Format in which Apple Inc. -
Java Class Annotation Example
Java Class Annotation Example Bud etherealizing her matriarch inimitably, unwavering and decayed. Antimonious and regulation Rog violated almost anywhere, though Saunderson tosses his Creon outdating. Which Roland disinhuming so ordinarily that Westleigh tooth her opposites? It is class to classes are pretty easy to reflection to list to catch, company or property to generate mock for? First method of java example. The java are given list of bibliography. Default annotations example, java code examples of the compiler to be applied to json. The new project for extracting information to create web sites without editions but i go into the annotated bibliography paper, we welcome to isolate the mechanics of using. Parsing the examples can be used from open source and. We will utilize the class analyzes the same type declaration etc at runtime reflection methods or evaluative lists the suite itself which are. Nice explanation about the front controller, we added and then issues a class of the information, management example demonstrates how can be executed and. There are working standalone spring boot application into system proposed here, because of annotations. Its annotations example annotation examples java. Hibernate does not to class or specify java example of fundamental never hurt or not. Read from java class acts as each article has been designed with examples select then we learned about? We made as classes that class or examples of annotation example classes or an earlier article example it tells spring. The table pointing back to interface. From java example is applied only contains a second argument. Junit provides a test cases, we want specific warnings and ideas into our newsletter for an understanding takt time in. -
Java and C I CSE 351 Autumn 2016
L26: JVM CSE351, Spring 2018 Java Virtual Machine CSE 351 Spring 2018 Model of a Computer “Showing the Weather” Pencil and Crayon on Paper Matai Feldacker-Grossman, Age 4 May 22, 2018 L26: JVM CSE351, Spring 2018 Roadmap C: Java: Memory & data Integers & floats car *c = malloc(sizeof(car)); Car c = new Car(); x86 assembly c->miles = 100; c.setMiles(100); c->gals = 17; c.setGals(17); Procedures & stacks float mpg = get_mpg(c); float mpg = Executables free(c); c.getMPG(); Arrays & structs Memory & caches Assembly get_mpg: Processes language: pushq %rbp Virtual memory movq %rsp, %rbp ... Memory allocation popq %rbp Java vs. C ret OS: Machine 0111010000011000 code: 100011010000010000000010 1000100111000010 110000011111101000011111 Computer system: 2 L26: JVM CSE351, Spring 2018 Implementing Programming Languages Many choices in how to implement programming models We’ve talked about compilation, can also interpret Interpreting languages has a long history . Lisp, an early programming language, was interpreted Interpreters are still in common use: . Python, Javascript, Ruby, Matlab, PHP, Perl, … Interpreter Your source code implementation Your source code Binary executable Interpreter binary Hardware Hardware 3 L26: JVM CSE351, Spring 2018 An Interpreter is a Program Execute (something close to) the source code directly Simpler/no compiler – less translation More transparent to debug – less translation Easier to run on different architectures – runs in a simulated environment that exists only inside the interpreter process . Just port the interpreter (program), not the program-intepreted Slower and harder to optimize 4 L26: JVM CSE351, Spring 2018 Interpreter vs. Compiler An aspect of a language implementation . A language can have multiple implementations . Some might be compilers and other interpreters “Compiled languages” vs. -
Hibernate Tools
APPENDIX A ■ ■ ■ More Advanced Features In this appendix, we discuss some of the features that, strictly speaking, lie outside the scope of this book, but that you should be aware of if you go on to use Hibernate in more depth. Managed Versioning and Optimistic Locking While we have saved versioning for this appendix’s discussion of advanced features, it is actually quite straightforward to understand and apply. Consider the following scenario: • Client A loads and edits a record. • Client B loads and edits the same record. • Client A commits its edited record data. • Client B commits its differently edited record data. While the scenario is simple, the problems it presents are not. If Client A establishes a transaction, then Client B may not be able to load and edit the same record. Yet in a web environment, it is not unlikely that Client A will close a browser window on the open record, never committing or canceling the transaction, so that the record remains locked until the session times out. Clearly this is not a satisfactory solution. Usually, you will not want to permit the alternative scenario, in which no locking is used, and the last person to save a record wins! The solution, versioning, is essentially a type of optimistic locking (see Chapter 8). When any changes to an entity are stored, a version column is updated to reflect the fact that the entity has changed. When a subsequent user tries to commit changes to the same entity, the original version number will be compared against the current value—if they differ, the commit will be rejected. -
Forcepoint DLP Supported File Formats and Size Limits
Forcepoint DLP Supported File Formats and Size Limits Supported File Formats and Size Limits | Forcepoint DLP | v8.8.1 This article provides a list of the file formats that can be analyzed by Forcepoint DLP, file formats from which content and meta data can be extracted, and the file size limits for network, endpoint, and discovery functions. See: ● Supported File Formats ● File Size Limits © 2021 Forcepoint LLC Supported File Formats Supported File Formats and Size Limits | Forcepoint DLP | v8.8.1 The following tables lists the file formats supported by Forcepoint DLP. File formats are in alphabetical order by format group. ● Archive For mats, page 3 ● Backup Formats, page 7 ● Business Intelligence (BI) and Analysis Formats, page 8 ● Computer-Aided Design Formats, page 9 ● Cryptography Formats, page 12 ● Database Formats, page 14 ● Desktop publishing formats, page 16 ● eBook/Audio book formats, page 17 ● Executable formats, page 18 ● Font formats, page 20 ● Graphics formats - general, page 21 ● Graphics formats - vector graphics, page 26 ● Library formats, page 29 ● Log formats, page 30 ● Mail formats, page 31 ● Multimedia formats, page 32 ● Object formats, page 37 ● Presentation formats, page 38 ● Project management formats, page 40 ● Spreadsheet formats, page 41 ● Text and markup formats, page 43 ● Word processing formats, page 45 ● Miscellaneous formats, page 53 Supported file formats are added and updated frequently. Key to support tables Symbol Description Y The format is supported N The format is not supported P Partial metadata -
Data Compression and Archiving Software Implementation and Their Algorithm Comparison
Calhoun: The NPS Institutional Archive Theses and Dissertations Thesis Collection 1992-03 Data compression and archiving software implementation and their algorithm comparison Jung, Young Je Monterey, California. Naval Postgraduate School http://hdl.handle.net/10945/26958 NAVAL POSTGRADUATE SCHOOL Monterey, California THESIS^** DATA COMPRESSION AND ARCHIVING SOFTWARE IMPLEMENTATION AND THEIR ALGORITHM COMPARISON by Young Je Jung March, 1992 Thesis Advisor: Chyan Yang Approved for public release; distribution is unlimited T25 46 4 I SECURITY CLASSIFICATION OF THIS PAGE REPORT DOCUMENTATION PAGE la REPORT SECURITY CLASSIFICATION 1b RESTRICTIVE MARKINGS UNCLASSIFIED 2a SECURITY CLASSIFICATION AUTHORITY 3 DISTRIBUTION/AVAILABILITY OF REPORT Approved for public release; distribution is unlimite 2b DECLASSIFICATION/DOWNGRADING SCHEDULE 4 PERFORMING ORGANIZATION REPORT NUMBER(S) 5 MONITORING ORGANIZATION REPORT NUMBER(S) 6a NAME OF PERFORMING ORGANIZATION 6b OFFICE SYMBOL 7a NAME OF MONITORING ORGANIZATION Naval Postgraduate School (If applicable) Naval Postgraduate School 32 6c ADDRESS {City, State, and ZIP Code) 7b ADDRESS (City, State, and ZIP Code) Monterey, CA 93943-5000 Monterey, CA 93943-5000 8a NAME OF FUNDING/SPONSORING 8b OFFICE SYMBOL 9 PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER ORGANIZATION (If applicable) 8c ADDRESS (City, State, and ZIP Code) 10 SOURCE OF FUNDING NUMBERS Program Element No Project Nc Work Unit Accession Number 1 1 TITLE (Include Security Classification) DATA COMPRESSION AND ARCHIVING SOFTWARE IMPLEMENTATION AND THEIR ALGORITHM COMPARISON 12 PERSONAL AUTHOR(S) Young Je Jung 13a TYPE OF REPORT 13b TIME COVERED 1 4 DATE OF REPORT (year, month, day) 15 PAGE COUNT Master's Thesis From To March 1992 94 16 SUPPLEMENTARY NOTATION The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S.