Vectorization for Java
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Writing R Extensions
Writing R Extensions Version 4.2.0 Under development (2021-09-29) R Core Team This manual is for R, version 4.2.0 Under development (2021-09-29). Copyright c 1999{2021 R Core Team Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into an- other language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team. i Table of Contents Acknowledgements ::::::::::::::::::::::::::::::::::::::::::::::::: 1 1 Creating R packages ::::::::::::::::::::::::::::::::::::::::::: 2 1.1 Package structure :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 3 1.1.1 The DESCRIPTION file ::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 4 1.1.2 Licensing ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 8 1.1.3 Package Dependencies::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 9 1.1.3.1 Suggested packages:::::::::::::::::::::::::::::::::::::::::::::::::::::: 12 1.1.4 The INDEX file ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 13 1.1.5 Package subdirectories ::::::::::::::::::::::::::::::::::::::::::::::::::::::: -
Java (Programming Langua a (Programming Language)
Java (programming language) From Wikipedia, the free encyclopedialopedia "Java language" redirects here. For the natural language from the Indonesian island of Java, see Javanese language. Not to be confused with JavaScript. Java multi-paradigm: object-oriented, structured, imperative, Paradigm(s) functional, generic, reflective, concurrent James Gosling and Designed by Sun Microsystems Developer Oracle Corporation Appeared in 1995[1] Java Standard Edition 8 Update Stable release 5 (1.8.0_5) / April 15, 2014; 2 months ago Static, strong, safe, nominative, Typing discipline manifest Major OpenJDK, many others implementations Dialects Generic Java, Pizza Ada 83, C++, C#,[2] Eiffel,[3] Generic Java, Mesa,[4] Modula- Influenced by 3,[5] Oberon,[6] Objective-C,[7] UCSD Pascal,[8][9] Smalltalk Ada 2005, BeanShell, C#, Clojure, D, ECMAScript, Influenced Groovy, J#, JavaScript, Kotlin, PHP, Python, Scala, Seed7, Vala Implementation C and C++ language OS Cross-platform (multi-platform) GNU General Public License, License Java CommuniCommunity Process Filename .java , .class, .jar extension(s) Website For Java Developers Java Programming at Wikibooks Java is a computer programming language that is concurrent, class-based, object-oriented, and specifically designed to have as few impimplementation dependencies as possible.ble. It is intended to let application developers "write once, run ananywhere" (WORA), meaning that code that runs on one platform does not need to be recompiled to rurun on another. Java applications ns are typically compiled to bytecode (class file) that can run on anany Java virtual machine (JVM)) regardless of computer architecture. Java is, as of 2014, one of tthe most popular programming ng languages in use, particularly for client-server web applications, witwith a reported 9 million developers.[10][11] Java was originallyy developed by James Gosling at Sun Microsystems (which has since merged into Oracle Corporation) and released in 1995 as a core component of Sun Microsystems'Micros Java platform. -
Real-Time Java for Embedded Devices: the Javamen Project*
REAL-TIME JAVA FOR EMBEDDED DEVICES: THE JAVAMEN PROJECT* A. Borg, N. Audsley, A. Wellings The University of York, UK ABSTRACT: Hardware Java-specific processors have been shown to provide the performance benefits over their software counterparts that make Java a feasible environment for executing even the most computationally expensive systems. In most cases, the core of these processors is a simple stack machine on which stack operations and logic and arithmetic operations are carried out. More complex bytecodes are implemented either in microcode through a sequence of stack and memory operations or in Java and therefore through a set of bytecodes. This paper investigates the Figure 1: Three alternatives for executing Java code (take from (6)) state-of-the-art in Java processors and identifies two areas of improvement for specialising these processors timeliness as a key issue. The language therefore fails to for real-time applications. This is achieved through a allow more advanced temporal requirements of threads combination of the implementation of real-time Java to be expressed and virtual machine implementations components in hardware and by using application- may behave unpredictably in this context. For example, specific characteristics expressed at the Java level to whereas a basic thread priority can be specified, it is not drive a co-design strategy. An implementation of these required to be observed by a virtual machine and there propositions will provide a flexible Ravenscar- is no guarantee that the highest priority thread will compliant virtual machine that provides better preempt lower priority threads. In order to address this performance while still guaranteeing real-time shortcoming, two competing specifications have been requirements. -
Vectorization Optimization
Vectorization Optimization Klaus-Dieter Oertel Intel IAGS HLRN User Workshop, 3-6 Nov 2020 Optimization Notice Copyright © 2020, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Changing Hardware Impacts Software More Cores → More Threads → Wider Vectors Intel® Xeon® Processor Intel® Xeon Phi™ processor 5100 5500 5600 E5-2600 E5-2600 E5-2600 Platinum 64-bit E5-2600 Knights Landing series series series V2 V3 V4 8180 Core(s) 1 2 4 6 8 12 18 22 28 72 Threads 2 2 8 12 16 24 36 44 56 288 SIMD Width 128 128 128 128 256 256 256 256 512 512 High performance software must be both ▪ Parallel (multi-thread, multi-process) ▪ Vectorized *Product specification for launched and shipped products available on ark.intel.com. Optimization Notice Copyright © 2020, Intel Corporation. All rights reserved. 2 *Other names and brands may be claimed as the property of others. Vectorize & Thread or Performance Dies Threaded + Vectorized can be much faster than either one alone Vectorized & Threaded “Automatic” Vectorization Not Enough Explicit pragmas and optimization often required The Difference Is Growing With Each New 130x Generation of Threaded Hardware Vectorized Serial 2010 2012 2013 2014 2016 2017 Intel® Xeon™ Intel® Xeon™ Intel® Xeon™ Intel® Xeon™ Intel® Xeon™ Intel® Xeon® Platinum Processor Processor Processor Processor Processor Processor X5680 E5-2600 E5-2600 v2 E5-2600 v3 E5-2600 v4 81xx formerly codenamed formerly codenamed formerly codenamed formerly codenamed formerly codenamed formerly codenamed Westmere Sandy Bridge Ivy Bridge Haswell Broadwell Skylake Server Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. -
Zing:® the Best JVM for the Enterprise
PRODUCT DATA SHEET Zing:® ZING The best JVM for the enterprise Zing Runtime for Java A JVM that is compatible and compliant The Performance Standard for Low Latency, with the Java SE specification. Zing is a Memory-Intensive or Interactive Applications better alternative to your existing JVM. INTRODUCING ZING Zing Vision (ZVision) Today Java is ubiquitous across the enterprise. Flexible and powerful, Java is the ideal choice A zero-overhead, always-on production- for development teams worldwide. time monitoring tool designed to support rapid troubleshooting of applications using Zing. Zing builds upon Java’s advantages by delivering a robust, highly scalable Java Virtual Machine ReadyNow! Technology (JVM) to match the needs of today’s real time enterprise. Zing is the best JVM choice for all Solves Java warm-up problems, gives Java workloads, including low-latency financial systems, SaaS or Cloud-based deployments, developers fine-grained control over Web-based eCommerce applications, insurance portals, multi-user gaming platforms, Big Data, compilation and allows DevOps to save and other use cases -- anywhere predictable Java performance is essential. and reuse accumulated optimizations. Zing enables developers to make effective use of memory -- without the stalls, glitches and jitter that have been part of Java’s heritage, and solves JVM “warm-up” problems that can degrade ZING ADVANTAGES performance at start up. With improved memory-handling and a more stable, consistent runtime Takes advantage of the large memory platform, Java developers can build and deploy richer applications incorporating real-time data and multiple CPU cores available in processing and analytics, driving new revenue and supporting new business innovations. -
Bypassing Portability Pitfalls of High-Level Low-Level Programming
Bypassing Portability Pitfalls of High-level Low-level Programming Yi Lin, Stephen M. Blackburn Australian National University [email protected], [email protected] Abstract memory-safety, encapsulation, and strong abstraction over hard- Program portability is an important software engineering consider- ware [12], which are desirable goals for system programming as ation. However, when high-level languages are extended to effec- well. Thus, high-level languages are potential candidates for sys- tively implement system projects for software engineering gain and tem programming. safety, portability is compromised—high-level code for low-level Prior research has focused on the feasibility and performance of programming cannot execute on a stock runtime, and, conversely, applying high-level languages to system programming [1, 7, 10, a runtime with special support implemented will not be portable 15, 16, 21, 22, 26–28]. The results showed that, with proper ex- across different platforms. tension and restriction, high-level languages are able to undertake We explore the portability pitfall of high-level low-level pro- the task of low-level programming, while preserving type-safety, gramming in the context of virtual machine implementation tasks. memory-safety, encapsulation and abstraction. Notwithstanding the Our approach is designing a restricted high-level language called cost for dynamic compilation and garbage collection, the perfor- RJava, with a flexible restriction model and effective low-level ex- mance of high-level languages when used to implement a virtual tensions, which is suitable for different scopes of virtual machine machine is still competitive with using a low-level language [2]. implementation, and also suitable for a low-level language bypass Using high-level languages to architect large systems is bene- for improved portability. -
Here I Led Subcommittee Reports Related to Data-Intensive Science and Post-Moore Computing) and in CRA’S Board of Directors Since 2015
Vivek Sarkar Curriculum Vitae Contents 1 Summary 2 2 Education 3 3 Professional Experience 3 3.1 2017-present: College of Computing, Georgia Institute of Technology . 3 3.2 2007-present: Department of Computer Science, Rice University . 5 3.3 1987-2007: International Business Machines Corporation . 7 4 Professional Awards 11 5 Research Awards 12 6 Industry Gifts 15 7 Graduate Student and Other Mentoring 17 8 Professional Service 19 8.1 Conference Committees . 20 8.2 Advisory/Award/Review/Steering Committees . 25 9 Keynote Talks, Invited Talks, Panels (Selected) 27 10 Teaching 33 11 Publications 37 11.1 Refereed Conference and Journal Publications . 37 11.2 Refereed Workshop Publications . 51 11.3 Books, Book Chapters, and Edited Volumes . 58 12 Patents 58 13 Software Artifacts (Selected) 59 14 Personal Information 60 Page 1 of 60 01/06/2020 1 Summary Over thirty years of sustained contributions to programming models, compilers and runtime systems for high performance computing, which include: 1) Leading the development of ASTI during 1991{1996, IBM's first product compiler component for optimizing locality, parallelism, and the (then) new FORTRAN 90 high-productivity array language (ASTI has continued to ship as part of IBM's XL Fortran product compilers since 1996, and was also used as the foundation for IBM's High Performance Fortran compiler product); 2) Leading the research and development of the open source Jikes Research Virtual Machine at IBM during 1998{2001, a first-of-a-kind Java Virtual Machine (JVM) and dynamic compiler implemented -
Vegen: a Vectorizer Generator for SIMD and Beyond
VeGen: A Vectorizer Generator for SIMD and Beyond Yishen Chen Charith Mendis Michael Carbin Saman Amarasinghe MIT CSAIL UIUC MIT CSAIL MIT CSAIL USA USA USA USA [email protected] [email protected] [email protected] [email protected] ABSTRACT Vector instructions are ubiquitous in modern processors. Traditional A1 A2 A3 A4 A1 A2 A3 A4 compiler auto-vectorization techniques have focused on targeting single instruction multiple data (SIMD) instructions. However, these B1 B2 B3 B4 B1 B2 B3 B4 auto-vectorization techniques are not sufficiently powerful to model non-SIMD vector instructions, which can accelerate applications A1+B1 A2+B2 A3+B3 A4+B4 A1+B1 A2-B2 A3+B3 A4-B4 in domains such as image processing, digital signal processing, and (a) vaddpd (b) vaddsubpd machine learning. To target non-SIMD instruction, compiler devel- opers have resorted to complicated, ad hoc peephole optimizations, expending significant development time while still coming up short. A1 A2 A3 A4 A1 A2 A3 A4 A5 A6 A7 A8 As vector instruction sets continue to rapidly evolve, compilers can- B1 B2 B3 B4 B5 B6 B7 B8 not keep up with these new hardware capabilities. B1 B2 B3 B4 In this paper, we introduce Lane Level Parallelism (LLP), which A1*B1+ A3*B3+ A5*B5+ A7*B8+ captures the model of parallelism implemented by both SIMD and A1+A2 B1+B2 A3+A4 B3+B4 A2*B2 A4*B4 A6*B6 A7*B8 non-SIMD vector instructions. We present VeGen, a vectorizer gen- (c) vhaddpd (d) vpmaddwd erator that automatically generates a vectorization pass to uncover target-architecture-specific LLP in programs while using only in- Figure 1: Examples of SIMD and non-SIMD instruction in struction semantics as input. -
Techniques for Real-System Characterization of Java Virtual Machine Energy and Power Behavior Gilberto Contreras Margaret Martonosi
Techniques for Real-System Characterization of Java Virtual Machine Energy and Power Behavior Gilberto Contreras Margaret Martonosi Department of Electrical Engineering Princeton University 1 Why Study Power in Java Systems? The Java platform has been adopted in a wide variety of devices Java servers demand performance, embedded devices require low-power Performance is important, power/energy/thermal issues are equally important How do we study and characterize these requirements in a multi-layer platform? 2 Power/Performance Design Issues Java Application Java Virtual Machine Operating System Hardware 3 Power/Performance Design Issues Java Application Garbage Class Runtime Execution Collection LoaderJava VirtualCompiler MachineEngine Operating System Hardware How do the various software layers affect power/performance characteristics of hardware? Where should time be invested when designing power and/or thermally aware Java virtual Machines? 4 Outline Approaches for Energy/Performance Characterization of Java virtual machines Methodology Breaking the JVM into sub-components Hardware-based power/performance characterization of JVM sub-components Results Jikes & Kaffe on Pentium M Kaffe on Intel XScale Conclusions 5 Power & Performance Analysis of Java Simulation Approach √ Flexible: easy to model non-existent hardware x Simulators may lack comprehensiveness and accuracy x Thermal studies require tens of seconds granularity Accurate simulators are too slow Hardware Approach √ Able to capture full-system characteristics -
Exploiting Automatic Vectorization to Employ SPMD on SIMD Registers
Exploiting automatic vectorization to employ SPMD on SIMD registers Stefan Sprenger Steffen Zeuch Ulf Leser Department of Computer Science Intelligent Analytics for Massive Data Department of Computer Science Humboldt-Universitat¨ zu Berlin German Research Center for Artificial Intelligence Humboldt-Universitat¨ zu Berlin Berlin, Germany Berlin, Germany Berlin, Germany [email protected] [email protected] [email protected] Abstract—Over the last years, vectorized instructions have multi-threading with SIMD instructions1. For these reasons, been successfully applied to accelerate database algorithms. How- vectorization is essential for the performance of database ever, these instructions are typically only available as intrinsics systems on modern CPU architectures. and specialized for a particular hardware architecture or CPU model. As a result, today’s database systems require a manual tai- Although modern compilers, like GCC [2], provide auto loring of database algorithms to the underlying CPU architecture vectorization [1], typically the generated code is not as to fully utilize all vectorization capabilities. In practice, this leads efficient as manually-written intrinsics code. Due to the strict to hard-to-maintain code, which cannot be deployed on arbitrary dependencies of SIMD instructions on the underlying hardware, hardware platforms. In this paper, we utilize ispc as a novel automatically transforming general scalar code into high- compiler that employs the Single Program Multiple Data (SPMD) execution model, which is usually found on GPUs, on the SIMD performing SIMD programs remains a (yet) unsolved challenge. lanes of modern CPUs. ispc enables database developers to exploit To this end, all techniques for auto vectorization have focused vectorization without requiring low-level details or hardware- on enhancing conventional C/C++ programs with SIMD instruc- specific knowledge. -
IT Acronyms.Docx
List of computing and IT abbreviations /.—Slashdot 1GL—First-Generation Programming Language 1NF—First Normal Form 10B2—10BASE-2 10B5—10BASE-5 10B-F—10BASE-F 10B-FB—10BASE-FB 10B-FL—10BASE-FL 10B-FP—10BASE-FP 10B-T—10BASE-T 100B-FX—100BASE-FX 100B-T—100BASE-T 100B-TX—100BASE-TX 100BVG—100BASE-VG 286—Intel 80286 processor 2B1Q—2 Binary 1 Quaternary 2GL—Second-Generation Programming Language 2NF—Second Normal Form 3GL—Third-Generation Programming Language 3NF—Third Normal Form 386—Intel 80386 processor 1 486—Intel 80486 processor 4B5BLF—4 Byte 5 Byte Local Fiber 4GL—Fourth-Generation Programming Language 4NF—Fourth Normal Form 5GL—Fifth-Generation Programming Language 5NF—Fifth Normal Form 6NF—Sixth Normal Form 8B10BLF—8 Byte 10 Byte Local Fiber A AAT—Average Access Time AA—Anti-Aliasing AAA—Authentication Authorization, Accounting AABB—Axis Aligned Bounding Box AAC—Advanced Audio Coding AAL—ATM Adaptation Layer AALC—ATM Adaptation Layer Connection AARP—AppleTalk Address Resolution Protocol ABCL—Actor-Based Concurrent Language ABI—Application Binary Interface ABM—Asynchronous Balanced Mode ABR—Area Border Router ABR—Auto Baud-Rate detection ABR—Available Bitrate 2 ABR—Average Bitrate AC—Acoustic Coupler AC—Alternating Current ACD—Automatic Call Distributor ACE—Advanced Computing Environment ACF NCP—Advanced Communications Function—Network Control Program ACID—Atomicity Consistency Isolation Durability ACK—ACKnowledgement ACK—Amsterdam Compiler Kit ACL—Access Control List ACL—Active Current -
Fedora Core, Java™ and You
Fedora Core, Java™ and You Gary Benson Software Engineer What is Java? The word ªJavaº is used to describe three things: The Java programming language The Java virtual machine The Java platform To support Java applications Fedora needs all three. What Fedora uses: GCJ and ECJ GCJ is the core of Fedora©s Java support: GCJ includes gcj, a compiler for the Java programming language. GCJ also has a runtime and class library, collectively called libgcj. The class library is separately known as GNU Classpath. ECJ is the Eclipse Compiler for Java: GCJ©s compiler gcj is not used for ªtraditionalº Java compilation. More on that later... Why libgcj? There are many free Java Virtual machines: Cacao, IKVM, JamVM, Jikes RVM, Kaffe, libgcj, Sable VM, ... There are two main reasons Fedora uses libgcj: Availability on many platforms. Ability to use precompiled native code. GNU Classpath Free core class library for Java virtual machines and compilers. The JPackage Project A collection of some 1,600 Java software packages for Linux: Distribution-agnostic RPM packages. Both runtimes/development kits and applications. Segregation between free and non-free packages. All free packages built entirely from source. Multiple runtimes/development kits may be installed. Fedora includes: JPackage-compatible runtime and development kit packages. A whole bunch of applications. JPackage JOnAS Fedora©s Java Compilers gcj can operate in several modes: Java source (.java) to Java bytecode (.class) Java source (.java) to native machine code (.o) Java bytecode (.class, .jar) to native machine code (.o) In Fedora: ECJ compiles Java source to bytecode. gcj compiles that bytecode to native machine code.