Java™ on the Bare Metal of Wireless Sensor Devices—The
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Events, Co-Routines, Continuations and Threads OS (And Application)Execution Models System Building
Events, Co-routines, Continuations and Threads OS (and application)Execution Models System Building General purpose systems need to deal with • Many activities – potentially overlapping – may be interdependent • Activities that depend on external phenomena – may requiring waiting for completion (e.g. disk read) – reacting to external triggers (e.g. interrupts) Need a systematic approach to system structuring © Kevin Elphinstone 2 Construction Approaches Events Coroutines Threads Continuations © Kevin Elphinstone 3 Events External entities generate (post) events. • keyboard presses, mouse clicks, system calls Event loop waits for events and calls an appropriate event handler. • common paradigm for GUIs Event handler is a function that runs until completion and returns to the event loop. © Kevin Elphinstone 4 Event Model The event model only requires a single stack Memory • All event handlers must return to the event loop CPU Event – No blocking Loop – No yielding PC Event SP Handler 1 REGS Event No preemption of handlers Handler 2 • Handlers generally short lived Event Handler 3 Data Stack © Kevin Elphinstone 5 What is ‘a’? int a; /* global */ int func() { a = 1; if (a == 1) { a = 2; } No concurrency issues within a return a; handler } © Kevin Elphinstone 6 Event-based kernel on CPU with protection Kernel-only Memory User Memory CPU Event Loop Scheduling? User PC Event Code SP Handler 1 REGS Event Handler 2 User Event Data Handler 3 Huh? How to support Data Stack multiple Stack processes? © Kevin Elphinstone 7 Event-based kernel on CPU with protection Kernel-only Memory User Memory CPU PC Trap SP Dispatcher User REGS Event Code Handler 1 User-level state in PCB Event PCB Handler 2 A User Kernel starts on fresh Timer Event Data stack on each trap (Scheduler) PCB B No interrupts, no blocking Data Current in kernel mode Thead PCB C Stack Stack © Kevin Elphinstone 8 Co-routines Originally described in: • Melvin E. -
Designing an Ultra Low-Overhead Multithreading Runtime for Nim
Designing an ultra low-overhead multithreading runtime for Nim Mamy Ratsimbazafy Weave [email protected] https://github.com/mratsim/weave Hello! I am Mamy Ratsimbazafy During the day blockchain/Ethereum 2 developer (in Nim) During the night, deep learning and numerical computing developer (in Nim) and data scientist (in Python) You can contact me at [email protected] Github: mratsim Twitter: m_ratsim 2 Where did this talk came from? ◇ 3 years ago: started writing a tensor library in Nim. ◇ 2 threading APIs at the time: OpenMP and simple threadpool ◇ 1 year ago: complete refactoring of the internals 3 Agenda ◇ Understanding the design space ◇ Hardware and software multithreading: definitions and use-cases ◇ Parallel APIs ◇ Sources of overhead and runtime design ◇ Minimum viable runtime plan in a weekend 4 Understanding the 1 design space Concurrency vs parallelism, latency vs throughput Cooperative vs preemptive, IO vs CPU 5 Parallelism is not 6 concurrency Kernel threading 7 models 1:1 Threading 1 application thread -> 1 hardware thread N:1 Threading N application threads -> 1 hardware thread M:N Threading M application threads -> N hardware threads The same distinctions can be done at a multithreaded language or multithreading runtime level. 8 The problem How to schedule M tasks on N hardware threads? Latency vs 9 Throughput - Do we want to do all the work in a minimal amount of time? - Numerical computing - Machine learning - ... - Do we want to be fair? - Clients-server - Video decoding - ... Cooperative vs 10 Preemptive Cooperative multithreading: -
An Ideal Match?
24 November 2020 An ideal match? Investigating how well-suited Concurrent ML is to implementing Belief Propagation for Stereo Matching James Cooper [email protected] OutlineI 1 Stereo Matching Generic Stereo Matching Belief Propagation 2 Concurrent ML Overview Investigation of Alternatives Comparative Benchmarks 3 Concurrent ML and Belief Propagation 4 Conclusion Recapitulation Prognostication 5 References Outline 1 Stereo Matching Generic Stereo Matching Belief Propagation 2 Concurrent ML Overview Investigation of Alternatives Comparative Benchmarks 3 Concurrent ML and Belief Propagation 4 Conclusion Recapitulation Prognostication 5 References Outline 1 Stereo Matching Generic Stereo Matching Belief Propagation 2 Concurrent ML Overview Investigation of Alternatives Comparative Benchmarks 3 Concurrent ML and Belief Propagation 4 Conclusion Recapitulation Prognostication 5 References Stereo Matching Generally SM is finding correspondences between stereo images Images are of the same scene Captured simultaneously Correspondences (`disparity') are used to estimate depth SM is an ill-posed problem { can only make best guess Impossible to perform `perfectly' in general case Stereo Matching ExampleI (a) Left camera's image (b) Right camera's image Figure 1: The popular 'Tsukuba' example stereo matching images, so called because they were created by researchers at the University of Tsukuba, Japan. They are probably the most widely-used benchmark images in stereo matching. Stereo Matching ExampleII (a) Ground truth disparity map (b) Disparity map generated using a simple Belief Propagation Stereo Matching implementation Figure 2: The ground truth disparity map for the Tsukuba images, and an example of a possible real disparity map produced by using Belief Propagation Stereo Matching. The ground truth represents what would be expected if stereo matching could be carried out `perfectly'. -
Tcl and Java Performance
Tcl and Java Performance http://ptolemy.eecs.berkeley.edu/~cxh/java/tclblend/scriptperf/scriptperf.html Tcl and Java Performance by H. John Reekie, University of California at Berkeley Christopher Hylands, University of California at Berkeley Edward A. Lee, University of California at Berkeley Abstract Combining scripting languages such as Tcl with lower−level programming languages such as Java offers new opportunities for flexible and rapid software development. In this paper, we benchmark various combinations of Tcl and Java against the two languages alone. We also provide some comparisons with JavaScript. Performance can vary by well over two orders of magnitude. We also uncovered some interesting threading issues that affect performance on the Solaris platform. "There are lies, damn lies and statistics" This paper is a work in progress, we used the information here to give our group some generalizations on the performance tradeoffs between various scripting languages. Updating the timing results to include JDK1.2 with a Just In Time (JIT) compiler would be useful. Introduction There is a growing trend towards integration of multiple languages through scripting. In a famously controversial white paper (Ousterhout 97), John Ousterhout, now of Scriptics Corporation, argues that scripting −− the use of a high−level, untyped, interpreted language to "glue" together components written in a lower−level language −− provides greater reuse benefits that other reuse technologies. Although traditionally a language such as C or C++ has been the lower−level language, more recent efforts have focused on using Java. Recently, Sun Microsystems laboratories announced two products aimed at fulfilling this goal with the Tcl and Java programming languages. -
Real-Time Operating System in Java
University of Wollongong Theses Collection University of Wollongong Theses Collection University of Wollongong Year Real-time operating system in Java Qinghua Lu University of Wollongong Lu, Qinghua, Real-time operating system in Java, MA thesis, School of Computer Science and Software Engineering, University of Wollongong, 2007. http://ro.uow.edu/theses/29 This paper is posted at Research Online. http://ro.uow.edu.au/theses/29 Real-time Operating System in Java A thesis submitted in fulfilment of the requirements for the award of the degree Master of Computer Science -Research from UNIVERSITY OF WOLLONGONG by Qinghua Lu School of Computer Science & Software Engineering August, 2007 1 Dedicated to My Parents, Lu Changyou and Luo Xiue! 2 The following papers were written as part of this research. 1. McKerrow, P.J., Lu, Q., Zhou, Z.Q. and Chen, L. (2007), Developing real-time systems in Java on Macintosh, Submitted to AUC’07, Apple University Consortium, Gold Coast, September, 23-26, 2007. 2. McKerrow, P.J., Lu, Q., Zhou, Z.Q. and Chen, L. (2007), Software development of embedded systems on Macintosh, Submitted to AUC’07, Apple University Consortium, Gold Coast, September, 23-26, 2007. 3 Declaration I, Qinghua Lu, declare that this thesis, submitted in fulfilment of the requirements for the award of Master of Computer Science -Research, in the School of Computer Science & Software Engineering, University of Wollongong, is wholly my own work unless otherwise referenced or acknowledged. The document has not been submitted for qualifications -
Alpha ELT Listing
Lienholder Name Lienholder Address City State Zip ELT ID 1ST ADVANTAGE FCU PO BX 2116 NEWPORT NEWS VA 23609 CFW 1ST COMMAND BK PO BX 901041 FORT WORTH TX 76101 FXQ 1ST FNCL BK USA 47 SHERMAN HILL RD WOODBURY CT 06798 GVY 1ST LIBERTY FCU PO BX 5002 GREAT FALLS MT 59403 ESY 1ST NORTHERN CA CU 1111 PINE ST MARTINEZ CA 94553 EUZ 1ST NORTHERN CR U 230 W MONROE ST STE 2850 CHICAGO IL 60606 GVK 1ST RESOURCE CU 47 W OXMOOR RD BIRMINGHAM AL 35209 DYW 1ST SECURITY BK WA PO BX 97000 LYNNWOOD WA 98046 FTK 1ST UNITED SVCS CU 5901 GIBRALTAR DR PLEASANTON CA 94588 W95 1ST VALLEY CU 401 W SECOND ST SN BERNRDNO CA 92401 K31 360 EQUIP FIN LLC 300 BEARDSLEY LN STE D201 AUSTIN TX 78746 DJH 360 FCU PO BX 273 WINDSOR LOCKS CT 06096 DBG 4FRONT CU PO BX 795 TRAVERSE CITY MI 49685 FBU 777 EQUIPMENT FIN LLC 600 BRICKELL AVE FL 19 MIAMI FL 33131 FYD A C AUTOPAY PO BX 40409 DENVER CO 80204 CWX A L FNCL CORP PO BX 11907 SANTA ANA CA 92711 J68 A L FNCL CORP PO BX 51466 ONTARIO CA 91761 J90 A L FNCL CORP PO BX 255128 SACRAMENTO CA 95865 J93 A L FNCL CORP PO BX 28248 FRESNO CA 93729 J95 A PLUS FCU PO BX 14867 AUSTIN TX 78761 AYV A PLUS LOANS 500 3RD ST W SACRAMENTO CA 95605 GCC A/M FNCL PO BX 1474 CLOVIS CA 93613 A94 AAA FCU PO BX 3788 SOUTH BEND IN 46619 CSM AAC CU 177 WILSON AVE NW GRAND RAPIDS MI 49534 GET AAFCU PO BX 619001 MD2100 DFW AIRPORT TX 75261 A90 ABLE INC 503 COLORADO ST AUSTIN TX 78701 CVD ABNB FCU 830 GREENBRIER CIR CHESAPEAKE VA 23320 CXE ABOUND FCU PO BX 900 RADCLIFF KY 40159 GKB ACADEMY BANK NA PO BX 26458 KANSAS CITY MO 64196 ATF ACCENTRA CU 400 4TH -
Apache Harmony Project Tim Ellison Geir Magnusson Jr
The Apache Harmony Project Tim Ellison Geir Magnusson Jr. Apache Harmony Project http://harmony.apache.org TS-7820 2007 JavaOneSM Conference | Session TS-7820 | Goal of This Talk In the next 45 minutes you will... Learn about the motivations, current status, and future plans of the Apache Harmony project 2007 JavaOneSM Conference | Session TS-7820 | 2 Agenda Project History Development Model Modularity VM Interface How Are We Doing? Relevance in the Age of OpenJDK Summary 2007 JavaOneSM Conference | Session TS-7820 | 3 Agenda Project History Development Model Modularity VM Interface How Are We Doing? Relevance in the Age of OpenJDK Summary 2007 JavaOneSM Conference | Session TS-7820 | 4 Apache Harmony In the Beginning May 2005—founded in the Apache Incubator Primary Goals 1. Compatible, independent implementation of Java™ Platform, Standard Edition (Java SE platform) under the Apache License 2. Community-developed, modular architecture allowing sharing and independent innovation 3. Protect IP rights of ecosystem 2007 JavaOneSM Conference | Session TS-7820 | 5 Apache Harmony Early history: 2005 Broad community discussion • Technical issues • Legal and IP issues • Project governance issues Goal: Consolidation and Consensus 2007 JavaOneSM Conference | Session TS-7820 | 6 Early History Early history: 2005/2006 Initial Code Contributions • Three Virtual machines ● JCHEVM, BootVM, DRLVM • Class Libraries ● Core classes, VM interface, test cases ● Security, beans, regex, Swing, AWT ● RMI and math 2007 JavaOneSM Conference | Session TS-7820 | -
The Synthesis Kernel
... " The Synthesis Kernel Calton Pu, Henry Massalin and 21 ~; John loannidis Columbia University ABSTRACT: The Synthesis distributed operating system combines etticient kernel calls with a high level, orthogonal interface. The key concept is the use of a code synthesizer in the kernel to generate specialized (thus short and fast) kernel routines for specific situations. We have three methods of synthesizing code: Factoring Invariants to bypass redundant computations; Collapsing Layers to eliminate unnecessary procedure calls and context switches; and Executable Data Structures to shorten data structure traversal time. Applying these methods, the kernel call synthesized to read Idevlmem takes about 15 microseconds on a 68020 machine. A simple model of computation called a synthetic machine supports parallel and distributed processing. The interface to synthetic machine consists of six operations on four kinds of ohjects. This combination of a high-level interface with the code synthesizer avoids the traditional trade-off in operating systems between powerful interfaces and efficient implementations . ., Complliing .\'yslt'nIS, Vol. 1 • No.1' Winter IIJI!I! I I I. Introduction and data, and synthetic 1/0 units to move data in to and out of the synthetic machine. The synthetic machine interface and kernel code synthesizer A trade-off between powerful features and efficient arc independent ideas that have a synergistic eliect. Without the implementation exists in many operating systems. Systems with code synthesizer. even a sophisticated implementation of synthetic high-level interfaces and powerful features, like Argus lO and Eden/ machines would be very inetlicient. Each high-level kernel call require a lot of code for their implementation, and this added would require a large amount of code with a long execution time. -
Empirical Comparison of Scons and GNU Make
Großer Beleg Empirical Comparison of SCons and GNU Make Ludwig Hähne August 21, 2008 Technical University Dresden Department of Computer Science Institute for System Architecture Chair for Operating Systems Professor: Prof. Dr. rer. nat. Hermann Härtig Tutor: Dipl.-Inf. Norman Feske Dipl.-Inf. Christian Helmuth Erklärung Hiermit erkläre ich, dass ich diese Arbeit selbstständig erstellt und keine anderen als die angegebenen Hilfsmittel benutzt habe. Dresden, den 26. Juni 2008 Ludwig Hähne Abstract Build systems are an integral part of every software developer’s tool kit. Next to the well-known Make build system, numerous alternative solutions emerged during the last decade. Even though the new systems introduced superior concepts like content signa- tures and promise to provide better build accuracy, Make is still the de facto standard. This paper examines GNU Make and SCons as representatives of two conceptually distinct approaches to conduct software builds. General build-system concepts and their respective realizations are discussed. The performance and scalability are empirically evaluated by confronting the two competitors with comparable real and synthetic build tasks. V Contents 1 Introduction 1 2 Background 3 2.1 Design Goals . .3 2.1.1 Convenience . .3 2.1.2 Correctness . .3 2.1.3 Performance . .3 2.1.4 Scalability . .4 2.2 Software Rebuilding . .4 2.2.1 Dependency analysis . .4 2.2.1.1 File signatures . .4 2.2.1.2 Fine grained dependencies . .5 2.2.1.3 Dependency declaration . .5 2.2.1.4 Dependency types . .5 2.2.2 Build infrastructure . .6 2.2.3 Command scheduling . .6 2.3 Build System Features . -
Thread Scheduling in Multi-Core Operating Systems Redha Gouicem
Thread Scheduling in Multi-core Operating Systems Redha Gouicem To cite this version: Redha Gouicem. Thread Scheduling in Multi-core Operating Systems. Computer Science [cs]. Sor- bonne Université, 2020. English. tel-02977242 HAL Id: tel-02977242 https://hal.archives-ouvertes.fr/tel-02977242 Submitted on 24 Oct 2020 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. Ph.D thesis in Computer Science Thread Scheduling in Multi-core Operating Systems How to Understand, Improve and Fix your Scheduler Redha GOUICEM Sorbonne Université Laboratoire d’Informatique de Paris 6 Inria Whisper Team PH.D.DEFENSE: 23 October 2020, Paris, France JURYMEMBERS: Mr. Pascal Felber, Full Professor, Université de Neuchâtel Reviewer Mr. Vivien Quéma, Full Professor, Grenoble INP (ENSIMAG) Reviewer Mr. Rachid Guerraoui, Full Professor, École Polytechnique Fédérale de Lausanne Examiner Ms. Karine Heydemann, Associate Professor, Sorbonne Université Examiner Mr. Etienne Rivière, Full Professor, University of Louvain Examiner Mr. Gilles Muller, Senior Research Scientist, Inria Advisor Mr. Julien Sopena, Associate Professor, Sorbonne Université Advisor ABSTRACT In this thesis, we address the problem of schedulers for multi-core architectures from several perspectives: design (simplicity and correct- ness), performance improvement and the development of application- specific schedulers. -
Solution Brief JVM Performance Monitoring
Solution Brief Monitoring the Health & Performance of Java Virtual Machines (JVM) Total Performance Visibility JVM: The Cornerstone of Java Enterprise Applications The Java Virtual Machine (JVM) forms the core of the Java application architecture. It plays the crucial role of interpreting and translating Java byte code into operations on the host platform. Since Java middleware (application servers such as Tomcat, JBoss EAP, JBoss AS, WildFly, WebSphere, and WebLogic) runs on the JVM, a performance issue at the JVM level has a major impact on business services supported by it. Monitoring of the JVM must be an integral part of any Java application performance monitoring strategy. IT Ops and DevOps teams use JVM Key Benefits performance metrics to troubleshoot server-side bottlenecks. Developers and architects can also benefit from JVM monitoring by uncovering code- Proactively detect and resolve level issues. Java application problems to ensure high service uptime and business continuity Key JVM Performance Questions to Answer Troubleshoot faster: Real-time • Is there any runaway thread hogging the CPU? Which line of code is it alerts pinpoint the exact line executing, in which class, and which method? of code that is impacting Java • Is the JVM heap and non-heap memory sized correctly? applications • Are there any out-of-memory-exceptions or memory leaks? In-depth analytics enable architects to optimize Java • When does garbage collection happen, and how much memory is applications to scale and support freed up each time? additional users • Are there any thread deadlocks happening that are causing Single pane of glass to monitor application processing to be hung? everything Java—from application server, JVM and database, all the way down to server and storage End-to-End JVM Monitoring with eG Enterprise infrastructure eG Enterprise is a comprehensive performance monitoring, diagnosis and reporting solution for Java application infrastructures. -
A Study of Cache Performance in Java Virtual Machines, the University of Texas at Austin, 2002
Cache Performance in Java Virtual Machines: A Study of Constituent Phases Anand S. Rajan, Juan Rubio and Lizy K. John Laboratory for Computer Architecture Department of Electrical & Computer Engineering The University of Texas at Austin (arajan, jrubio,[email protected]) Abstract This paper studies the cache performance of Java programs in the interpreted and just-in- time (JIT) modes by analyzing memory reference traces of the SPECjvm98 applications with the Latte JVM. Specifically, we study the cache performance of the different components of the JVM (class loader, the execution engine and the garbage collector). Poor data cache performance in JITs is caused by code installation, and the data write miss rate can be as high as 70% in the execution engine. In addition, this code installation also causes deterioration in performance of the instruction cache during execution of translated code. We also believe that there is considerable interference between data accesses of the garbage collector and that of the compiler-translator execution engine of the JIT mode. We observe that the miss percentages in the garbage collection phase are of the order of 60% for the JIT mode; we believe that interference between data accesses of the garbage collector and the JIT execution engine lead to further deterioration of the data cache performance wherever the contribution of the garbage collector is significant. We observe that an increase in cache sizes does not substantially improve performance in the case of data cache writes, which is identified to be the principal performance bottleneck in JITs. 1 1. Introduction Java[1] is a widely used programming language due to the machine independent nature of bytecodes.