Cilk: an Efficient Multithreaded Runtime System
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Other Apis What’S Wrong with Openmp?
Threaded Programming Other APIs What’s wrong with OpenMP? • OpenMP is designed for programs where you want a fixed number of threads, and you always want the threads to be consuming CPU cycles. – cannot arbitrarily start/stop threads – cannot put threads to sleep and wake them up later • OpenMP is good for programs where each thread is doing (more-or-less) the same thing. • Although OpenMP supports C++, it’s not especially OO friendly – though it is gradually getting better. • OpenMP doesn’t support other popular base languages – e.g. Java, Python What’s wrong with OpenMP? (cont.) Can do this Can do this Can’t do this Threaded programming APIs • Essential features – a way to create threads – a way to wait for a thread to finish its work – a mechanism to support thread private data – some basic synchronisation methods – at least a mutex lock, or atomic operations • Optional features – support for tasks – more synchronisation methods – e.g. condition variables, barriers,... – higher levels of abstraction – e.g. parallel loops, reductions What are the alternatives? • POSIX threads • C++ threads • Intel TBB • Cilk • OpenCL • Java (not an exhaustive list!) POSIX threads • POSIX threads (or Pthreads) is a standard library for shared memory programming without directives. – Part of the ANSI/IEEE 1003.1 standard (1996) • Interface is a C library – no standard Fortran interface – can be used with C++, but not OO friendly • Widely available – even for Windows – typically installed as part of OS – code is pretty portable • Lots of low-level control over behaviour of threads • Lacks a proper memory consistency model Thread forking #include <pthread.h> int pthread_create( pthread_t *thread, const pthread_attr_t *attr, void*(*start_routine, void*), void *arg) • Creates a new thread: – first argument returns a pointer to a thread descriptor. -
Glibc and System Calls Documentation Release 1.0
Glibc and System Calls Documentation Release 1.0 Rishi Agrawal <[email protected]> Dec 28, 2017 Contents 1 Introduction 1 1.1 Acknowledgements...........................................1 2 Basics of a Linux System 3 2.1 Introduction...............................................3 2.2 Programs and Compilation........................................3 2.3 Libraries.................................................7 2.4 System Calls...............................................7 2.5 Kernel.................................................. 10 2.6 Conclusion................................................ 10 2.7 References................................................ 11 3 Working with glibc 13 3.1 Introduction............................................... 13 3.2 Why this chapter............................................. 13 3.3 What is glibc .............................................. 13 3.4 Download and extract glibc ...................................... 14 3.5 Walkthrough glibc ........................................... 14 3.6 Reading some functions of glibc ................................... 17 3.7 Compiling and installing glibc .................................... 18 3.8 Using new glibc ............................................ 21 3.9 Conclusion................................................ 23 4 System Calls On x86_64 from User Space 25 4.1 Setting Up Arguements......................................... 25 4.2 Calling the System Call......................................... 27 4.3 Retrieving the Return Value...................................... -
Preview Objective-C Tutorial (PDF Version)
Objective-C Objective-C About the Tutorial Objective-C is a general-purpose, object-oriented programming language that adds Smalltalk-style messaging to the C programming language. This is the main programming language used by Apple for the OS X and iOS operating systems and their respective APIs, Cocoa and Cocoa Touch. This reference will take you through simple and practical approach while learning Objective-C Programming language. Audience This reference has been prepared for the beginners to help them understand basic to advanced concepts related to Objective-C Programming languages. Prerequisites Before you start doing practice with various types of examples given in this reference, I'm making an assumption that you are already aware about what is a computer program, and what is a computer programming language? Copyright & Disclaimer © Copyright 2015 by Tutorials Point (I) Pvt. Ltd. All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book can retain a copy for future reference but commercial use of this data is not allowed. Distribution or republishing any content or a part of the content of this e-book in any manner is also not allowed without written consent of the publisher. We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. Tutorials Point (I) Pvt. Ltd. provides no guarantee regarding the accuracy, timeliness or completeness of our website or its contents including this tutorial. If you discover any errors on our website or in this tutorial, please notify us at [email protected] ii Objective-C Table of Contents About the Tutorial .................................................................................................................................. -
Distributed & Cloud Computing: Lecture 1
Distributed & cloud computing: Lecture 1 Chokchai “Box” Leangsuksun SWECO Endowed Professor, Computer Science Louisiana Tech University [email protected] CTO, PB Tech International Inc. [email protected] Class Info ■" Class Hours: noon-1:50 pm T-Th ! ■" Dr. Box’s & Contact Info: ●"www.latech.edu/~box ●"Phone 318-257-3291 ●"Email: [email protected] Class Info ■" Main Text: ! ●" 1) Distributed Systems: Concepts and Design By Coulouris, Dollimore, Kindberg and Blair Edition 5, © Addison-Wesley 2012 ●" 2) related publications & online literatures Class Info ■" Objective: ! ●"the theory, design and implementation of distributed systems ●"Discussion on abstractions, Concepts and current systems/applications ●"Best practice on Research on DS & cloud computing Course Contents ! •" The Characterization of distributed computing and cloud computing. •" System Models. •" Networking and inter-process communication. •" OS supports and Virtualization. •" RAS, Performance & Reliability Modeling. Security. !! Course Contents •" Introduction to Cloud Computing ! •" Various models and applications. •" Deployment models •" Service models (SaaS, PaaS, IaaS, Xaas) •" Public Cloud: Amazon AWS •" Private Cloud: openStack. •" Cost models ( between cloud vs host your own) ! •" ! !!! Course Contents case studies! ! •" Introduction to HPC! •" Multicore & openMP! •" Manycore, GPGPU & CUDA! •" Cloud-based EKG system! •" Distributed Object & Web Services (if time allows)! •" Distributed File system (if time allows)! •" Replication & Disaster Recovery Preparedness! !!!!!! Important Dates ! ■" Apr 10: Mid Term exam ■" Apr 22: term paper & presentation due ■" May 15: Final exam Evaluations ! ■" 20% Lab Exercises/Quizzes ■" 20% Programming Assignments ■" 10% term paper ■" 20% Midterm Exam ■" 30% Final Exam Intro to Distributed Computing •" Distributed System Definitions. •" Distributed Systems Examples: –" The Internet. –" Intranets. –" Mobile Computing –" Cloud Computing •" Resource Sharing. •" The World Wide Web. •" Distributed Systems Challenges. Based on Mostafa’s lecture 10 Distributed Systems 0. -
Distributed Algorithms with Theoretic Scalability Analysis of Radial and Looped Load flows for Power Distribution Systems
Electric Power Systems Research 65 (2003) 169Á/177 www.elsevier.com/locate/epsr Distributed algorithms with theoretic scalability analysis of radial and looped load flows for power distribution systems Fangxing Li *, Robert P. Broadwater ECE Department Virginia Tech, Blacksburg, VA 24060, USA Received 15 April 2002; received in revised form 14 December 2002; accepted 16 December 2002 Abstract This paper presents distributed algorithms for both radial and looped load flows for unbalanced, multi-phase power distribution systems. The distributed algorithms are developed from tree-based sequential algorithms. Formulas of scalability for the distributed algorithms are presented. It is shown that computation time dominates communication time in the distributed computing model. This provides benefits to real-time load flow calculations, network reconfigurations, and optimization studies that rely on load flow calculations. Also, test results match the predictions of derived formulas. This shows the formulas can be used to predict the computation time when additional processors are involved. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Distributed computing; Scalability analysis; Radial load flow; Looped load flow; Power distribution systems 1. Introduction Also, the method presented in Ref. [10] was tested in radial distribution systems with no more than 528 buses. Parallel and distributed computing has been applied More recent works [11Á/14] presented distributed to many scientific and engineering computations such as implementations for power flows or power flow based weather forecasting and nuclear simulations [1,2]. It also algorithms like optimizations and contingency analysis. has been applied to power system analysis calculations These works also targeted power transmission systems. [3Á/14]. -
Concurrent Cilk: Lazy Promotion from Tasks to Threads in C/C++
Concurrent Cilk: Lazy Promotion from Tasks to Threads in C/C++ Christopher S. Zakian, Timothy A. K. Zakian Abhishek Kulkarni, Buddhika Chamith, and Ryan R. Newton Indiana University - Bloomington, fczakian, tzakian, adkulkar, budkahaw, [email protected] Abstract. Library and language support for scheduling non-blocking tasks has greatly improved, as have lightweight (user) threading packages. How- ever, there is a significant gap between the two developments. In previous work|and in today's software packages|lightweight thread creation incurs much larger overheads than tasking libraries, even on tasks that end up never blocking. This limitation can be removed. To that end, we describe an extension to the Intel Cilk Plus runtime system, Concurrent Cilk, where tasks are lazily promoted to threads. Concurrent Cilk removes the overhead of thread creation on threads which end up calling no blocking operations, and is the first system to do so for C/C++ with legacy support (standard calling conventions and stack representations). We demonstrate that Concurrent Cilk adds negligible overhead to existing Cilk programs, while its promoted threads remain more efficient than OS threads in terms of context-switch overhead and blocking communication. Further, it enables development of blocking data structures that create non-fork-join dependence graphs|which can expose more parallelism, and better supports data-driven computations waiting on results from remote devices. 1 Introduction Both task-parallelism [1, 11, 13, 15] and lightweight threading [20] libraries have become popular for different kinds of applications. The key difference between a task and a thread is that threads may block|for example when performing IO|and then resume again. -
The Glib/GTK+ Development Platform
The GLib/GTK+ Development Platform A Getting Started Guide Version 0.8 Sébastien Wilmet March 29, 2019 Contents 1 Introduction 3 1.1 License . 3 1.2 Financial Support . 3 1.3 Todo List for this Book and a Quick 2019 Update . 4 1.4 What is GLib and GTK+? . 4 1.5 The GNOME Desktop . 5 1.6 Prerequisites . 6 1.7 Why and When Using the C Language? . 7 1.7.1 Separate the Backend from the Frontend . 7 1.7.2 Other Aspects to Keep in Mind . 8 1.8 Learning Path . 9 1.9 The Development Environment . 10 1.10 Acknowledgments . 10 I GLib, the Core Library 11 2 GLib, the Core Library 12 2.1 Basics . 13 2.1.1 Type Definitions . 13 2.1.2 Frequently Used Macros . 13 2.1.3 Debugging Macros . 14 2.1.4 Memory . 16 2.1.5 String Handling . 18 2.2 Data Structures . 20 2.2.1 Lists . 20 2.2.2 Trees . 24 2.2.3 Hash Tables . 29 2.3 The Main Event Loop . 31 2.4 Other Features . 33 II Object-Oriented Programming in C 35 3 Semi-Object-Oriented Programming in C 37 3.1 Header Example . 37 3.1.1 Project Namespace . 37 3.1.2 Class Namespace . 39 3.1.3 Lowercase, Uppercase or CamelCase? . 39 3.1.4 Include Guard . 39 3.1.5 C++ Support . 39 1 3.1.6 #include . 39 3.1.7 Type Definition . 40 3.1.8 Object Constructor . 40 3.1.9 Object Destructor . -
Grid Computing: What Is It, and Why Do I Care?*
Grid Computing: What Is It, and Why Do I Care?* Ken MacInnis <[email protected]> * Or, “Mi caja es su caja!” (c) Ken MacInnis 2004 1 Outline Introduction and Motivation Examples Architecture, Components, Tools Lessons Learned and The Future Questions? (c) Ken MacInnis 2004 2 What is “grid computing”? Many different definitions: Utility computing Cycles for sale Distributed computing distributed.net RC5, SETI@Home High-performance resource sharing Clusters, storage, visualization, networking “We will probably see the spread of ‘computer utilities’, which, like present electric and telephone utilities, will service individual homes and offices across the country.” Len Kleinrock (1969) The word “grid” doesn’t equal Grid Computing: Sun Grid Engine is a mere scheduler! (c) Ken MacInnis 2004 3 Better definitions: Common protocols allowing large problems to be solved in a distributed multi-resource multi-user environment. “A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities.” Kesselman & Foster (1998) “…coordinated resource sharing and problem solving in dynamic, multi- institutional virtual organizations.” Kesselman, Foster, Tuecke (2000) (c) Ken MacInnis 2004 4 New Challenges for Computing Grid computing evolved out of a need to share resources Flexible, ever-changing “virtual organizations” High-energy physics, astronomy, more Differing site policies with common needs Disparate computing needs -
Parallelism in Cilk Plus
Cilk Plus: Language Support for Thread and Vector Parallelism Arch D. Robison Intel Sr. Principal Engineer Outline Motivation for Intel® Cilk Plus SIMD notations Fork-Join notations Karatsuba multiplication example GCC branch Copyright© 2012, Intel Corporation. All rights reserved. 2 *Other brands and names are the property of their respective owners. Multi-Threading and Vectorization are Essential to Performance Latest Intel® Xeon® chip: 8 cores 2 independent threads per core 8-lane (single prec.) vector unit per thread = 128-fold potential for single socket Intel® Many Integrated Core Architecture >50 cores (KNC) ? independent threads per core 16-lane (single prec.) vector unit per thread = parallel heaven Copyright© 2012, Intel Corporation. All rights reserved. 3 *Other brands and names are the property of their respective owners. Importance of Abstraction Software outlives hardware. Recompiling is easier than rewriting. Coding too closely to hardware du jour makes moving to new hardware expensive. C++ philosophy: abstraction with minimal penalty Do not expect compiler to be clever. But let it do tedious bookkeeping. Copyright© 2012, Intel Corporation. All rights reserved. 4 *Other brands and names are the property of their respective owners. “Three Layer Cake” Abstraction Message Passing exploit multiple nodes Fork-Join exploit multiple cores exploit parallelism at multiple algorithmic levels SIMD exploit vector hardware Copyright© 2012, Intel Corporation. All rights reserved. 5 *Other brands and names are the property of their respective owners. Composition Message Driven compose via send/receive Fork-Join compose via call/return SIMD compose sequentially Copyright© 2012, Intel Corporation. All rights reserved. 6 *Other brands and names are the property of their respective owners. -
Computing at Massive Scale: Scalability and Dependability Challenges
This is a repository copy of Computing at massive scale: Scalability and dependability challenges. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/105671/ Version: Accepted Version Proceedings Paper: Yang, R and Xu, J orcid.org/0000-0002-4598-167X (2016) Computing at massive scale: Scalability and dependability challenges. In: Proceedings - 2016 IEEE Symposium on Service-Oriented System Engineering, SOSE 2016. 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE), 29 Mar - 02 Apr 2016, Oxford, United Kingdom. IEEE , pp. 386-397. ISBN 9781509022533 https://doi.org/10.1109/SOSE.2016.73 (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. -
A CPU/GPU Task-Parallel Runtime with Explicit Epoch Synchronization
TREES: A CPU/GPU Task-Parallel Runtime with Explicit Epoch Synchronization Blake A. Hechtman, Andrew D. Hilton, and Daniel J. Sorin Department of Electrical and Computer Engineering Duke University Abstract —We have developed a task-parallel runtime targeting CPUs are a poor fit for GPUs. To understand system, called TREES, that is designed for high why this mismatch exists, we must first understand the performance on CPU/GPU platforms. On platforms performance of an idealized task-parallel application with multiple CPUs, Cilk’s “work-first” principle (with no runtime) and then how the runtime’s overhead underlies how task-parallel applications can achieve affects it. The performance of a task-parallel application performance, but work-first is a poor fit for GPUs. We is a function of two characteristics: its total amount of build upon work-first to create the “work-together” work to be performed (T1, the time to execute on 1 principle that addresses the specific strengths and processor) and its critical path (T∞, the time to execute weaknesses of GPUs. The work-together principle on an infinite number of processors). Prior work has extends work-first by stating that (a) the overhead on shown that the runtime of a system with P processors, the critical path should be paid by the entire system at TP, is bounded by = ( ) + ( ) due to the once and (b) work overheads should be paid co- greedy o ff line scheduler bound [3][10]. operatively. We have implemented the TREES runtime A task-parallel runtime introduces overheads and, for in OpenCL, and we experimentally evaluate TREES purposes of performance analysis, we distinguish applications on a CPU/GPU platform. -
An Analysis of the D Programming Language Sumanth Yenduri University of Mississippi- Long Beach
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CSUSB ScholarWorks Journal of International Technology and Information Management Volume 16 | Issue 3 Article 7 2007 An Analysis of the D Programming Language Sumanth Yenduri University of Mississippi- Long Beach Louise Perkins University of Southern Mississippi- Long Beach Md. Sarder University of Southern Mississippi- Long Beach Follow this and additional works at: http://scholarworks.lib.csusb.edu/jitim Part of the Business Intelligence Commons, E-Commerce Commons, Management Information Systems Commons, Management Sciences and Quantitative Methods Commons, Operational Research Commons, and the Technology and Innovation Commons Recommended Citation Yenduri, Sumanth; Perkins, Louise; and Sarder, Md. (2007) "An Analysis of the D Programming Language," Journal of International Technology and Information Management: Vol. 16: Iss. 3, Article 7. Available at: http://scholarworks.lib.csusb.edu/jitim/vol16/iss3/7 This Article is brought to you for free and open access by CSUSB ScholarWorks. It has been accepted for inclusion in Journal of International Technology and Information Management by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [email protected]. Analysis of Programming Language D Journal of International Technology and Information Management An Analysis of the D Programming Language Sumanth Yenduri Louise Perkins Md. Sarder University of Southern Mississippi - Long Beach ABSTRACT The C language and its derivatives have been some of the dominant higher-level languages used, and the maturity has stemmed several newer languages that, while still relatively young, possess the strength of decades of trials and experimentation with programming concepts.