A Survey of Processors with Explicit Multithreading
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Chapter 1: Introduction What Is an Operating System?
Chapter 1: Introduction What is an Operating System? Mainframe Systems Desktop Systems Multiprocessor Systems Distributed Systems Clustered System Real -Time Systems Handheld Systems Computing Environments Operating System Concepts 1.1 Silberschatz, Galvin and Gagne 2002 What is an Operating System? A program that acts as an intermediary between a user of a computer and the computer hardware. Operating system goals: ) Execute user programs and make solving user problems easier. ) Make the computer system convenient to use. Use the computer hardware in an efficient manner. Operating System Concepts 1.2 Silberschatz, Galvin and Gagne 2002 1 Computer System Components 1. Hardware – provides basic computing resources (CPU, memory, I/O devices). 2. Operating system – controls and coordinates the use of the hardware among the various application programs for the various users. 3. Applications programs – define the ways in which the system resources are used to solve the computing problems of the users (compilers, database systems, video games, business programs). 4. Users (people, machines, other computers). Operating System Concepts 1.3 Silberschatz, Galvin and Gagne 2002 Abstract View of System Components Operating System Concepts 1.4 Silberschatz, Galvin and Gagne 2002 2 Operating System Definitions Resource allocator – manages and allocates resources. Control program – controls the execution of user programs and operations of I/O devices . Kernel – the one program running at all times (all else being application programs). -
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]. -
Three-Dimensional Integrated Circuit Design: EDA, Design And
Integrated Circuits and Systems Series Editor Anantha Chandrakasan, Massachusetts Institute of Technology Cambridge, Massachusetts For other titles published in this series, go to http://www.springer.com/series/7236 Yuan Xie · Jason Cong · Sachin Sapatnekar Editors Three-Dimensional Integrated Circuit Design EDA, Design and Microarchitectures 123 Editors Yuan Xie Jason Cong Department of Computer Science and Department of Computer Science Engineering University of California, Los Angeles Pennsylvania State University [email protected] [email protected] Sachin Sapatnekar Department of Electrical and Computer Engineering University of Minnesota [email protected] ISBN 978-1-4419-0783-7 e-ISBN 978-1-4419-0784-4 DOI 10.1007/978-1-4419-0784-4 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2009939282 © Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Foreword We live in a time of great change. -
Parallel Programming
Parallel Programming Parallel Programming Parallel Computing Hardware Shared memory: multiple cpus are attached to the BUS all processors share the same primary memory the same memory address on different CPU’s refer to the same memory location CPU-to-memory connection becomes a bottleneck: shared memory computers cannot scale very well Parallel Programming Parallel Computing Hardware Distributed memory: each processor has its own private memory computational tasks can only operate on local data infinite available memory through adding nodes requires more difficult programming Parallel Programming OpenMP versus MPI OpenMP (Open Multi-Processing): easy to use; loop-level parallelism non-loop-level parallelism is more difficult limited to shared memory computers cannot handle very large problems MPI(Message Passing Interface): require low-level programming; more difficult programming scalable cost/size can handle very large problems Parallel Programming MPI Distributed memory: Each processor can access only the instructions/data stored in its own memory. The machine has an interconnection network that supports passing messages between processors. A user specifies a number of concurrent processes when program begins. Every process executes the same program, though theflow of execution may depend on the processors unique ID number (e.g. “if (my id == 0) then ”). ··· Each process performs computations on its local variables, then communicates with other processes (repeat), to eventually achieve the computed result. In this model, processors pass messages both to send/receive information, and to synchronize with one another. Parallel Programming Introduction to MPI Communicators and Groups: MPI uses objects called communicators and groups to define which collection of processes may communicate with each other. -
Real-Time Performance During CUDA™ a Demonstration and Analysis of Redhawk™ CUDA RT Optimizations
A Concurrent Real-Time White Paper 2881 Gateway Drive Pompano Beach, FL 33069 (954) 974-1700 www.concurrent-rt.com Real-Time Performance During CUDA™ A Demonstration and Analysis of RedHawk™ CUDA RT Optimizations By: Concurrent Real-Time Linux® Development Team November 2010 Overview There are many challenges to creating a real-time Linux distribution that provides guaranteed low process-dispatch latencies and minimal process run-time jitter. Concurrent Real Time’s RedHawk Linux distribution meets and exceeds these challenges, providing a hard real-time environment on many qualified hardware configurations, even in the presence of a heavy system load. However, there are additional challenges faced when guaranteeing real-time performance of processes while CUDA applications are simultaneously running on the system. The proprietary CUDA driver supplied by NVIDIA® frequently makes demands upon kernel resources that can dramatically impact real-time performance. This paper discusses a demonstration application developed by Concurrent to illustrate that RedHawk Linux kernel optimizations allow hard real-time performance guarantees to be preserved even while demanding CUDA applications are running. The test results will show how RedHawk performance compares to CentOS performance running the same application. The design and implementation details of the demonstration application are also discussed in this paper. Demonstration This demonstration features two selectable real-time test modes: 1. Jitter Mode: measure and graph the run-time jitter of a real-time process 2. PDL Mode: measure and graph the process-dispatch latency of a real-time process While the demonstration is running, it is possible to switch between these different modes at any time. -
18-741 Advanced Computer Architecture Lecture 1: Intro And
18-742 Fall 2012 Parallel Computer Architecture Lecture 10: Multithreading II Prof. Onur Mutlu Carnegie Mellon University 9/28/2012 Reminder: Review Assignments Due: Sunday, September 30, 11:59pm. Mutlu, “Some Ideas and Principles for Achieving Higher System Energy Efficiency,” NSF Position Paper and Presentation 2012. Ebrahimi et al., “Parallel Application Memory Scheduling,” MICRO 2011. Seshadri et al., “The Evicted-Address Filter: A Unified Mechanism to Address Both Cache Pollution and Thrashing,” PACT 2012. Pekhimenko et al., “Linearly Compressed Pages: A Main Memory Compression Framework with Low Complexity and Low Latency,” CMU SAFARI Technical Report 2012. 2 Feedback on Project Proposals In your email General feedback points Concrete mechanisms, even if not fully right, is a good place to start testing your ideas 3 Last Lecture Asymmetry in Memory Scheduling Wrap up Asymmetry Multithreading Fine-grained Coarse-grained 4 Today More Multithreading 5 More Multithreading 6 Readings: Multithreading Required Spracklen and Abraham, “Chip Multithreading: Opportunities and Challenges,” HPCA Industrial Session, 2005. Kalla et al., “IBM Power5 Chip: A Dual-Core Multithreaded Processor,” IEEE Micro 2004. Tullsen et al., “Exploiting choice: instruction fetch and issue on an implementable simultaneous multithreading processor,” ISCA 1996. Eyerman and Eeckhout, “A Memory-Level Parallelism Aware Fetch Policy for SMT Processors,” HPCA 2007. Recommended Hirata et al., “An Elementary Processor Architecture with Simultaneous Instruction Issuing from Multiple Threads,” ISCA 1992 Smith, “A pipelined, shared resource MIMD computer,” ICPP 1978. Gabor et al., “Fairness and Throughput in Switch on Event Multithreading,” MICRO 2006. Agarwal et al., “APRIL: A Processor Architecture for Multiprocessing,” ISCA 1990. 7 Review: Fine-grained vs. -
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 -
Unit: 4 Processes and Threads in Distributed Systems
Unit: 4 Processes and Threads in Distributed Systems Thread A program has one or more locus of execution. Each execution is called a thread of execution. In traditional operating systems, each process has an address space and a single thread of execution. It is the smallest unit of processing that can be scheduled by an operating system. A thread is a single sequence stream within in a process. Because threads have some of the properties of processes, they are sometimes called lightweight processes. In a process, threads allow multiple executions of streams. Thread Structure Process is used to group resources together and threads are the entities scheduled for execution on the CPU. The thread has a program counter that keeps track of which instruction to execute next. It has registers, which holds its current working variables. It has a stack, which contains the execution history, with one frame for each procedure called but not yet returned from. Although a thread must execute in some process, the thread and its process are different concepts and can be treated separately. What threads add to the process model is to allow multiple executions to take place in the same process environment, to a large degree independent of one another. Having multiple threads running in parallel in one process is similar to having multiple processes running in parallel in one computer. Figure: (a) Three processes each with one thread. (b) One process with three threads. In former case, the threads share an address space, open files, and other resources. In the latter case, process share physical memory, disks, printers and other resources. -
Declustering Spatial Databases on a Multi-Computer Architecture
Declustering spatial databases on a multi-computer architecture 1 2 ? 3 Nikos Koudas and Christos Faloutsos and Ibrahim Kamel 1 Computer Systems Research Institute University of Toronto 2 AT&T Bell Lab oratories Murray Hill, NJ 3 Matsushita Information Technology Lab oratory Abstract. We present a technique to decluster a spatial access metho d + on a shared-nothing multi-computer architecture [DGS 90]. We prop ose a software architecture with the R-tree as the underlying spatial access metho d, with its non-leaf levels on the `master-server' and its leaf no des distributed across the servers. The ma jor contribution of our work is the study of the optimal capacity of leaf no des, or `chunk size' (or `striping unit'): we express the resp onse time on range queries as a function of the `chunk size', and we show how to optimize it. We implemented our metho d on a network of workstations, using a real dataset, and we compared the exp erimental and the theoretical results. The conclusion is that our formula for the resp onse time is very accurate (the maximum relative error was 29%; the typical error was in the vicinity of 10-15%). We illustrate one of the p ossible ways to exploit such an accurate formula, by examining several `what-if ' scenarios. One ma jor, practical conclusion is that a chunk size of 1 page gives either optimal or close to optimal results, for a wide range of the parameters. Keywords: Parallel data bases, spatial access metho ds, shared nothing ar- chitecture. 1 Intro duction One of the requirements for the database management systems (DBMSs) of the future is the ability to handle spatial data. -
Chap01: Computer Abstractions and Technology
CHAPTER 1 Computer Abstractions and Technology 1.1 Introduction 3 1.2 Eight Great Ideas in Computer Architecture 11 1.3 Below Your Program 13 1.4 Under the Covers 16 1.5 Technologies for Building Processors and Memory 24 1.6 Performance 28 1.7 The Power Wall 40 1.8 The Sea Change: The Switch from Uniprocessors to Multiprocessors 43 1.9 Real Stuff: Benchmarking the Intel Core i7 46 1.10 Fallacies and Pitfalls 49 1.11 Concluding Remarks 52 1.12 Historical Perspective and Further Reading 54 1.13 Exercises 54 CMPS290 Class Notes (Chap01) Page 1 / 24 by Kuo-pao Yang 1.1 Introduction 3 Modern computer technology requires professionals of every computing specialty to understand both hardware and software. Classes of Computing Applications and Their Characteristics Personal computers o A computer designed for use by an individual, usually incorporating a graphics display, a keyboard, and a mouse. o Personal computers emphasize delivery of good performance to single users at low cost and usually execute third-party software. o This class of computing drove the evolution of many computing technologies, which is only about 35 years old! Server computers o A computer used for running larger programs for multiple users, often simultaneously, and typically accessed only via a network. o Servers are built from the same basic technology as desktop computers, but provide for greater computing, storage, and input/output capacity. Supercomputers o A class of computers with the highest performance and cost o Supercomputers consist of tens of thousands of processors and many terabytes of memory, and cost tens to hundreds of millions of dollars. -
Arch2030: a Vision of Computer Architecture Research Over
Arch2030: A Vision of Computer Architecture Research over the Next 15 Years This material is based upon work supported by the National Science Foundation under Grant No. (1136993). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Arch2030: A Vision of Computer Architecture Research over the Next 15 Years Luis Ceze, Mark D. Hill, Thomas F. Wenisch Sponsored by ARCH2030: A VISION OF COMPUTER ARCHITECTURE RESEARCH OVER THE NEXT 15 YEARS Summary .........................................................................................................................................................................1 The Specialization Gap: Democratizing Hardware Design ..........................................................................................2 The Cloud as an Abstraction for Architecture Innovation ..........................................................................................4 Going Vertical ................................................................................................................................................................5 Architectures “Closer to Physics” ................................................................................................................................5 Machine Learning as a Key Workload ..........................................................................................................................6 About this