CS 162 Operating Systems and Systems Programming Lecture 3
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Ebook - Informations About Operating Systems Version: August 15, 2006 | Download
eBook - Informations about Operating Systems Version: August 15, 2006 | Download: www.operating-system.org AIX Internet: AIX AmigaOS Internet: AmigaOS AtheOS Internet: AtheOS BeIA Internet: BeIA BeOS Internet: BeOS BSDi Internet: BSDi CP/M Internet: CP/M Darwin Internet: Darwin EPOC Internet: EPOC FreeBSD Internet: FreeBSD HP-UX Internet: HP-UX Hurd Internet: Hurd Inferno Internet: Inferno IRIX Internet: IRIX JavaOS Internet: JavaOS LFS Internet: LFS Linspire Internet: Linspire Linux Internet: Linux MacOS Internet: MacOS Minix Internet: Minix MorphOS Internet: MorphOS MS-DOS Internet: MS-DOS MVS Internet: MVS NetBSD Internet: NetBSD NetWare Internet: NetWare Newdeal Internet: Newdeal NEXTSTEP Internet: NEXTSTEP OpenBSD Internet: OpenBSD OS/2 Internet: OS/2 Further operating systems Internet: Further operating systems PalmOS Internet: PalmOS Plan9 Internet: Plan9 QNX Internet: QNX RiscOS Internet: RiscOS Solaris Internet: Solaris SuSE Linux Internet: SuSE Linux Unicos Internet: Unicos Unix Internet: Unix Unixware Internet: Unixware Windows 2000 Internet: Windows 2000 Windows 3.11 Internet: Windows 3.11 Windows 95 Internet: Windows 95 Windows 98 Internet: Windows 98 Windows CE Internet: Windows CE Windows Family Internet: Windows Family Windows ME Internet: Windows ME Seite 1 von 138 eBook - Informations about Operating Systems Version: August 15, 2006 | Download: www.operating-system.org Windows NT 3.1 Internet: Windows NT 3.1 Windows NT 4.0 Internet: Windows NT 4.0 Windows Server 2003 Internet: Windows Server 2003 Windows Vista Internet: Windows Vista Windows XP Internet: Windows XP Apple - Company Internet: Apple - Company AT&T - Company Internet: AT&T - Company Be Inc. - Company Internet: Be Inc. - Company BSD Family Internet: BSD Family Cray Inc. -
Processes Process States
Processes • A process is a program in execution • Synonyms include job, task, and unit of work • Not surprisingly, then, the parts of a process are precisely the parts of a running program: Program code, sometimes called the text section Program counter (where we are in the code) and other registers (data that CPU instructions can touch directly) Stack — for subroutines and their accompanying data Data section — for statically-allocated entities Heap — for dynamically-allocated entities Process States • Five states in general, with specific operating systems applying their own terminology and some using a finer level of granularity: New — process is being created Running — CPU is executing the process’s instructions Waiting — process is, well, waiting for an event, typically I/O or signal Ready — process is waiting for a processor Terminated — process is done running • See the text for a general state diagram of how a process moves from one state to another The Process Control Block (PCB) • Central data structure for representing a process, a.k.a. task control block • Consists of any information that varies from process to process: process state, program counter, registers, scheduling information, memory management information, accounting information, I/O status • The operating system maintains collections of PCBs to track current processes (typically as linked lists) • System state is saved/loaded to/from PCBs as the CPU goes from process to process; this is called… The Context Switch • Context switch is the technical term for the act -
Introduction to Multi-Threading and Vectorization Matti Kortelainen Larsoft Workshop 2019 25 June 2019 Outline
Introduction to multi-threading and vectorization Matti Kortelainen LArSoft Workshop 2019 25 June 2019 Outline Broad introductory overview: • Why multithread? • What is a thread? • Some threading models – std::thread – OpenMP (fork-join) – Intel Threading Building Blocks (TBB) (tasks) • Race condition, critical region, mutual exclusion, deadlock • Vectorization (SIMD) 2 6/25/19 Matti Kortelainen | Introduction to multi-threading and vectorization Motivations for multithreading Image courtesy of K. Rupp 3 6/25/19 Matti Kortelainen | Introduction to multi-threading and vectorization Motivations for multithreading • One process on a node: speedups from parallelizing parts of the programs – Any problem can get speedup if the threads can cooperate on • same core (sharing L1 cache) • L2 cache (may be shared among small number of cores) • Fully loaded node: save memory and other resources – Threads can share objects -> N threads can use significantly less memory than N processes • If smallest chunk of data is so big that only one fits in memory at a time, is there any other option? 4 6/25/19 Matti Kortelainen | Introduction to multi-threading and vectorization What is a (software) thread? (in POSIX/Linux) • “Smallest sequence of programmed instructions that can be managed independently by a scheduler” [Wikipedia] • A thread has its own – Program counter – Registers – Stack – Thread-local memory (better to avoid in general) • Threads of a process share everything else, e.g. – Program code, constants – Heap memory – Network connections – File handles -
Chapter 3: Processes
Chapter 3: Processes Operating System Concepts – 9th Edition Silberschatz, Galvin and Gagne ©2013 Chapter 3: Processes Process Concept Process Scheduling Operations on Processes Interprocess Communication Examples of IPC Systems Communication in Client-Server Systems Operating System Concepts – 9th Edition 3.2 Silberschatz, Galvin and Gagne ©2013 Objectives To introduce the notion of a process -- a program in execution, which forms the basis of all computation To describe the various features of processes, including scheduling, creation and termination, and communication To explore interprocess communication using shared memory and message passing To describe communication in client-server systems Operating System Concepts – 9th Edition 3.3 Silberschatz, Galvin and Gagne ©2013 Process Concept An operating system executes a variety of programs: Batch system – jobs Time-shared systems – user programs or tasks Textbook uses the terms job and process almost interchangeably Process – a program in execution; process execution must progress in sequential fashion Multiple parts The program code, also called text section Current activity including program counter, processor registers Stack containing temporary data Function parameters, return addresses, local variables Data section containing global variables Heap containing memory dynamically allocated during run time Operating System Concepts – 9th Edition 3.4 Silberschatz, Galvin and Gagne ©2013 Process Concept (Cont.) Program is passive entity stored on disk (executable -
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. -
CS 450: Operating Systems Sean Wallace <[email protected]>
Deadlock CS 450: Operating Systems Computer Sean Wallace <[email protected]> Science Science deadlock |ˈdedˌläk| noun 1 [ in sing. ] a situation, typically one involving opposing parties, in which no progress can be made: an attempt to break the deadlock. –New Oxford American Dictionary 2 Traffic Gridlock 3 Software Gridlock mutex_A.lock() mutex_B.lock() mutex_B.lock() mutex_A.lock() # critical section # critical section mutex_B.unlock() mutex_B.unlock() mutex_A.unlock() mutex_A.unlock() 4 Necessary Conditions for Deadlock 5 That is, what conditions need to be true (of some system) so that deadlock is possible? (Not the same as causing deadlock!) 6 1. Mutual Exclusion Resources can be held by process in a mutually exclusive manner 7 2. Hold & Wait While holding one resource (in mutex), a process can request another resource 8 3. No Preemption One process can not force another to give up a resource; i.e., releasing is voluntary 9 4. Circular Wait Resource requests and allocations create a cycle in the resource allocation graph 10 Resource Allocation Graphs 11 Process: Resource: Request: Allocation: 12 R1 R2 P1 P2 P3 R3 Circular wait is absent = no deadlock 13 R1 R2 P1 P2 P3 R3 All 4 necessary conditions in place; Deadlock! 14 In a system with only single-instance resources, necessary conditions ⟺ deadlock 15 P3 R1 P1 P2 R2 P2 Cycle without Deadlock! 16 Not practical (or always possible) to detect deadlock using a graph —but convenient to help us reason about things 17 Approaches to Dealing with Deadlock 18 1. Ostrich algorithm (Ignore it and hope it never happens) 2. -
Sched-ITS: an Interactive Tutoring System to Teach CPU Scheduling Concepts in an Operating Systems Course
Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2017 Sched-ITS: An Interactive Tutoring System to Teach CPU Scheduling Concepts in an Operating Systems Course Bharath Kumar Koya Wright State University Follow this and additional works at: https://corescholar.libraries.wright.edu/etd_all Part of the Computer Engineering Commons, and the Computer Sciences Commons Repository Citation Koya, Bharath Kumar, "Sched-ITS: An Interactive Tutoring System to Teach CPU Scheduling Concepts in an Operating Systems Course" (2017). Browse all Theses and Dissertations. 1745. https://corescholar.libraries.wright.edu/etd_all/1745 This Thesis is brought to you for free and open access by the Theses and Dissertations at CORE Scholar. It has been accepted for inclusion in Browse all Theses and Dissertations by an authorized administrator of CORE Scholar. For more information, please contact [email protected]. SCHED – ITS: AN INTERACTIVE TUTORING SYSTEM TO TEACH CPU SCHEDULING CONCEPTS IN AN OPERATING SYSTEMS COURSE A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science By BHARATH KUMAR KOYA B.E, Andhra University, India, 2015 2017 Wright State University WRIGHT STATE UNIVERSITY GRADUATE SCHOOL April 24, 2017 I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY SUPERVISION BY Bharath Kumar Koya ENTITLED SCHED-ITS: An Interactive Tutoring System to Teach CPU Scheduling Concepts in an Operating System Course BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQIREMENTS FOR THE DEGREE OF Master of Science. _____________________________________ Adam R. Bryant, Ph.D. Thesis Director _____________________________________ Mateen M. Rizki, Ph.D. Chair, Department of Computer Science and Engineering Committee on Final Examination _____________________________________ Adam R. -
Research Purpose Operating Systems – a Wide Survey
GESJ: Computer Science and Telecommunications 2010|No.3(26) ISSN 1512-1232 RESEARCH PURPOSE OPERATING SYSTEMS – A WIDE SURVEY Pinaki Chakraborty School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi – 110067, India. E-mail: [email protected] Abstract Operating systems constitute a class of vital software. A plethora of operating systems, of different types and developed by different manufacturers over the years, are available now. This paper concentrates on research purpose operating systems because many of them have high technological significance and they have been vividly documented in the research literature. Thirty-four academic and research purpose operating systems have been briefly reviewed in this paper. It was observed that the microkernel based architecture is being used widely to design research purpose operating systems. It was also noticed that object oriented operating systems are emerging as a promising option. Hence, the paper concludes by suggesting a study of the scope of microkernel based object oriented operating systems. Keywords: Operating system, research purpose operating system, object oriented operating system, microkernel 1. Introduction An operating system is a software that manages all the resources of a computer, both hardware and software, and provides an environment in which a user can execute programs in a convenient and efficient manner [1]. However, the principles and concepts used in the operating systems were not standardized in a day. In fact, operating systems have been evolving through the years [2]. There were no operating systems in the early computers. In those systems, every program required full hardware specification to execute correctly and perform each trivial task, and its own drivers for peripheral devices like card readers and line printers. -
Parallel Computing
Parallel Computing Announcements ● Midterm has been graded; will be distributed after class along with solutions. ● SCPD students: Midterms have been sent to the SCPD office and should be sent back to you soon. Announcements ● Assignment 6 due right now. ● Assignment 7 (Pathfinder) out, due next Tuesday at 11:30AM. ● Play around with graphs and graph algorithms! ● Learn how to interface with library code. ● No late submissions will be considered. This is as late as we're allowed to have the assignment due. Why Algorithms and Data Structures Matter Making Things Faster ● Choose better algorithms and data structures. ● Dropping from O(n2) to O(n log n) for large data sets will make your programs faster. ● Optimize your code. ● Try to reduce the constant factor in the big-O notation. ● Not recommended unless all else fails. ● Get a better computer. ● Having more memory and processing power can improve performance. ● New option: Use parallelism. How Your Programs Run Threads of Execution ● When running a program, that program gets a thread of execution (or thread). ● Each thread runs through code as normal. ● A program can have multiple threads running at the same time, each of which performs different tasks. ● A program that uses multiple threads is called multithreaded; writing a multithreaded program or algorithm is called multithreading. Threads in C++ ● The newest version of C++ (C++11) has libraries that support threading. ● To create a thread: ● Write the function that you want to execute. ● Construct an object of type thread to run that function. – Need header <thread> for this. ● That function will run in parallel alongside the original program. -
A Complete Bibliography of Publications in Software—Practice and Experience
A Complete Bibliography of Publications in Software|Practice and Experience Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 23 July 2021 Version 3.26 Title word cross-reference [Bar82a, Bar82c, Bar84b]. < [SMGMOFM07a, SMGMOFM07b]. > [SMGMOFM07a, SMGMOFM07b]. 2 [MST13, MDB19]. 3 [DS09]. 4 [MSR+07]. \ 0 [GW96]. 1 [GW96]. $1.50 [Bar78d]. $11 [PK04]. TM [MZB00, Win02]. 8 [DB21b]. k [Bar84a]. $12.00 [Rob72]. $13 [Bar84a]. [AW93, Mer93]. κ [MG94]. µ $13.00 [Rob72]. $18.50 [Jon74]. $185 [BS90c, BDS+92, SMNB21]. N [Bar79b]. $19.30 [Lan74a]. $19.50 [Dav78]. [MS98, Coh98, KST94, YAVHC21]. P 3 $25.00 [Pet77, And78]. 3 [BE02, FMA02]. [DC03]. PM [CLD+17]. q [GSR17]. τ $31-25 [Pet77]. $31.35 [Bri82]. 32 [VED06]. 2:5 [TSZ14, UDS+07]. $35.00 [Inc86]. $39.50 [Sim83]. 5 [CPMAH+20]. $58.50 [Wal81a]. $6.95 -ary [MS98]. -bit [AM10, SF85, VED06]. [Tho74]. 64 [AM10, VED06]. 68 -gram [Coh98, KST94, YAVHC21]. -grams [Ear76, Hol77]. $68.25 [Pit82]. $7.00 [GSR17]. -level [FM77]. -queens [Plu74]. [Bar72a]. $7.50 [Bar78d]. $7.95 -R [Ear76, Hol77]. -shortest-paths [MG94]. [Bar76a, Lav77]. $78.50 [Sim83]. 8 -System [BS90c]. [Plu74, SF85]. $8.95 [Bar82a, Bar82c, Bar84b]. $9.75 . [Bis81b]. .NET [Coo04, Han04]. [Bar77e, Mul76]. $9.80 [Atk79a]. $9.95 1 2 0 [Bar81, Edw98a, Edw98b, Gru83, Llo82, 2 [Bar74a, Bar74b, Bar80b, Bud85, Cor88b, Val77a, Val78, Wal83b]. -
Threading SIMD and MIMD in the Multicore Context the Ultrasparc T2
Overview SIMD and MIMD in the Multicore Context Single Instruction Multiple Instruction ● (note: Tute 02 this Weds - handouts) ● Flynn’s Taxonomy Single Data SISD MISD ● multicore architecture concepts Multiple Data SIMD MIMD ● for SIMD, the control unit and processor state (registers) can be shared ■ hardware threading ■ SIMD vs MIMD in the multicore context ● however, SIMD is limited to data parallelism (through multiple ALUs) ■ ● T2: design features for multicore algorithms need a regular structure, e.g. dense linear algebra, graphics ■ SSE2, Altivec, Cell SPE (128-bit registers); e.g. 4×32-bit add ■ system on a chip Rx: x x x x ■ 3 2 1 0 execution: (in-order) pipeline, instruction latency + ■ thread scheduling Ry: y3 y2 y1 y0 ■ caches: associativity, coherence, prefetch = ■ memory system: crossbar, memory controller Rz: z3 z2 z1 z0 (zi = xi + yi) ■ intermission ■ design requires massive effort; requires support from a commodity environment ■ speculation; power savings ■ massive parallelism (e.g. nVidia GPGPU) but memory is still a bottleneck ■ OpenSPARC ● multicore (CMT) is MIMD; hardware threading can be regarded as MIMD ● T2 performance (why the T2 is designed as it is) ■ higher hardware costs also includes larger shared resources (caches, TLBs) ● the Rock processor (slides by Andrew Over; ref: Tremblay, IEEE Micro 2009 ) needed ⇒ less parallelism than for SIMD COMP8320 Lecture 2: Multicore Architecture and the T2 2011 ◭◭◭ • ◮◮◮ × 1 COMP8320 Lecture 2: Multicore Architecture and the T2 2011 ◭◭◭ • ◮◮◮ × 3 Hardware (Multi)threading The UltraSPARC T2: System on a Chip ● recall concurrent execution on a single CPU: switch between threads (or ● OpenSparc Slide Cast Ch 5: p79–81,89 processes) requires the saving (in memory) of thread state (register values) ● aggressively multicore: 8 cores, each with 8-way hardware threading (64 virtual ■ motivation: utilize CPU better when thread stalled for I/O (6300 Lect O1, p9–10) CPUs) ■ what are the costs? do the same for smaller stalls? (e.g. -
Parallel Programming with Openmp
Parallel Programming with OpenMP OpenMP Parallel Programming Introduction: OpenMP Programming Model Thread-based parallelism utilized on shared-memory platforms Parallelization is either explicit, where programmer has full control over parallelization or through using compiler directives, existing in the source code. Thread is a process of a code is being executed. A thread of execution is the smallest unit of processing. Multiple threads can exist within the same process and share resources such as memory OpenMP Parallel Programming Introduction: OpenMP Programming Model Master thread is a single thread that runs sequentially; parallel execution occurs inside parallel regions and between two parallel regions, only the master thread executes the code. This is called the fork-join model: OpenMP Parallel Programming OpenMP Parallel Computing Hardware Shared memory allows immediate access to all data from all processors without explicit communication. 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 OpenMP 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 An alternative is MPI (Message Passing Interface): require low-level programming; more difficult programming