Operating System - Miroslaw Malek
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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). -
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. -
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. -
Gpu Concurrency
GPU CONCURRENCY ROBERT SEARLES 5/26/2021 EXECUTION SCHEDULING & MANAGEMENT Pre-emptive scheduling Concurrent scheduling Processes share GPU through time-slicing Processes run on GPU simultaneously Scheduling managed by system User creates & manages scheduling streams C B A B C A B A time time time- slice 2 CUDA CONCURRENCY MECHANISMS Streams MPS MIG Partition Type Single process Logical Physical Max Partitions Unlimited 48 7 Performance Isolation No By percentage Yes Memory Protection No Yes Yes Memory Bandwidth QoS No No Yes Error Isolation No No Yes Cross-Partition Interop Always IPC Limited IPC Reconfigure Dynamic Process launch When idle MPS: Multi-Process Service MIG: Multi-Instance GPU 3 CUDA STREAMS 4 STREAM SEMANTICS 1. Two operations issued into the same stream will execute in issue- order. Operation B issued after Operation A will not begin to execute until Operation A has completed. 2. Two operations issued into separate streams have no ordering prescribed by CUDA. Operation A issued into stream 1 may execute before, during, or after Operation B issued into stream 2. Operation: Usually, cudaMemcpyAsync or a kernel call. More generally, most CUDA API calls that take a stream parameter, as well as stream callbacks. 5 STREAM EXAMPLES Host/Device execution concurrency: Kernel<<<b, t>>>(…); // this kernel execution can overlap with cpuFunction(…); // this host code Concurrent kernels: Kernel<<<b, t, 0, streamA>>>(…); // these kernels have the possibility Kernel<<<b, t, 0, streamB>>>(…); // to execute concurrently In practice, concurrent -
Hyper-Threading Technology Architecture and Microarchitecture
Hyper-Threading Technology Architecture and Microarchitecture Deborah T. Marr, Desktop Products Group, Intel Corp. Frank Binns, Desktop ProductsGroup, Intel Corp. David L. Hill, Desktop Products Group, Intel Corp. Glenn Hinton, Desktop Products Group, Intel Corp. David A. Koufaty, Desktop Products Group, Intel Corp. J. Alan Miller, Desktop Products Group, Intel Corp. Michael Upton, CPU Architecture, Desktop Products Group, Intel Corp. Index words: architecture, microarchitecture, Hyper-Threading Technology, simultaneous multi- threading, multiprocessor INTRODUCTION ABSTRACT The amazing growth of the Internet and telecommunications is powered by ever-faster systems Intel’s Hyper-Threading Technology brings the concept demanding increasingly higher levels of processor of simultaneous multi-threading to the Intel performance. To keep up with this demand we cannot Architecture. Hyper-Threading Technology makes a rely entirely on traditional approaches to processor single physical processor appear as two logical design. Microarchitecture techniques used to achieve processors; the physical execution resources are shared past processor performance improvement–super- and the architecture state is duplicated for the two pipelining, branch prediction, super-scalar execution, logical processors. From a software or architecture out-of-order execution, caches–have made perspective, this means operating systems and user microprocessors increasingly more complex, have more programs can schedule processes or threads to logical transistors, and consume more power. In fact, transistor processors as they would on multiple physical counts and power are increasing at rates greater than processors. From a microarchitecture perspective, this processor performance. Processor architects are means that instructions from both logical processors therefore looking for ways to improve performance at a will persist and execute simultaneously on shared greater rate than transistor counts and power execution resources. -
Personal-Computer Systems • Parallel Systems • Distributed Systems • Real -Time Systems
Module 1: Introduction • What is an operating system? • Simple Batch Systems • Multiprogramming Batched Systems • Time-Sharing Systems • Personal-Computer Systems • Parallel Systems • Distributed Systems • Real -Time Systems Applied Operating System Concepts 1.1 Silberschatz, Galvin, and Gagne Ď 1999 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. Applied Operating System Concepts 1.2 Silberschatz, Galvin, and Gagne Ď 1999 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). Applied Operating System Concepts 1.3 Silberschatz, Galvin, and Gagne Ď 1999 Abstract View of System Components Applied Operating System Concepts 1.4 Silberschatz, Galvin, and Gagne Ď 1999 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). Applied Operating System Concepts 1.5 Silberschatz, Galvin, and Gagne Ď 1999 Memory Layout for a Simple Batch System Applied Operating System Concepts 1.7 Silberschatz, Galvin, and Gagne Ď 1999 Multiprogrammed Batch Systems Several jobs are kept in main memory at the same time, and the CPU is multiplexed among them. -
Scheduling Many-Task Workloads on Supercomputers: Dealing with Trailing Tasks
Scheduling Many-Task Workloads on Supercomputers: Dealing with Trailing Tasks Timothy G. Armstrong, Zhao Zhang Daniel S. Katz, Michael Wilde, Ian T. Foster Department of Computer Science Computation Institute University of Chicago University of Chicago & Argonne National Laboratory [email protected], [email protected] [email protected], [email protected], [email protected] Abstract—In order for many-task applications to be attrac- as a worker and allocate one task per node. If tasks are tive candidates for running on high-end supercomputers, they single-threaded, each core or virtual thread can be treated must be able to benefit from the additional compute, I/O, as a worker. and communication performance provided by high-end HPC hardware relative to clusters, grids, or clouds. Typically this The second feature of many-task applications is an empha- means that the application should use the HPC resource in sis on high performance. The many tasks that make up the such a way that it can reduce time to solution beyond what application effectively collaborate to produce some result, is possible otherwise. Furthermore, it is necessary to make and in many cases it is important to get the results quickly. efficient use of the computational resources, achieving high This feature motivates the development of techniques to levels of utilization. Satisfying these twin goals is not trivial, because while the efficiently run many-task applications on HPC hardware. It parallelism in many task computations can vary over time, allows people to design and develop performance-critical on many large machines the allocation policy requires that applications in a many-task style and enables the scaling worker CPUs be provisioned and also relinquished in large up of existing many-task applications to run on much larger blocks rather than individually. -
Operating System
OPERATING SYSTEM INDEX LESSON 1: INTRODUCTION TO OPERATING SYSTEM LESSON 2: FILE SYSTEM – I LESSON 3: FILE SYSTEM – II LESSON 4: CPU SCHEDULING LESSON 5: MEMORY MANAGEMENT – I LESSON 6: MEMORY MANAGEMENT – II LESSON 7: DISK SCHEDULING LESSON 8: PROCESS MANAGEMENT LESSON 9: DEADLOCKS LESSON 10: CASE STUDY OF UNIX LESSON 11: CASE STUDY OF MS-DOS LESSON 12: CASE STUDY OF MS-WINDOWS NT Lesson No. 1 Intro. to Operating System 1 Lesson Number: 1 Writer: Dr. Rakesh Kumar Introduction to Operating System Vetter: Prof. Dharminder Kr. 1.0 OBJECTIVE The objective of this lesson is to make the students familiar with the basics of operating system. After studying this lesson they will be familiar with: 1. What is an operating system? 2. Important functions performed by an operating system. 3. Different types of operating systems. 1. 1 INTRODUCTION Operating system (OS) is a program or set of programs, which acts as an interface between a user of the computer & the computer hardware. The main purpose of an OS is to provide an environment in which we can execute programs. The main goals of the OS are (i) To make the computer system convenient to use, (ii) To make the use of computer hardware in efficient way. Operating System is system software, which may be viewed as collection of software consisting of procedures for operating the computer & providing an environment for execution of programs. It’s an interface between user & computer. So an OS makes everything in the computer to work together smoothly & efficiently. Figure 1: The relationship between application & system software Lesson No. -
Intel's Hyper-Threading
Intel’s Hyper-Threading Cody Tinker & Christopher Valerino Introduction ● How to handle a thread, or the smallest portion of code that can be run independently, plays a core component in enhancing a program's parallelism. ● Due to the fact that modern computers process many tasks and programs simultaneously, techniques that allow for threads to be handled more efficiently to lower processor downtime are valuable. ● Motive is to reduce the number of idle resources of a processor. ● Examples of processors that use hyperthreading ○ Intel Xeon D-1529 ○ Intel i7-6785R ○ Intel Pentium D1517 Introduction ● Two main trends have been followed to increase parallelism: ○ Increase number of cores on a chip ○ Increase core throughput What is Multithreading? ● Executing more than one thread at a time ● Normally, an operating system handles a program by scheduling individual threads and then passing them to the processor. ● Two different types of hardware multithreading ○ Temporal ■ One thread per pipeline stage ○ Simultaneous (SMT) ■ Multiple threads per pipeline stage ● Intel’s hyper-threading is a SMT design Hyper-threading ● Hyper-threading is the hardware solution to increasing processor throughput by decreasing resource idle time. ● Allows multiple concurrent threads to be executed ○ Threads are interleaved so that resources not being used by one thread are used by others ○ Most processors are limited to 2 concurrent threads per physical core ○ Some do support 8 concurrent threads per physical core ● Needs the ability to fetch instructions from -
COSC 6385 Computer Architecture - Multi-Processors (IV) Simultaneous Multi-Threading and Multi-Core Processors Edgar Gabriel Spring 2011
COSC 6385 Computer Architecture - Multi-Processors (IV) Simultaneous multi-threading and multi-core processors Edgar Gabriel Spring 2011 Edgar Gabriel Moore’s Law • Long-term trend on the number of transistor per integrated circuit • Number of transistors double every ~18 month Source: http://en.wikipedia.org/wki/Images:Moores_law.svg COSC 6385 – Computer Architecture Edgar Gabriel 1 What do we do with that many transistors? • Optimizing the execution of a single instruction stream through – Pipelining • Overlap the execution of multiple instructions • Example: all RISC architectures; Intel x86 underneath the hood – Out-of-order execution: • Allow instructions to overtake each other in accordance with code dependencies (RAW, WAW, WAR) • Example: all commercial processors (Intel, AMD, IBM, SUN) – Branch prediction and speculative execution: • Reduce the number of stall cycles due to unresolved branches • Example: (nearly) all commercial processors COSC 6385 – Computer Architecture Edgar Gabriel What do we do with that many transistors? (II) – Multi-issue processors: • Allow multiple instructions to start execution per clock cycle • Superscalar (Intel x86, AMD, …) vs. VLIW architectures – VLIW/EPIC architectures: • Allow compilers to indicate independent instructions per issue packet • Example: Intel Itanium series – Vector units: • Allow for the efficient expression and execution of vector operations • Example: SSE, SSE2, SSE3, SSE4 instructions COSC 6385 – Computer Architecture Edgar Gabriel 2 Limitations of optimizing a single instruction -
Chapter 20: the Linux System
Chapter 20: The Linux System Operating System Concepts – 10th dition Silberschatz, Galvin and Gagne ©2018 Chapter 20: The Linux System Linux History Design Principles Kernel Modules Process Management Scheduling Memory Management File Systems Input and Output Interprocess Communication Network Structure Security Operating System Concepts – 10th dition 20!2 Silberschatz, Galvin and Gagne ©2018 Objectives To explore the history o# the UNIX operating system from hich Linux is derived and the principles upon which Linux’s design is based To examine the Linux process model and illustrate how Linux schedules processes and provides interprocess communication To look at memory management in Linux To explore how Linux implements file systems and manages I/O devices Operating System Concepts – 10th dition 20!" Silberschatz, Galvin and Gagne ©2018 History Linux is a modern, free operating system (ased on $NIX standards First developed as a small (ut sel#-contained kernel in -.91 by Linus Torvalds, with the major design goal o# UNIX compatibility, released as open source Its history has (een one o# collaboration by many users from all around the orld, corresponding almost exclusively over the Internet It has been designed to run efficiently and reliably on common PC hardware, but also runs on a variety of other platforms The core Linux operating system kernel is entirely original, but it can run much existing free UNIX soft are, resulting in an entire UNIX-compatible operating system free from proprietary code Linux system has