Scheduling Support for Heterogeneous Hardware Accelerators under Linux
|
|
- Rosa Regina Taylor
- 8 years ago
- Views:
Transcription
1 Scheduling Support for Heterogeneous Hardware Accelerators under Linux Tobias Wiersema University of Paderborn Paderborn, December / 24 Tobias Wiersema Linux scheduler extension for accelerators
2 Introduction Motivation Basics Concept Implementation Time sharing Migration Efficiency 2 / 24 Tobias Wiersema Linux scheduler extension for accelerators
3 Motivation Basics Accelerator-based heterogeneous systems Accelerators: General-purpose GPUs, FPGAs, ClearSpeed boards,... Accelerated systems increasingly important Off-the-shelf components Hybrid processor/accelerator designs (Cell BE, Virtex II Pro, Excalibur) Supercomputing (TSUBAME, TianHe-1a) New programming models (data-parallel execution) Cost-efficient and power-efficient designs Problem: Development of accelerated software 3 / 24 Tobias Wiersema Linux scheduler extension for accelerators
4 Current situation user space Accelerated applications kernel space Runtime library Driver Scheduler hardware Accelerator CPUs
5 Operating system integration Motivation Basics Integrate into OS kernel Hardware abstraction Master-Slave-relationship Communication Synchronization Scheduling: Global decisions 5 / 24 Tobias Wiersema Linux scheduler extension for accelerators
6 Operating system integration Motivation Basics Integrate into OS kernel Hardware abstraction Master-Slave-relationship Communication Synchronization Scheduling: Global decisions 5 / 24 Tobias Wiersema Linux scheduler extension for accelerators
7 Time Sharing Introduction Motivation Basics Historic predecessor: Multiprogramming Enhance processor utilization Time sharing Take fair turns on processor Shared usage of sought-after resources Tasks Users Today mostly relies on preemption 6 / 24 Tobias Wiersema Linux scheduler extension for accelerators
8 Motivation Basics Completely Fair Scheduler Current Linux scheduler: CFS Previous: O(n), O(1) Fairness O(log n): Red-black-tree Virtual time 7 / 24 Tobias Wiersema Linux scheduler extension for accelerators
9 Concept Implementation Introduction Motivation Basics Concept Implementation Time sharing Migration Efficiency 8 / 24 Tobias Wiersema Linux scheduler extension for accelerators
10 Current situation user space Accelerated applications kernel space Runtime library Driver Scheduler hardware Accelerator CPUs
11 Scheduling possibilities user space Accelerated applications kernel space Runtime library Driver Scheduler hardware Accelerator CPUs
12 Concept Implementation Possible approaches Interrupt path: Application accelerator 1. Application runtime library 2. Runtime library driver Pro and contra + Transparent scheduling + Unchanged applications Accelerator specific No time sharing No task migration 11 / 24 Tobias Wiersema Linux scheduler extension for accelerators
13 Concept Implementation Possible approaches Interrupt path: Application accelerator 1. Application runtime library 2. Runtime library driver Pro and contra + Transparent scheduling + Unchanged applications Accelerator specific No time sharing No task migration 11 / 24 Tobias Wiersema Linux scheduler extension for accelerators
14 Approach: user space Accelerated applications kernel space Runtime library Driver Scheduler extension Scheduler hardware Accelerator CPUs
15 Concept Implementation Kernel extension and programming model with Cooperative multitasking Checkpointing Pro and contra No transparent scheduling No unchanged applications + Not accelerator specific + Time sharing without preemption + Task migration 13 / 24 Tobias Wiersema Linux scheduler extension for accelerators
16 Concept Implementation Kernel extension and programming model with Cooperative multitasking Checkpointing Pro and contra No transparent scheduling No unchanged applications + Not accelerator specific + Time sharing without preemption + Task migration 13 / 24 Tobias Wiersema Linux scheduler extension for accelerators
17 Scheduling granularity Concept Implementation time spent on accelerator a) granularity copy execution allocation re-request re-request free time spent on accelerator b) granularity copy execution allocation re-request free 14 / 24 Tobias Wiersema Linux scheduler extension for accelerators
18 Concept Implementation Challenges / Design decisions Schedulable entity: Thread Ghost threads Static affinity model Problem: Affinity inversion 15 / 24 Tobias Wiersema Linux scheduler extension for accelerators
19 Integration into Linux kernel Concept Implementation Once One per computing unit One per task struct computing_units struct computing_unit_info struct cfs_rq rq struct hardware_properties hp struct task_struct struct sched_entity se struct sched_entity hwse struct hardware_properties struct sched_entity struct meta_info mi struct cfs_rq list tasks rb_tree tasks_timeline struct meta_info mi 16 / 24 Tobias Wiersema Linux scheduler extension for accelerators
20 Time sharing Migration Efficiency Introduction Motivation Basics Concept Implementation Time sharing Migration Efficiency 17 / 24 Tobias Wiersema Linux scheduler extension for accelerators
21 Time sharing of an accelerator Time sharing Migration Efficiency tasks on one GPU searched strings in billions seconds 18 / 24 Tobias Wiersema Linux scheduler extension for accelerators
22 Heterogeneous task migration Time sharing Migration Efficiency tasks on either CPU or GPU searched strings in billions seconds 19 / 24 Tobias Wiersema Linux scheduler extension for accelerators
23 Time sharing Migration Efficiency Task switching overhead tasks on one GPU seconds execution time average turnaround time sec 1sec 2sec 4sec fcfs granularity setting 20 / 24 Tobias Wiersema Linux scheduler extension for accelerators
24 Time sharing Migration Efficiency Load balancer 100% 90% 80% 70% 60% 50% 40% 30% load balancing MD5 computation PF computation 20% 10% 0% concurrent tasks 21 / 24 Tobias Wiersema Linux scheduler extension for accelerators
25 Conclusions of the kernel extension Kernel-controlled time sharing Accelerator sharing Fairness Heterogeneous task migration Speedup Load balancing Exploit heterogeneity Enhance accelerator utilization 22 / 24 Tobias Wiersema Linux scheduler extension for accelerators
26 Future work Enhance load balancer Combine with enhanced runtime libraries Automate programming model Enhance ghost threads Communication Synchronization 23 / 24 Tobias Wiersema Linux scheduler extension for accelerators
27 Thank you for your attention! Do you have questions? 24 / 24 Tobias Wiersema Linux scheduler extension for accelerators
28 Completely Fair Scheduler Current Linux scheduler: CFS Previous: O(n), O(1) Fairness O(log n): Red-black-tree Virtual clock No time slices 25 Tobias Wiersema Linux scheduler extension for accelerators
29 Notation of time sharing task timings turnaround time a) A1 B1 A2 A3 A1 B1 A2 latency execution time latency execution time A3 b) A1 B1 A2 B1 A3 B1 A3 A2 B1 A1 turnaround time 26 Tobias Wiersema Linux scheduler extension for accelerators
30 Task migration with true time sharing searched strings in billions a) b) seconds 27 Tobias Wiersema Linux scheduler extension for accelerators
31 Task migration with QL5 and no fixed set of tasks searched strings in billions a) seconds 28 Tobias Wiersema Linux scheduler extension for accelerators
32 Runqueue of an accelerator Running Waiting... Runqueue with ghost threads... Execution units Accelerator 29 Tobias Wiersema Linux scheduler extension for accelerators
33 Affinity Inversion example Step 1 Step 2 Step 3 Step 4 Unit 1 A A A C C Unit 2 B B B 30 Tobias Wiersema Linux scheduler extension for accelerators
34 Overview of data structures Once One per computing unit One per task struct computing_units list list_of_cus[type] current_id access_mutex struct computing_unit_info id type struct cfs_rq rq struct hardware_properties hp struct task_struct struct sched_entity se struct sched_entity hwse struct hardware_properties concurrent_kernels bandwitdth struct cfs_rq load min_vruntime list tasks rb_tree tasks_timeline count maxcount access_mutex struct sched_entity load semaphore_up need_migrate task_granularity_nsec current_affinity offerer offered_affinity struct meta_info mi 31 Tobias Wiersema Linux scheduler extension for accelerators
35 Task states if using an accelerator cooperatively Non-accelerated computation Accelerated computation No computing unit assigned free allocate computing unit assignment invalid finished computation or denied re-request Computing unit assignment valid successful re-request 32 Tobias Wiersema Linux scheduler extension for accelerators
36 Turnaround times with infinite granularity 75 Tasks (25 MD5, 50 PF), started at the same time seconds tasks 33 Tobias Wiersema Linux scheduler extension for accelerators
37 Turnaround times with four second granularity 75 Tasks (25 MD5, 50 PF), started at the same time seconds tasks 34 Tobias Wiersema Linux scheduler extension for accelerators
Programming and Scheduling Model for Supporting Heterogeneous Architectures in Linux
Programming and Scheduling Model for Supporting Heterogeneous Architectures in Linux Third Workshop on Computer Architecture and Operating System co-design Paris, 25.01.2012 Tobias Beisel, Tobias Wiersema,
More informationMultiprocessor Scheduling and Scheduling in Linux Kernel 2.6
Multiprocessor Scheduling and Scheduling in Linux Kernel 2.6 Winter Term 2008 / 2009 Jun.-Prof. Dr. André Brinkmann Andre.Brinkmann@uni-paderborn.de Universität Paderborn PC² Agenda Multiprocessor and
More informationDeciding which process to run. (Deciding which thread to run) Deciding how long the chosen process can run
SFWR ENG 3BB4 Software Design 3 Concurrent System Design 2 SFWR ENG 3BB4 Software Design 3 Concurrent System Design 11.8 10 CPU Scheduling Chapter 11 CPU Scheduling Policies Deciding which process to run
More informationLinux Process Scheduling. sched.c. schedule() scheduler_tick() hooks. try_to_wake_up() ... CFS CPU 0 CPU 1 CPU 2 CPU 3
Linux Process Scheduling sched.c schedule() scheduler_tick() try_to_wake_up() hooks RT CPU 0 CPU 1 CFS CPU 2 CPU 3 Linux Process Scheduling 1. Task Classification 2. Scheduler Skeleton 3. Completely Fair
More information10.04.2008. Thomas Fahrig Senior Developer Hypervisor Team. Hypervisor Architecture Terminology Goals Basics Details
Thomas Fahrig Senior Developer Hypervisor Team Hypervisor Architecture Terminology Goals Basics Details Scheduling Interval External Interrupt Handling Reserves, Weights and Caps Context Switch Waiting
More informationOPERATING SYSTEMS SCHEDULING
OPERATING SYSTEMS SCHEDULING Jerry Breecher 5: CPU- 1 CPU What Is In This Chapter? This chapter is about how to get a process attached to a processor. It centers around efficient algorithms that perform
More informationOperating System: Scheduling
Process Management Operating System: Scheduling OS maintains a data structure for each process called Process Control Block (PCB) Information associated with each PCB: Process state: e.g. ready, or waiting
More informationTask Scheduling for Multicore Embedded Devices
Embedded Linux Conference 2013 Task Scheduling for Multicore Embedded Devices 2013. 02. 22. Gap-Joo Na (funkygap@etri.re.kr) Contents 2 What is multicore?? 1. Multicore trends 2. New Architectures 3. Software
More informationKernel comparison of OpenSolaris, Windows Vista and. Linux 2.6
Kernel comparison of OpenSolaris, Windows Vista and Linux 2.6 The idea of writing this paper is evoked by Max Bruning's view on Solaris, BSD and Linux. The comparison of advantages and disadvantages among
More informationPage 1 of 5. IS 335: Information Technology in Business Lecture Outline Operating Systems
Lecture Outline Operating Systems Objectives Describe the functions and layers of an operating system List the resources allocated by the operating system and describe the allocation process Explain how
More informationCHAPTER 1 INTRODUCTION
1 CHAPTER 1 INTRODUCTION 1.1 MOTIVATION OF RESEARCH Multicore processors have two or more execution cores (processors) implemented on a single chip having their own set of execution and architectural recourses.
More informationCompletely Fair Scheduler and its tuning 1
Completely Fair Scheduler and its tuning 1 Jacek Kobus and Rafał Szklarski 1 Introduction The introduction of a new, the so called completely fair scheduler (CFS) to the Linux kernel 2.6.23 (October 2007)
More informationCPU SCHEDULING (CONT D) NESTED SCHEDULING FUNCTIONS
CPU SCHEDULING CPU SCHEDULING (CONT D) Aims to assign processes to be executed by the CPU in a way that meets system objectives such as response time, throughput, and processor efficiency Broken down into
More informationCPU Scheduling Outline
CPU Scheduling Outline What is scheduling in the OS? What are common scheduling criteria? How to evaluate scheduling algorithms? What are common scheduling algorithms? How is thread scheduling different
More informationCPU Scheduling. Core Definitions
CPU Scheduling General rule keep the CPU busy; an idle CPU is a wasted CPU Major source of CPU idleness: I/O (or waiting for it) Many programs have a characteristic CPU I/O burst cycle alternating phases
More informationLinux Scheduler Analysis and Tuning for Parallel Processing on the Raspberry PI Platform. Ed Spetka Mike Kohler
Linux Scheduler Analysis and Tuning for Parallel Processing on the Raspberry PI Platform Ed Spetka Mike Kohler Outline Abstract Hardware Overview Completely Fair Scheduler Design Theory Breakdown of the
More informationObjectives. Chapter 5: CPU Scheduling. CPU Scheduler. Non-preemptive and preemptive. Dispatcher. Alternating Sequence of CPU And I/O Bursts
Objectives Chapter 5: CPU Scheduling Introduce CPU scheduling, which is the basis for multiprogrammed operating systems Describe various CPU-scheduling algorithms Discuss evaluation criteria for selecting
More informationCPU Scheduling. CPU Scheduling
CPU Scheduling Electrical and Computer Engineering Stephen Kim (dskim@iupui.edu) ECE/IUPUI RTOS & APPS 1 CPU Scheduling Basic Concepts Scheduling Criteria Scheduling Algorithms Multiple-Processor Scheduling
More informationLinux scheduler history. We will be talking about the O(1) scheduler
CPU Scheduling Linux scheduler history We will be talking about the O(1) scheduler SMP Support in 2.4 and 2.6 versions 2.4 Kernel 2.6 Kernel CPU1 CPU2 CPU3 CPU1 CPU2 CPU3 Linux Scheduling 3 scheduling
More informationProcess Scheduling II
Process Scheduling II COMS W4118 Prof. Kaustubh R. Joshi krj@cs.columbia.edu hdp://www.cs.columbia.edu/~krj/os References: OperaWng Systems Concepts (9e), Linux Kernel Development, previous W4118s Copyright
More informationOperating Systems Concepts: Chapter 7: Scheduling Strategies
Operating Systems Concepts: Chapter 7: Scheduling Strategies Olav Beckmann Huxley 449 http://www.doc.ic.ac.uk/~ob3 Acknowledgements: There are lots. See end of Chapter 1. Home Page for the course: http://www.doc.ic.ac.uk/~ob3/teaching/operatingsystemsconcepts/
More informationOperating System Tutorial
Operating System Tutorial OPERATING SYSTEM TUTORIAL Simply Easy Learning by tutorialspoint.com tutorialspoint.com i ABOUT THE TUTORIAL Operating System Tutorial An operating system (OS) is a collection
More informationOperatin g Systems: Internals and Design Principle s. Chapter 10 Multiprocessor and Real-Time Scheduling Seventh Edition By William Stallings
Operatin g Systems: Internals and Design Principle s Chapter 10 Multiprocessor and Real-Time Scheduling Seventh Edition By William Stallings Operating Systems: Internals and Design Principles Bear in mind,
More informationOperating Systems 4 th Class
Operating Systems 4 th Class Lecture 1 Operating Systems Operating systems are essential part of any computer system. Therefore, a course in operating systems is an essential part of any computer science
More informationRoad Map. Scheduling. Types of Scheduling. Scheduling. CPU Scheduling. Job Scheduling. Dickinson College Computer Science 354 Spring 2010.
Road Map Scheduling Dickinson College Computer Science 354 Spring 2010 Past: What an OS is, why we have them, what they do. Base hardware and support for operating systems Process Management Threads Present:
More informationMulti-core Programming System Overview
Multi-core Programming System Overview Based on slides from Intel Software College and Multi-Core Programming increasing performance through software multi-threading by Shameem Akhter and Jason Roberts,
More informationNVIDIA CUDA Software and GPU Parallel Computing Architecture. David B. Kirk, Chief Scientist
NVIDIA CUDA Software and GPU Parallel Computing Architecture David B. Kirk, Chief Scientist Outline Applications of GPU Computing CUDA Programming Model Overview Programming in CUDA The Basics How to Get
More informationNetworking Virtualization Using FPGAs
Networking Virtualization Using FPGAs Russell Tessier, Deepak Unnikrishnan, Dong Yin, and Lixin Gao Reconfigurable Computing Group Department of Electrical and Computer Engineering University of Massachusetts,
More informationChapter 5 Process Scheduling
Chapter 5 Process Scheduling CPU Scheduling Objective: Basic Scheduling Concepts CPU Scheduling Algorithms Why Multiprogramming? Maximize CPU/Resources Utilization (Based on Some Criteria) CPU Scheduling
More informationObjectives. Chapter 5: Process Scheduling. Chapter 5: Process Scheduling. 5.1 Basic Concepts. To introduce CPU scheduling
Objectives To introduce CPU scheduling To describe various CPU-scheduling algorithms Chapter 5: Process Scheduling To discuss evaluation criteria for selecting the CPUscheduling algorithm for a particular
More informationImprovement of Scheduling Granularity for Deadline Scheduler
Improvement of Scheduling Granularity for Deadline Scheduler Yoshitake Kobayashi Advanced Software Technology Group Corporate Software Engineering Center TOSHIBA CORPORATION Copyright 2012, Toshiba Corporation.
More informationCPU Scheduling. Basic Concepts. Basic Concepts (2) Basic Concepts Scheduling Criteria Scheduling Algorithms Batch systems Interactive systems
Basic Concepts Scheduling Criteria Scheduling Algorithms Batch systems Interactive systems Based on original slides by Silberschatz, Galvin and Gagne 1 Basic Concepts CPU I/O Burst Cycle Process execution
More informationScheduling. Yücel Saygın. These slides are based on your text book and on the slides prepared by Andrew S. Tanenbaum
Scheduling Yücel Saygın These slides are based on your text book and on the slides prepared by Andrew S. Tanenbaum 1 Scheduling Introduction to Scheduling (1) Bursts of CPU usage alternate with periods
More informationEECS 750: Advanced Operating Systems. 01/28 /2015 Heechul Yun
EECS 750: Advanced Operating Systems 01/28 /2015 Heechul Yun 1 Recap: Completely Fair Scheduler(CFS) Each task maintains its virtual time V i = E i 1 w i, where E is executed time, w is a weight Pick the
More informationThe Uintah Framework: A Unified Heterogeneous Task Scheduling and Runtime System
The Uintah Framework: A Unified Heterogeneous Task Scheduling and Runtime System Qingyu Meng, Alan Humphrey, Martin Berzins Thanks to: John Schmidt and J. Davison de St. Germain, SCI Institute Justin Luitjens
More informationLinux O(1) CPU Scheduler. Amit Gud amit (dot) gud (at) veritas (dot) com http://amitgud.tk
Linux O(1) CPU Scheduler Amit Gud amit (dot) gud (at) veritas (dot) com http://amitgud.tk April 27, 2005 Agenda CPU scheduler basics CPU scheduler algorithms overview Linux CPU scheduler goals What is
More informationICS 143 - Principles of Operating Systems
ICS 143 - Principles of Operating Systems Lecture 5 - CPU Scheduling Prof. Nalini Venkatasubramanian nalini@ics.uci.edu Note that some slides are adapted from course text slides 2008 Silberschatz. Some
More informationUpdate on big.little scheduling experiments. Morten Rasmussen Technology Researcher
Update on big.little scheduling experiments Morten Rasmussen Technology Researcher 1 Agenda Why is big.little different from SMP? Summary of previous experiments on emulated big.little. New results for
More informationProcess Scheduling in Linux
Process Scheduling in Linux This document contains notes about how the Linux kernel handles process scheduling. They cover the general scheduler skeleton, scheduling classes, the completely fair scheduling
More informationProgramming models for heterogeneous computing. Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga
Programming models for heterogeneous computing Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga Talk outline [30 slides] 1. Introduction [5 slides] 2.
More informationChapter 5: CPU Scheduling. Operating System Concepts 8 th Edition
Chapter 5: CPU Scheduling Silberschatz, Galvin and Gagne 2009 Chapter 5: CPU Scheduling Basic Concepts Scheduling Criteria Scheduling Algorithms Thread Scheduling Multiple-Processor Scheduling Operating
More informationLong-term monitoring of apparent latency in PREEMPT RT Linux real-time systems
Long-term monitoring of apparent latency in PREEMPT RT Linux real-time systems Carsten Emde Open Source Automation Development Lab (OSADL) eg Aichhalder Str. 39, 78713 Schramberg, Germany C.Emde@osadl.org
More informationMitigating Starvation of Linux CPU-bound Processes in the Presence of Network I/O
Mitigating Starvation of Linux CPU-bound Processes in the Presence of Network I/O 1 K. Salah 1 Computer Engineering Department Khalifa University of Science Technology and Research (KUSTAR) Sharjah, UAE
More informationControl 2004, University of Bath, UK, September 2004
Control, University of Bath, UK, September ID- IMPACT OF DEPENDENCY AND LOAD BALANCING IN MULTITHREADING REAL-TIME CONTROL ALGORITHMS M A Hossain and M O Tokhi Department of Computing, The University of
More informationTasks Schedule Analysis in RTAI/Linux-GPL
Tasks Schedule Analysis in RTAI/Linux-GPL Claudio Aciti and Nelson Acosta INTIA - Depto de Computación y Sistemas - Facultad de Ciencias Exactas Universidad Nacional del Centro de la Provincia de Buenos
More informationLinux Process Scheduling Policy
Lecture Overview Introduction to Linux process scheduling Policy versus algorithm Linux overall process scheduling objectives Timesharing Dynamic priority Favor I/O-bound process Linux scheduling algorithm
More informationFPGA-based Multithreading for In-Memory Hash Joins
FPGA-based Multithreading for In-Memory Hash Joins Robert J. Halstead, Ildar Absalyamov, Walid A. Najjar, Vassilis J. Tsotras University of California, Riverside Outline Background What are FPGAs Multithreaded
More informationLarge-scale performance monitoring framework for cloud monitoring. Live Trace Reading and Processing
Large-scale performance monitoring framework for cloud monitoring Live Trace Reading and Processing Julien Desfossez Michel Dagenais May 2014 École Polytechnique de Montreal Live Trace Reading Read the
More informationWhy Computers Are Getting Slower (and what we can do about it) Rik van Riel Sr. Software Engineer, Red Hat
Why Computers Are Getting Slower (and what we can do about it) Rik van Riel Sr. Software Engineer, Red Hat Why Computers Are Getting Slower The traditional approach better performance Why computers are
More informationOverlapping Data Transfer With Application Execution on Clusters
Overlapping Data Transfer With Application Execution on Clusters Karen L. Reid and Michael Stumm reid@cs.toronto.edu stumm@eecg.toronto.edu Department of Computer Science Department of Electrical and Computer
More informationMCA Standards For Closely Distributed Multicore
MCA Standards For Closely Distributed Multicore Sven Brehmer Multicore Association, cofounder, board member, and MCAPI WG Chair CEO of PolyCore Software 2 Embedded Systems Spans the computing industry
More informationOperating Systems. 05. Threads. Paul Krzyzanowski. Rutgers University. Spring 2015
Operating Systems 05. Threads Paul Krzyzanowski Rutgers University Spring 2015 February 9, 2015 2014-2015 Paul Krzyzanowski 1 Thread of execution Single sequence of instructions Pointed to by the program
More informationProcessor Scheduling. Queues Recall OS maintains various queues
Processor Scheduling Chapters 9 and 10 of [OS4e], Chapter 6 of [OSC]: Queues Scheduling Criteria Cooperative versus Preemptive Scheduling Scheduling Algorithms Multi-level Queues Multiprocessor and Real-Time
More informationAnnouncements. Basic Concepts. Histogram of Typical CPU- Burst Times. Dispatcher. CPU Scheduler. Burst Cycle. Reading
Announcements Reading Chapter 5 Chapter 7 (Monday or Wednesday) Basic Concepts CPU I/O burst cycle Process execution consists of a cycle of CPU execution and I/O wait. CPU burst distribution What are the
More informationResource Scheduling Best Practice in Hybrid Clusters
Available online at www.prace-ri.eu Partnership for Advanced Computing in Europe Resource Scheduling Best Practice in Hybrid Clusters C. Cavazzoni a, A. Federico b, D. Galetti a, G. Morelli b, A. Pieretti
More informationProcess Description and Control. 2004-2008 william stallings, maurizio pizzonia - sistemi operativi
Process Description and Control 1 Process A program in execution (running) on a computer The entity that can be assigned to and executed on a processor A unit of activity characterized by a at least one
More informationFPGA Accelerator Virtualization in an OpenPOWER cloud. Fei Chen, Yonghua Lin IBM China Research Lab
FPGA Accelerator Virtualization in an OpenPOWER cloud Fei Chen, Yonghua Lin IBM China Research Lab Trend of Acceleration Technology Acceleration in Cloud is Taking Off Used FPGA to accelerate Bing search
More informationOS OBJECTIVE QUESTIONS
OS OBJECTIVE QUESTIONS Which one of the following is Little s formula Where n is the average queue length, W is the time that a process waits 1)n=Lambda*W 2)n=Lambda/W 3)n=Lambda^W 4)n=Lambda*(W-n) Answer:1
More informationOperating Systems, 6 th ed. Test Bank Chapter 7
True / False Questions: Chapter 7 Memory Management 1. T / F In a multiprogramming system, main memory is divided into multiple sections: one for the operating system (resident monitor, kernel) and one
More informationGPU File System Encryption Kartik Kulkarni and Eugene Linkov
GPU File System Encryption Kartik Kulkarni and Eugene Linkov 5/10/2012 SUMMARY. We implemented a file system that encrypts and decrypts files. The implementation uses the AES algorithm computed through
More informationò Scheduling overview, key trade-offs, etc. ò O(1) scheduler older Linux scheduler ò Today: Completely Fair Scheduler (CFS) new hotness
Last time Scheduling overview, key trade-offs, etc. O(1) scheduler older Linux scheduler Scheduling, part 2 Don Porter CSE 506 Today: Completely Fair Scheduler (CFS) new hotness Other advanced scheduling
More informationW4118 Operating Systems. Instructor: Junfeng Yang
W4118 Operating Systems Instructor: Junfeng Yang Outline Advanced scheduling issues Multilevel queue scheduling Multiprocessor scheduling issues Real-time scheduling Scheduling in Linux Scheduling algorithm
More informationAgenda. Context. System Power Management Issues. Power Capping Overview. Power capping participants. Recommendations
Power Capping Linux Agenda Context System Power Management Issues Power Capping Overview Power capping participants Recommendations Introduction of Linux Power Capping Framework 2 Power Hungry World Worldwide,
More informationEffective Computing with SMP Linux
Effective Computing with SMP Linux Multi-processor systems were once a feature of high-end servers and mainframes, but today, even desktops for personal use have multiple processors. Linux is a popular
More information2. is the number of processes that are completed per time unit. A) CPU utilization B) Response time C) Turnaround time D) Throughput
Import Settings: Base Settings: Brownstone Default Highest Answer Letter: D Multiple Keywords in Same Paragraph: No Chapter: Chapter 5 Multiple Choice 1. Which of the following is true of cooperative scheduling?
More informationOperating Systems OBJECTIVES 7.1 DEFINITION. Chapter 7. Note:
Chapter 7 OBJECTIVES Operating Systems Define the purpose and functions of an operating system. Understand the components of an operating system. Understand the concept of virtual memory. Understand the
More informationScheduling 0 : Levels. High level scheduling: Medium level scheduling: Low level scheduling
Scheduling 0 : Levels High level scheduling: Deciding whether another process can run is process table full? user process limit reached? load to swap space or memory? Medium level scheduling: Balancing
More informationBasics of Virtualisation
Basics of Virtualisation Volker Büge Institut für Experimentelle Kernphysik Universität Karlsruhe Die Kooperation von The x86 Architecture Why do we need virtualisation? x86 based operating systems are
More informationMODULE 3 VIRTUALIZED DATA CENTER COMPUTE
MODULE 3 VIRTUALIZED DATA CENTER COMPUTE Module 3: Virtualized Data Center Compute Upon completion of this module, you should be able to: Describe compute virtualization Discuss the compute virtualization
More informationA Survey of Parallel Processing in Linux
A Survey of Parallel Processing in Linux Kojiro Akasaka Computer Science Department San Jose State University San Jose, CA 95192 408 924 1000 kojiro.akasaka@sjsu.edu ABSTRACT Any kernel with parallel processing
More informationReal-Time Operating Systems for MPSoCs
Real-Time Operating Systems for MPSoCs Hiroyuki Tomiyama Graduate School of Information Science Nagoya University http://member.acm.org/~hiroyuki MPSoC 2009 1 Contributors Hiroaki Takada Director and Professor
More informationXeon+FPGA Platform for the Data Center
Xeon+FPGA Platform for the Data Center ISCA/CARL 2015 PK Gupta, Director of Cloud Platform Technology, DCG/CPG Overview Data Center and Workloads Xeon+FPGA Accelerator Platform Applications and Eco-system
More informationCS4410 - Fall 2008 Homework 2 Solution Due September 23, 11:59PM
CS4410 - Fall 2008 Homework 2 Solution Due September 23, 11:59PM Q1. Explain what goes wrong in the following version of Dekker s Algorithm: CSEnter(int i) inside[i] = true; while(inside[j]) inside[i]
More informationScheduling. Scheduling. Scheduling levels. Decision to switch the running process can take place under the following circumstances:
Scheduling Scheduling Scheduling levels Long-term scheduling. Selects which jobs shall be allowed to enter the system. Only used in batch systems. Medium-term scheduling. Performs swapin-swapout operations
More informationJob Scheduling Model
Scheduling 1 Job Scheduling Model problem scenario: a set of jobs needs to be executed using a single server, on which only one job at a time may run for theith job, we have an arrival timea i and a run
More informationGPU Profiling with AMD CodeXL
GPU Profiling with AMD CodeXL Software Profiling Course Hannes Würfel OUTLINE 1. Motivation 2. GPU Recap 3. OpenCL 4. CodeXL Overview 5. CodeXL Internals 6. CodeXL Profiling 7. CodeXL Debugging 8. Sources
More informationThe QEMU/KVM Hypervisor
The /KVM Hypervisor Understanding what's powering your virtual machine Dr. David Alan Gilbert dgilbert@redhat.com 2015-10-14 Topics Hypervisors and where /KVM sits Components of a virtual machine KVM Devices:
More informationLinux Scheduler. Linux Scheduler
or or Affinity Basic Interactive es 1 / 40 Reality... or or Affinity Basic Interactive es The Linux scheduler tries to be very efficient To do that, it uses some complex data structures Some of what it
More informationGraphics Cards and Graphics Processing Units. Ben Johnstone Russ Martin November 15, 2011
Graphics Cards and Graphics Processing Units Ben Johnstone Russ Martin November 15, 2011 Contents Graphics Processing Units (GPUs) Graphics Pipeline Architectures 8800-GTX200 Fermi Cayman Performance Analysis
More informationSolution Guide Parallels Virtualization for Linux
Solution Guide Parallels Virtualization for Linux Overview Created in 1991, Linux was designed to be UNIX-compatible software that was composed entirely of open source or free software components. Linux
More informationMain Points. Scheduling policy: what to do next, when there are multiple threads ready to run. Definitions. Uniprocessor policies
Scheduling Main Points Scheduling policy: what to do next, when there are multiple threads ready to run Or multiple packets to send, or web requests to serve, or Definitions response time, throughput,
More informationOverview on Modern Accelerators and Programming Paradigms Ivan Giro7o igiro7o@ictp.it
Overview on Modern Accelerators and Programming Paradigms Ivan Giro7o igiro7o@ictp.it Informa(on & Communica(on Technology Sec(on (ICTS) Interna(onal Centre for Theore(cal Physics (ICTP) Mul(ple Socket
More informationReal-Time Scheduling 1 / 39
Real-Time Scheduling 1 / 39 Multiple Real-Time Processes A runs every 30 msec; each time it needs 10 msec of CPU time B runs 25 times/sec for 15 msec C runs 20 times/sec for 5 msec For our equation, A
More informationIntroduction to GP-GPUs. Advanced Computer Architectures, Cristina Silvano, Politecnico di Milano 1
Introduction to GP-GPUs Advanced Computer Architectures, Cristina Silvano, Politecnico di Milano 1 GPU Architectures: How do we reach here? NVIDIA Fermi, 512 Processing Elements (PEs) 2 What Can It Do?
More informationA Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems Anton Beloglazov, Rajkumar Buyya, Young Choon Lee, and Albert Zomaya Present by Leping Wang 1/25/2012 Outline Background
More informationCS 377: Operating Systems. Outline. A review of what you ve learned, and how it applies to a real operating system. Lecture 25 - Linux Case Study
CS 377: Operating Systems Lecture 25 - Linux Case Study Guest Lecturer: Tim Wood Outline Linux History Design Principles System Overview Process Scheduling Memory Management File Systems A review of what
More informationEmbedded Systems: map to FPGA, GPU, CPU?
Embedded Systems: map to FPGA, GPU, CPU? Jos van Eijndhoven jos@vectorfabrics.com Bits&Chips Embedded systems Nov 7, 2013 # of transistors Moore s law versus Amdahl s law Computational Capacity Hardware
More informationLOAD BALANCING DISTRIBUTED OPERATING SYSTEMS, SCALABILITY, SS 2015. Hermann Härtig
LOAD BALANCING DISTRIBUTED OPERATING SYSTEMS, SCALABILITY, SS 2015 Hermann Härtig ISSUES starting points independent Unix processes and block synchronous execution who does it load migration mechanism
More informationVirtualization Technologies and Blackboard: The Future of Blackboard Software on Multi-Core Technologies
Virtualization Technologies and Blackboard: The Future of Blackboard Software on Multi-Core Technologies Kurt Klemperer, Principal System Performance Engineer kklemperer@blackboard.com Agenda Session Length:
More informationREAL TIME OPERATING SYSTEMS. Lesson-10:
REAL TIME OPERATING SYSTEMS Lesson-10: Real Time Operating System 1 1. Real Time Operating System Definition 2 Real Time A real time is the time which continuously increments at regular intervals after
More informationMulti-Threading Performance on Commodity Multi-Core Processors
Multi-Threading Performance on Commodity Multi-Core Processors Jie Chen and William Watson III Scientific Computing Group Jefferson Lab 12000 Jefferson Ave. Newport News, VA 23606 Organization Introduction
More informationTypes Of Operating Systems
Types Of Operating Systems Date 10/01/2004 1/24/2004 Operating Systems 1 Brief history of OS design In the beginning OSes were runtime libraries The OS was just code you linked with your program and loaded
More informationChapter 2: OS Overview
Chapter 2: OS Overview CmSc 335 Operating Systems 1. Operating system objectives and functions Operating systems control and support the usage of computer systems. a. usage users of a computer system:
More informationGPU Parallel Computing Architecture and CUDA Programming Model
GPU Parallel Computing Architecture and CUDA Programming Model John Nickolls Outline Why GPU Computing? GPU Computing Architecture Multithreading and Arrays Data Parallel Problem Decomposition Parallel
More informationModule 8. Industrial Embedded and Communication Systems. Version 2 EE IIT, Kharagpur 1
Module 8 Industrial Embedded and Communication Systems Version 2 EE IIT, Kharagpur 1 Lesson 37 Real-Time Operating Systems: Introduction and Process Management Version 2 EE IIT, Kharagpur 2 Instructional
More informationChapter 3 Operating-System Structures
Contents 1. Introduction 2. Computer-System Structures 3. Operating-System Structures 4. Processes 5. Threads 6. CPU Scheduling 7. Process Synchronization 8. Deadlocks 9. Memory Management 10. Virtual
More informationOperating Systems Lecture #6: Process Management
Lecture #6: Process Written by based on the lecture series of Dr. Dayou Li and the book Understanding 4th ed. by I.M.Flynn and A.McIver McHoes (2006) Department of Computer Science and Technology,., 2013
More informationWhy Threads Are A Bad Idea (for most purposes)
Why Threads Are A Bad Idea (for most purposes) John Ousterhout Sun Microsystems Laboratories john.ousterhout@eng.sun.com http://www.sunlabs.com/~ouster Introduction Threads: Grew up in OS world (processes).
More informationStream Processing on GPUs Using Distributed Multimedia Middleware
Stream Processing on GPUs Using Distributed Multimedia Middleware Michael Repplinger 1,2, and Philipp Slusallek 1,2 1 Computer Graphics Lab, Saarland University, Saarbrücken, Germany 2 German Research
More informationserious tools for serious apps
524028-2 Label.indd 1 serious tools for serious apps Real-Time Debugging Real-Time Linux Debugging and Analysis Tools Deterministic multi-core debugging, monitoring, tracing and scheduling Ideal for time-critical
More information