DOCSLIB.ORG

Performance Profiling in a Virtualized Environment

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Performance Profiling in a Virtualized Environment Performance Profiling in a Virtualized Environment Jiaqing Du+, Nipun Sehrawat*, Willy Zwaenepoel+ +EPFL, Switzerland *IIT Guwaha, India Virtualizaon‐based Clouds • Diverse implementaons – soware: Xen, KVM, VMware, … – hardware: Intel x86, AMD x86, PowerPC, … • Opportunies for performance profiling & tuning – public cloud: guest OS & applicaons – private cloud: whole soware stack Profilers based on CPU performance counters do not work well with virtual machines. Jiaqing Du, HotCloud, June 22, 2010 2 Profiling in a Virtualized Environment • General challenge • XenOProf: profiling in the VMM – only for paravirtualizaon‐based Xen – require accesses to the VMM • Profiling in the guest – normally no results – “OProfile can't work with VMware when using performance counter interface. ” Jiaqing Du, HotCloud, June 22, 2010 3 Performance Profiling • Understand runme behavior • Tune performance • Mature & used extensively – VTune, OProfile, … %CYCLE Funcon Module 98.5529 vmx_vcpu_run kvm‐intel.ko 0.2226 (no symbols) libc.so 0.1034 hpet_cpuhp_nofy vmlinux 0.1034 nave_patch vmlinux Jiaqing Du, HotCloud, June 22, 2010 4 Nave Profiling • Performance monitoring unit (PMU) – a set of event counters – generate an interrupt when a counter overflows • PMU‐based profiler User control process – sampling configuraon Kernel – sample collecon Module Interpret* – sample interpretaon Configure Collect CPU PMU Jiaqing Du, HotCloud, June 22, 2010 5 Guest‐wide Profiling • Expose PMU interfaces to the guest User control process Kernel Module Interpret* Configure Collect VMM CPU PMU Jiaqing Du, HotCloud, June 22, 2010 6 Guest‐wide Profiling • Where to save & restore the registers? • CPU switch – only in‐guest execuon is accounted to the guest guest1 VMM guest2 VMM guest2 save&restore • Domain switch – in‐VMM execuon is also accounted to the guest guest1 VMM guest2 VMM guest2 domain1 domain2 save&restore Jiaqing Du, HotCloud, June 22, 2010 7 Guest‐wide Profiling for KVM • Kernel‐based virtual machine (KVM) – a Linux kernel subsystem – a set of kernel modules + QEMU – built on hardware virtualizaon extensions • Intel VT extensions – provide a list of hardware features – facilitate our implementaon • No modificaons to the guest and the profiler Jiaqing Du, HotCloud, June 22, 2010 8 Profiling Packet Receive • Experiment – push packets to a Linux guest in KVM – run OProfile in the guest – monitor instrucon rerements Linux KVM virtual NIC Linux Hardware Hardware NIC NIC Jiaqing Du, HotCloud, June 22, 2010 9 Profiling Packet Receive CPU Switch Domain Switch INSTR Funcon INSTR Funcon 167 csum_paral 2261 cp_interrupt 106 csum_paral_copy_generic 1336 cp_rx_poll 74 copy_to_user 1034 cp_start_xmit 47 ipt_do_table 421 nave_apic_mem_write 38 tcp_v4_rcv 374 nave_apic_mem_read … … 191 csum_paral 19 cp_rx_poll 105 csum_paral_copy_generic 6 cp_start_xmit 94 copy_to_user 6 cp_interrupt 79 ipt_do_table 3 nave_apic_mem_write 51 tcp_v4_rcv 1184 counter overflows 7286 counter overflows Jiaqing Du, HotCloud, June 22, 2010 10 Other Things in the Paper • System‐wide profiling – profiling in the VMM – provide full‐scale view: guest + VMM • Virtualizaon techniques – paravirtualizaon – dynamic binary translaon Jiaqing Du, HotCloud, June 22, 2010 11 Conclusions • Profilers do not work well with virtual machines. • We implement guest‐wide profiling in a VMM based on hardware assistance. • Profiling helps understand the real cost of I/O operaons in a guest. Jiaqing Du, HotCloud, June 22, 2010 12 .
Load more

Recommended publications
  • Developer Guide
    Red Hat Enterprise Linux 6 Developer Guide An introduction to application development tools in Red Hat Enterprise Linux 6 Last Updated: 2017-10-20 Red Hat Enterprise Linux 6 Developer Guide An introduction to application development tools in Red Hat Enterprise Linux 6 Robert Krátký Red Hat Customer Content Services [email protected] Don Domingo Red Hat Customer Content Services Jacquelynn East Red Hat Customer Content Services Legal Notice Copyright © 2016 Red Hat, Inc. and others. This document is licensed by Red Hat under the Creative Commons Attribution-ShareAlike 3.0 Unported License. If you distribute this document, or a modified version of it, you must provide attribution to Red Hat, Inc. and provide a link to the original. If the document is modified, all Red Hat trademarks must be removed. Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law. Red Hat, Red Hat Enterprise Linux, the Shadowman logo, JBoss, OpenShift, Fedora, the Infinity logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries. Linux ® is the registered trademark of Linus Torvalds in the United States and other countries. Java ® is a registered trademark of Oracle and/or its affiliates. XFS ® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries. MySQL ® is a registered trademark of MySQL AB in the United States, the European Union and other countries. Node.js ® is an official trademark of Joyent.
    [Show full text]
  • Analysing CMS Software Performance Using Igprof, Oprofile and Callgrind
    Analysing CMS software performance using IgProf, OPro¯le and callgrind L Tuura1, V Innocente2, G Eulisse1 1 Northeastern University, Boston, MA, USA 2 CERN, Geneva, Switzerland Abstract. The CMS experiment at LHC has a very large body of software of its own and uses extensively software from outside the experiment. Understanding the performance of such a complex system is a very challenging task, not the least because there are extremely few developer tools capable of pro¯ling software systems of this scale, or producing useful reports. CMS has mainly used IgProf, valgrind, callgrind and OPro¯le for analysing the performance and memory usage patterns of our software. We describe the challenges, at times rather extreme ones, faced as we've analysed the performance of our software and how we've developed an understanding of the performance features. We outline the key lessons learnt so far and the actions taken to make improvements. We describe why an in-house general pro¯ler tool still ends up besting a number of renowned open-source tools, and the improvements we've made to it in the recent year. 1. The performance optimisation issue The Compact Muon Solenoid (CMS) experiment on the Large Hadron Collider at CERN [1] is expected to start running within a year. The computing requirements for the experiment are immense. For the ¯rst year of operation, 2008, we budget 32.5M SI2k (SPECint 2000) computing capacity. This corresponds to some 2650 servers if translated into the most capable computers currently available to us.1 It is critical CMS on the one hand acquires the resources needed for the planned physics analyses, and on the other hand optimises the software to complete the task with the resources available.
    [Show full text]
  • Understanding the Linux Kernel, 3Rd Edition by Daniel P
    1 Understanding the Linux Kernel, 3rd Edition By Daniel P. Bovet, Marco Cesati ............................................... Publisher: O'Reilly Pub Date: November 2005 ISBN: 0-596-00565-2 Pages: 942 Table of Contents | Index In order to thoroughly understand what makes Linux tick and why it works so well on a wide variety of systems, you need to delve deep into the heart of the kernel. The kernel handles all interactions between the CPU and the external world, and determines which programs will share processor time, in what order. It manages limited memory so well that hundreds of processes can share the system efficiently, and expertly organizes data transfers so that the CPU isn't kept waiting any longer than necessary for the relatively slow disks. The third edition of Understanding the Linux Kernel takes you on a guided tour of the most significant data structures, algorithms, and programming tricks used in the kernel. Probing beyond superficial features, the authors offer valuable insights to people who want to know how things really work inside their machine. Important Intel-specific features are discussed. Relevant segments of code are dissected line by line. But the book covers more than just the functioning of the code; it explains the theoretical underpinnings of why Linux does things the way it does. This edition of the book covers Version 2.6, which has seen significant changes to nearly every kernel subsystem, particularly in the areas of memory management and block devices. The book focuses on the following topics: • Memory management, including file buffering, process swapping, and Direct memory Access (DMA) • The Virtual Filesystem layer and the Second and Third Extended Filesystems • Process creation and scheduling • Signals, interrupts, and the essential interfaces to device drivers • Timing • Synchronization within the kernel • Interprocess Communication (IPC) • Program execution Understanding the Linux Kernel will acquaint you with all the inner workings of Linux, but it's more than just an academic exercise.
    [Show full text]
  • Best Practice Guide - Generic X86 Vegard Eide, NTNU Nikos Anastopoulos, GRNET Henrik Nagel, NTNU 02-05-2013
    Best Practice Guide - Generic x86 Vegard Eide, NTNU Nikos Anastopoulos, GRNET Henrik Nagel, NTNU 02-05-2013 1 Best Practice Guide - Generic x86 Table of Contents 1. Introduction .............................................................................................................................. 3 2. x86 - Basic Properties ................................................................................................................ 3 2.1. Basic Properties .............................................................................................................. 3 2.2. Simultaneous Multithreading ............................................................................................. 4 3. Programming Environment ......................................................................................................... 5 3.1. Modules ........................................................................................................................ 5 3.2. Compiling ..................................................................................................................... 6 3.2.1. Compilers ........................................................................................................... 6 3.2.2. General Compiler Flags ......................................................................................... 6 3.2.2.1. GCC ........................................................................................................ 6 3.2.2.2. Intel ........................................................................................................
    [Show full text]
  • Pipenightdreams Osgcal-Doc Mumudvb Mpg123-Alsa Tbb
    pipenightdreams osgcal-doc mumudvb mpg123-alsa tbb-examples libgammu4-dbg gcc-4.1-doc snort-rules-default davical cutmp3 libevolution5.0-cil aspell-am python-gobject-doc openoffice.org-l10n-mn libc6-xen xserver-xorg trophy-data t38modem pioneers-console libnb-platform10-java libgtkglext1-ruby libboost-wave1.39-dev drgenius bfbtester libchromexvmcpro1 isdnutils-xtools ubuntuone-client openoffice.org2-math openoffice.org-l10n-lt lsb-cxx-ia32 kdeartwork-emoticons-kde4 wmpuzzle trafshow python-plplot lx-gdb link-monitor-applet libscm-dev liblog-agent-logger-perl libccrtp-doc libclass-throwable-perl kde-i18n-csb jack-jconv hamradio-menus coinor-libvol-doc msx-emulator bitbake nabi language-pack-gnome-zh libpaperg popularity-contest xracer-tools xfont-nexus opendrim-lmp-baseserver libvorbisfile-ruby liblinebreak-doc libgfcui-2.0-0c2a-dbg libblacs-mpi-dev dict-freedict-spa-eng blender-ogrexml aspell-da x11-apps openoffice.org-l10n-lv openoffice.org-l10n-nl pnmtopng libodbcinstq1 libhsqldb-java-doc libmono-addins-gui0.2-cil sg3-utils linux-backports-modules-alsa-2.6.31-19-generic yorick-yeti-gsl python-pymssql plasma-widget-cpuload mcpp gpsim-lcd cl-csv libhtml-clean-perl asterisk-dbg apt-dater-dbg libgnome-mag1-dev language-pack-gnome-yo python-crypto svn-autoreleasedeb sugar-terminal-activity mii-diag maria-doc libplexus-component-api-java-doc libhugs-hgl-bundled libchipcard-libgwenhywfar47-plugins libghc6-random-dev freefem3d ezmlm cakephp-scripts aspell-ar ara-byte not+sparc openoffice.org-l10n-nn linux-backports-modules-karmic-generic-pae
    [Show full text]
  • Improving the Approach to Linux Performance Analysis an Analyst Point of View
    Improving the Approach to Linux Performance Analysis An analyst point of view Jose Santos Guanglei Li IBM’s Linux Technology Center IBM’s China Development Lab [email protected] [email protected] Abstract 1 Introduction In-depth Linux kernel performance analysis As the complexity of the Linux kernel in- and debugging historically has been a focus creases, so does the complexity of the prob- which required resident kernel hackers. This lems that impact performance on a produc- effort not only required deep kernel knowledge tion system. In addition, the hardware sys- to analyze the code, but also required program- tems on which Linux runs today is also increas- ming skills in order to modify the code, re-build ing in complexity, scale, and size. Customers the kernel, re-boot and extract and format the are also running more robust and challenging information from the system. workloads in production environments and are A variety of powerful Linux system tools expecting more reliability, stability, and perfor- are emerging which provide significantly more mance from the underlying operating system flexibility for the analyst and the system owner. and hardware platform. The end result of all This paper highlights an example set of per- this complexity is that new performance bar- formance problems which are often seen on a riers continue to emerge which in many cases system, and focuses on the methodology, ap- are increasingly difficult to analyze and under- proach, and steps that can be used to address stand. each problem. In the past, many of these problems required the Examples of a set of pre-defined SystemTAP expertise of kernel developers to create special- “tapsets” are provided which make it easier ized one-off tools that were specific to the prob- for the non-programmer to extract information lem at hand and are of little use to other prob- about kernel events from a running system, ef- lems.
    [Show full text]
  • Performance Tuning
    Computer Architecture and Performance Tuning Understanding performance tuning Andrzej Nowak CERN openlab CERN openlab Summer Student Lectures 2008 Understanding Performance Tuning Contents 1. Software performance tuning in general 2. Drilling down on performance figures 3. General tips In this talk, we focus on x86_64 processors (Intel Core and friends, AMD Athlon/Barcelona, etc) 2 Andrzej Nowak – CERN Understanding Performance Tuning Performance tuning in general 3 Andrzej Nowak – CERN Understanding Performance Tuning Improving application performance Question #1 – “Why is it SOOOOOO SLOW?” Exchanging hardware Removing common bottlenecks New CPU, new hard drive, more memory New, new, new… Replacing whole software components Replacing shared or external libraries Improving existing code Performance monitoring will give you the answer It allows you to find the things you could change in your setup to improve performance 4 Andrzej Nowak – CERN Understanding Performance Tuning Performance tuning Why tune performance? To get more speed and/or throughput… …or to just keep up with the hardware or previous performance figures Processor clock frequencies don’t go up anymore! No free meals since the millennium Who needs performance tuning? Who can do performance tuning? Some bottlenecks are really easy to find… … but performance tuning can be VERY tricky Performance tuning is a lot like tuning a car… but you can do well with only one wrench and you don’t need all those expensive parts 5 Andrzej Nowak – CERN Understanding Performance Tuning Performance tuning levels Source code Function calls Excessive calls of a function or a group of functions Blocking (i.e. I/O) Loops within your program Iterating over sparse/long structures General characteristics of your program Excessive memory allocations and copying, excessive calculations, checks, malformed conditions, etc.
    [Show full text]
  • Perf Tool: Performance Analysis Tool for Linux
    Performance Measurements: Perf Tool /************************************************************************************ ** Notes on Linux perf tool ** ** Intended audience: Those who would like to learn more about ** Linux perf performance analysis and profiling tool. ** ** Used: CPE 631 Advanced Computer Systems and Architectures ** CPE 619 Modeling and Analysis of Computer and Communication Systems ** ** ver 0.1, Spring 2012 ** ver 0.2, Spring 2018 ** ver 0.3, Spring 2021 ** ** @Aleksandar Milenkovic, [email protected] ************************************************************************************/ Perf Tool: Performance Analysis Tool for Linux 1. Introduction Perf is a profiler tool for Linux 2.6+ based systems that abstracts away CPU hardware differences in Linux performance measurements and presents a simple command line interface. It covers hardware level (CPU/PMU, Performance Monitoring Unit) features and software features (software counters, tracepoints) as well. To learn more about perf type in man perf. <<~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -bash-4.2$ man perf -bash-4.2$ $ man perf-stat -bash-4.2$ $ man perf-top ... # Note before the start, do the following to enable devtoolset-6 -bash-4.2$ scl enable devtoolset-6 bash # You can verify that you are using the right environment -bash-4.2$ bash-4.2$ which gcc /opt/rh/devtoolset-6/root/usr/bin/gcc ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~>> 2. Commands The perf tool offers a rich set of commands to collect and analyze performance and trace data. The command
    [Show full text]
  • Technical Notes All Changes in Fedora 13
    Fedora 13 Technical Notes All changes in Fedora 13 Edited by The Fedora Docs Team Copyright © 2010 Red Hat, Inc. and others. The text of and illustrations in this document are licensed by Red Hat under a Creative Commons Attribution–Share Alike 3.0 Unported license ("CC-BY-SA"). An explanation of CC-BY-SA is available at http://creativecommons.org/licenses/by-sa/3.0/. The original authors of this document, and Red Hat, designate the Fedora Project as the "Attribution Party" for purposes of CC-BY-SA. In accordance with CC-BY-SA, if you distribute this document or an adaptation of it, you must provide the URL for the original version. Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law. Red Hat, Red Hat Enterprise Linux, the Shadowman logo, JBoss, MetaMatrix, Fedora, the Infinity Logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries. For guidelines on the permitted uses of the Fedora trademarks, refer to https:// fedoraproject.org/wiki/Legal:Trademark_guidelines. Linux® is the registered trademark of Linus Torvalds in the United States and other countries. Java® is a registered trademark of Oracle and/or its affiliates. XFS® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries. All other trademarks are the property of their respective owners. Abstract This document lists all changed packages between Fedora 12 and Fedora 13.
    [Show full text]
  • Gerbes-Automatic Profiling for Climate Modeling.Pdf
    Automatic Profiling for Climate Modeling Anja Gerbes1, Nabeeh Jumah2, Julian Kunkel3 1Center for Scientific Computing (CSC) 2Universität Hamburg 3Deutsches Klimarechenzentrum (DKRZ) ABSTRACT PROFILER Some applications are time consuming like climate modeling, which include lengthy simulations. • CPU Profiling is necessary to understand CPU • LIKWID is not build on top of the Linux perf Hence, coding is sensitive for performance. Spending more time on optimization of specific code usage quickly and completely interface; this allows LIKWID to provide full parts can improve total performance. Profiling an application is a well-known technique to do that. support for new architectures quick and inde- • Perf, Oprofile and LIKWID are a performance pendent from special kernel versions. Many tools are available for developers to get performance information about their code. With our measurement and profiling system to identify provided python package Performance Analysis and Source-Code Instrumentation Toolsuite (PASCIT) is and analyze the hot spots. • LIKWID supports hardware based events and an automatic instrumentation of an user’s source code possible. Developers mark the parts that they metrics to each hardware counter, this is more need performance information about. • Perf, Oprofile and LIKWID follows the UNIX than just CPU clock cycles. design philosophy of „one task, one tool“. We present an effort to profile climate modeling codes with two alternative methods. • Compared LIKWID to Oprofile • usage of GGDML translation tool to mark directly the computational kernels of an application for • Typical questions during performance analysis: – Oprofile does not require any special compi- profiling. – Which parts of the program take the most ex- lation flags, or even recompiled code.
    [Show full text]
  • Hardware Performance Counters (HWPMC)
    Advanced Operating System: Hardware Performance Counters (HWPMC) Dr Robert N. M. Watson 2020-2021 Hardware performance counters are a processor facility that gathers statistics about architectural and micro- architectural performance properties of code execution and data access: Architectural features are those exposed explicitly via the documented instruction set and device interfaces – e.g., the number of instructions executed, or the number of load instructions executed. Micro-architectural features have to do with the programmer-transparent implementation details, such as pipelin- ing, superscalar execution, caches, and so on – e.g., the number of L2 cache misses taken. The scope for programmer transparency (e.g., what is included in the architecture vs. micro-architecture) varies by Instruction-Set Architecture (ISA): whereas MIPS exposes certain pipelining effects to the programmer (e.g., branch-delay slots), ARM and x86 minimise the visible exposure other than performance impact. MIPS and ARM both require explicit cache management by the operating system during I/O operations and code loading, whereas x86 also masks those behaviours. Although this distinction between architectural and microarchitectural events seems clear, in fact life is more tricky. In some microarchitectures (especially superscalar ones), tracking committed instructions can affect area and frequency – e.g., by requiring the full program counter (PC) or instruction encoding to be plumbed through the implementation where the design might not otherwise require it. As a result, counters for architectural events (e.g., loads and stores) might in fact be for speculated rather than committed instructions. When the processor is predicting accurately, committed instruction count will dominate incorrectly inspected instruction count, leading the two to be largely the same.
    [Show full text]
  • ARM Cortex-A Series Programmer's Guide
    ARM® Cortex™-A Series Version: 4.0 Programmer’s Guide Copyright © 2011 – 2013 ARM. All rights reserved. ARM DEN0013D (ID012214) ARM Cortex-A Series Programmer’s Guide Copyright © 2011 – 2013 ARM. All rights reserved. Release Information The following changes have been made to this book. Change history Date Issue Confidentiality Change 25 March 2011 A Non-Confidential First release 10 August 2011 B Non-Confidential Second release. Updated to include Virtualization, Cortex-A15 processor, and LPAE. Corrected and revised throughout 25 June 2012 C Non-Confidential Updated to include Cortex-A7 processor, and big.LITTLE. Index added. Corrected and revised throughout. 22 January 2014 D Non-Confidential Updated to include Cortex-A12 processor, Cache Coherent Interconnect, expanded GIC coverage, Multi-core processors, Corrected and revised throughout. Proprietary Notice This Cortex-A Series Programmer’s Guide is protected by copyright and the practice or implementation of the information herein may be protected by one or more patents or pending applications. No part of this Cortex-A Series Programmer’s Guide may be reproduced in any form by any means without the express prior written permission of ARM. No license, express or implied, by estoppel or otherwise to any intellectual property rights is granted by this Cortex-A Series Programmer’s Guide. Your access to the information in this Cortex-A Series Programmer’s Guide is conditional upon your acceptance that you will not use or permit others to use the information for the purposes of determining whether implementations of the information herein infringe any third party patents. This Cortex-A Series Programmer’s Guide is provided “as is”.
    [Show full text]