Schedule: Sunday
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Performance Counters in Htop 3.0
Slide: [ ] Talk: Perf counters in htop 3.0 Presenter: https://hisham.hm PID USER PRI NI VIRT RES SHR S CPU% MEM% TIME+ Command Performance counters in htop 3.0 Hisham Muhammad @[email protected] https://hisham.hm Slide: [ 2 ] Date: 2018-08-25 Talk: Perf counters in htop 3.0 Presenter: https://hisham.hm PID USER PRI NI VIRT RES SHR S CPU% MEM% TIME+ Command About me original author of htop, a project started in 2004 http://hisham.hm/htop/ lead dev of LuaRocks, package manager for Lua http://luarocks.org/ co-founder of the GoboLinux distribution http://gobolinux.org/ developer at Kong – FLOSS API gateway http://getkong.org/ (we’re hiring!) Slide: [ 3 ] Date: 2018-08-25 Talk: Perf counters in htop 3.0 Presenter: https://hisham.hm PID USER PRI NI VIRT RES SHR S CPU% MEM% TIME+ Command What is htop an interactive process manager intended to be “a better top” by this all I originally meant was: scrolling! (versions of top improved a lot since!) Slide: [ 4 ] Date: 2018-08-25 Talk: Perf counters in htop 3.0 Presenter: https://hisham.hm PID USER PRI NI VIRT RES SHR S CPU% MEM% TIME+ Command Hello, htop! Slide: [ 5 ] Date: 2018-08-25 Talk: Perf counters in htop 3.0 Presenter: https://hisham.hm PID USER PRI NI VIRT RES SHR S CPU% MEM% TIME+ Command htop beyond Linux Linux MacOS FreeBSD OpenBSD DragonFlyBSD Solaris (illumos) Slide: [ 6 ] Date: 2018-08-25 Talk: Perf counters in htop 3.0 Presenter: https://hisham.hm PID USER PRI NI VIRT RES SHR S CPU% MEM% TIME+ Command Then Apple released a broken kernel.. -
20 Linux System Monitoring Tools Every Sysadmin Should Know by Nixcraft on June 27, 2009 · 315 Comments · Last Updated November 6, 2012
About Forum Howtos & FAQs Low graphics Shell Scripts RSS/Feed nixcraft - insight into linux admin work 20 Linux System Monitoring Tools Every SysAdmin Should Know by nixCraft on June 27, 2009 · 315 comments · Last updated November 6, 2012 Need to monitor Linux server performance? Try these built-in commands and a few add-on tools. Most Linux distributions are equipped with tons of monitoring. These tools provide metrics which can be used to get information about system activities. You can use these tools to find the possible causes of a performance problem. The commands discussed below are some of the most basic commands when it comes to system analysis and debugging server issues such as: 1. Finding out bottlenecks. 2. Disk (storage) bottlenecks. 3. CPU and memory bottlenecks. 4. Network bottlenecks. #1: top - Process Activity Command The top program provides a dynamic real-time view of a running system i.e. actual process activity. By default, it displays the most CPU-intensive tasks running on the server and updates the list every five seconds. Fig.01: Linux top command Commonly Used Hot Keys The top command provides several useful hot keys: Hot Usage Key t Displays summary information off and on. m Displays memory information off and on. Sorts the display by top consumers of various system resources. Useful for quick identification of performance- A hungry tasks on a system. f Enters an interactive configuration screen for top. Helpful for setting up top for a specific task. o Enables you to interactively select the ordering within top. r Issues renice command. -
An Introduction to Linux IPC
An introduction to Linux IPC Michael Kerrisk © 2013 linux.conf.au 2013 http://man7.org/ Canberra, Australia [email protected] 2013-01-30 http://lwn.net/ [email protected] man7 .org 1 Goal ● Limited time! ● Get a flavor of main IPC methods man7 .org 2 Me ● Programming on UNIX & Linux since 1987 ● Linux man-pages maintainer ● http://www.kernel.org/doc/man-pages/ ● Kernel + glibc API ● Author of: Further info: http://man7.org/tlpi/ man7 .org 3 You ● Can read a bit of C ● Have a passing familiarity with common syscalls ● fork(), open(), read(), write() man7 .org 4 There’s a lot of IPC ● Pipes ● Shared memory mappings ● FIFOs ● File vs Anonymous ● Cross-memory attach ● Pseudoterminals ● proc_vm_readv() / proc_vm_writev() ● Sockets ● Signals ● Stream vs Datagram (vs Seq. packet) ● Standard, Realtime ● UNIX vs Internet domain ● Eventfd ● POSIX message queues ● Futexes ● POSIX shared memory ● Record locks ● ● POSIX semaphores File locks ● ● Named, Unnamed Mutexes ● System V message queues ● Condition variables ● System V shared memory ● Barriers ● ● System V semaphores Read-write locks man7 .org 5 It helps to classify ● Pipes ● Shared memory mappings ● FIFOs ● File vs Anonymous ● Cross-memory attach ● Pseudoterminals ● proc_vm_readv() / proc_vm_writev() ● Sockets ● Signals ● Stream vs Datagram (vs Seq. packet) ● Standard, Realtime ● UNIX vs Internet domain ● Eventfd ● POSIX message queues ● Futexes ● POSIX shared memory ● Record locks ● ● POSIX semaphores File locks ● ● Named, Unnamed Mutexes ● System V message queues ● Condition variables ● System V shared memory ● Barriers ● ● System V semaphores Read-write locks man7 .org 6 It helps to classify ● Pipes ● Shared memory mappings ● FIFOs ● File vs Anonymous ● Cross-memoryn attach ● Pseudoterminals tio a ● proc_vm_readv() / proc_vm_writev() ● Sockets ic n ● Signals ● Stream vs Datagram (vs uSeq. -
FOSDEM 2017 Schedule
FOSDEM 2017 - Saturday 2017-02-04 (1/9) Janson K.1.105 (La H.2215 (Ferrer) H.1301 (Cornil) H.1302 (Depage) H.1308 (Rolin) H.1309 (Van Rijn) H.2111 H.2213 H.2214 H.3227 H.3228 Fontaine)… 09:30 Welcome to FOSDEM 2017 09:45 10:00 Kubernetes on the road to GIFEE 10:15 10:30 Welcome to the Legal Python Winding Itself MySQL & Friends Opening Intro to Graph … Around Datacubes Devroom databases Free/open source Portability of containers software and drones Optimizing MySQL across diverse HPC 10:45 without SQL or touching resources with my.cnf Singularity Welcome! 11:00 Software Heritage The Veripeditus AR Let's talk about The State of OpenJDK MSS - Software for The birth of HPC Cuba Game Framework hardware: The POWER Make your Corporate planning research Applying profilers to of open. CLA easy to use, aircraft missions MySQL Using graph databases please! 11:15 in popular open source CMSs 11:30 Jockeying the Jigsaw The power of duck Instrumenting plugins Optimized and Mixed License FOSS typing and linear for Performance reproducible HPC Projects algrebra Schema Software deployment 11:45 Incremental Graph Queries with 12:00 CloudABI LoRaWAN for exploring Open J9 - The Next Free It's time for datetime Reproducible HPC openCypher the Internet of Things Java VM sysbench 1.0: teaching Software Installation on an old dog new tricks Cray Systems with EasyBuild 12:15 Making License 12:30 Compliance Easy: Step Diagnosing Issues in Webpush notifications Putting Your Jobs Under Twitter Streaming by Open Source Step. Java Apps using for Kinto Introducing gh-ost the Microscope using Graph with Gephi Thermostat and OGRT Byteman. -
Linux and Free Software: What and Why?
Linux and Free Software: What and Why? (Qué son Linux y el Software libre y cómo beneficia su uso a las empresas para lograr productividad económica y ventajas técnicas?) JugoJugo CreativoCreativo Michael Kerrisk UniversidadUniversidad dede SantanderSantander UDESUDES © 2012 Bucaramanga,Bucaramanga, ColombiaColombia [email protected] 77 JuneJune 20122012 http://man7.org/ man7.org 1 Who am I? ● Programmer, educator, and writer ● UNIX since 1987; Linux since late 1990s ● Linux man-pages maintainer since 2004 ● Author of a book on Linux programming man7.org 2 Overview ● What is Linux? ● How are Linux and Free Software created? ● History ● Where is Linux used today? ● What is Free Software? ● Source code; Software licensing ● Importance and advantages of Free Software and Software Freedom ● Concluding remarks man7.org 3 ● What is Linux? ● How are Linux and Free Software created? ● History ● Where is Linux used today? ● What is Free Software? ● Source code; Software licensing ● Importance and advantages of Free Software and Software Freedom ● Concluding remarks man7.org 4 What is Linux? ● An operating system (sistema operativo) ● (Operating System = OS) ● Examples of other operating systems: ● Windows ● Mac OS X Penguins are the Linux mascot man7.org 5 But, what's an operating system? ● Two definitions: ● Kernel ● Kernel + package of common programs man7.org 6 OS Definition 1: Kernel ● Computer scientists' definition: ● Operating System = Kernel (núcleo) ● Kernel = fundamental program on which all other programs depend man7.org 7 Programs can live -
Linux Kernel and Driver Development Training Slides
Linux Kernel and Driver Development Training Linux Kernel and Driver Development Training © Copyright 2004-2021, Bootlin. Creative Commons BY-SA 3.0 license. Latest update: October 9, 2021. Document updates and sources: https://bootlin.com/doc/training/linux-kernel Corrections, suggestions, contributions and translations are welcome! embedded Linux and kernel engineering Send them to [email protected] - Kernel, drivers and embedded Linux - Development, consulting, training and support - https://bootlin.com 1/470 Rights to copy © Copyright 2004-2021, Bootlin License: Creative Commons Attribution - Share Alike 3.0 https://creativecommons.org/licenses/by-sa/3.0/legalcode You are free: I to copy, distribute, display, and perform the work I to make derivative works I to make commercial use of the work Under the following conditions: I Attribution. You must give the original author credit. I Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under a license identical to this one. I For any reuse or distribution, you must make clear to others the license terms of this work. I Any of these conditions can be waived if you get permission from the copyright holder. Your fair use and other rights are in no way affected by the above. Document sources: https://github.com/bootlin/training-materials/ - Kernel, drivers and embedded Linux - Development, consulting, training and support - https://bootlin.com 2/470 Hyperlinks in the document There are many hyperlinks in the document I Regular hyperlinks: https://kernel.org/ I Kernel documentation links: dev-tools/kasan I Links to kernel source files and directories: drivers/input/ include/linux/fb.h I Links to the declarations, definitions and instances of kernel symbols (functions, types, data, structures): platform_get_irq() GFP_KERNEL struct file_operations - Kernel, drivers and embedded Linux - Development, consulting, training and support - https://bootlin.com 3/470 Company at a glance I Engineering company created in 2004, named ”Free Electrons” until Feb. -
Lab 09: Operating System Basics
ESE 150 – Lab 09: Operating System Basics LAB 09 Today’s Lab has the following objectives: 1. Learn how to remotely log into Eniac Linux server 2. Learn some of the basics of process management on the Linux Operating System This lab will take place in Ketterer (Moore 200). Background: OPERATING SYSTEMS We learned in lecture that a CPU can really only execute one task or instruction (like ADD or SUBTRACT, etc) at a time. The Operating System is a program that runs on a CPU with the job of managing the CPU’s time. It schedules programs that user’s would like run for time on the CPU, essentially its main job is to keep the CPU busy. Another aspect of the OS is to protect access to the hardware that surrounds the CPU (like input and output devices – keyboards, mice, etc.) so that programs don’t have direct access to the hardware, but instead ask the OS for permission to access it. This also lends itself to “virtualizing” the CPU and its hardware so that each program that runs on the CPU believes it is the only program running on the CPU at any given time. Before the personal computer existed, before Mac OS and Windows came into being, an operating system named UNIX was written to manage large computers at AT&T Bell Laboratories in the 1970s that became a model for modern operating systems (like Windows, and Mac OSX). In the 1990’s an operating system named Linux was invented modeled very heavily on the UNIX operating system. -
Linux Performance Tools
Linux Performance Tools Brendan Gregg Senior Performance Architect Performance Engineering Team [email protected] @brendangregg This Tutorial • A tour of many Linux performance tools – To show you what can be done – With guidance for how to do it • This includes objectives, discussion, live demos – See the video of this tutorial Observability Benchmarking Tuning Stac Tuning • Massive AWS EC2 Linux cloud – 10s of thousands of cloud instances • FreeBSD for content delivery – ~33% of US Internet traffic at night • Over 50M subscribers – Recently launched in ANZ • Use Linux server tools as needed – After cloud monitoring (Atlas, etc.) and instance monitoring (Vector) tools Agenda • Methodologies • Tools • Tool Types: – Observability – Benchmarking – Tuning – Static • Profiling • Tracing Methodologies Methodologies • Objectives: – Recognize the Streetlight Anti-Method – Perform the Workload Characterization Method – Perform the USE Method – Learn how to start with the questions, before using tools – Be aware of other methodologies My system is slow… DEMO & DISCUSSION Methodologies • There are dozens of performance tools for Linux – Packages: sysstat, procps, coreutils, … – Commercial products • Methodologies can provide guidance for choosing and using tools effectively • A starting point, a process, and an ending point An#-Methodologies • The lack of a deliberate methodology… Street Light An<-Method 1. Pick observability tools that are: – Familiar – Found on the Internet – Found at random 2. Run tools 3. Look for obvious issues Drunk Man An<-Method • Tune things at random until the problem goes away Blame Someone Else An<-Method 1. Find a system or environment component you are not responsible for 2. Hypothesize that the issue is with that component 3. Redirect the issue to the responsible team 4. -
Control Groups (Cgroups)
System Programming for Linux Containers Control Groups (cgroups) Michael Kerrisk, man7.org © 2020 [email protected] February 2020 Outline 19 Cgroups 19-1 19.1 Introduction to cgroups v1 and v2 19-3 19.2 Cgroups v1: hierarchies and controllers 19-17 19.3 Cgroups v1: populating a cgroup 19-24 19.4 Cgroups v1: release notification 19-33 19.5 Cgroups v1: a survey of the controllers 19-43 19.6 Cgroups /procfiles 19-65 19.7 Cgroup namespaces 19-68 Outline 19 Cgroups 19-1 19.1 Introduction to cgroups v1 and v2 19-3 19.2 Cgroups v1: hierarchies and controllers 19-17 19.3 Cgroups v1: populating a cgroup 19-24 19.4 Cgroups v1: release notification 19-33 19.5 Cgroups v1: a survey of the controllers 19-43 19.6 Cgroups /procfiles 19-65 19.7 Cgroup namespaces 19-68 Goals Cgroups is a big topic Many controllers V1 versus V2 interfaces Our goal: understand fundamental semantics of cgroup filesystem and interfaces Useful from a programming perspective How do I build container frameworks? What else can I build with cgroups? And useful from a system engineering perspective What’s going on underneath my container’s hood? System Programming for Linux Containers ©2020, Michael Kerrisk Cgroups 19-4 §19.1 Focus We’ll focus on: General principles of operation; goals of cgroups The cgroup filesystem Interacting with the cgroup filesystem using shell commands Problems with cgroups v1, motivations for cgroups v2 Differences between cgroups v1 and v2 We’ll look briefly at some of the controllers System Programming for Linux Containers ©2020, Michael Kerrisk Cgroups 19-5 §19.1 -
Python Workflows on HPC Systems
Python Workflows on HPC Systems Dominik Strassel1; , Philipp Reusch1; and Janis Keuper2;1; 1CC-HPC, Fraunhofer ITWM, Kaiserslautern, Germany 2Institute for Machine Learning and Analytics, Offenburg University,Germany Abstract—The recent successes and wide spread application of phenomenon that regularly causes following GPU-jobs compute intensive machine learning and data analytics methods to crash when these processes are still occupying GPU have been boosting the usage of the Python programming lan- memory. guage on HPC systems. While Python provides many advantages for the users, it has not been designed with a focus on multi- • Python jobs appear to be “escaping” the resource con- user environments or parallel programming - making it quite trol mechanisms of batch systems on a regular basis, challenging to maintain stable and secure Python workflows on a allocating more memory and CPU cores than scheduled HPC system. In this paper, we analyze the key problems induced - hence, affecting other users on the system. by the usage of Python on HPC clusters and sketch appropriate • The maintenance and management of the diverse and workarounds for efficiently maintaining multi-user Python soft- ware environments, securing and restricting resources of Python fast evolving Python software environment is quite jobs and containing Python processes, while focusing on Deep challenging, especially when the needs of a diverse user Learning applications running on GPU clusters. group are contradicting. Index Terms—high performance computing, python, deep This paper presents the intermediate results of our ongoing learning, machine learning, data analytics investigation of causes and possible solutions to these prob- lems in the context of machine learning applications on GPU- I. -
Literature Review and Implementation Overview: High Performance
LITERATURE REVIEW AND IMPLEMENTATION OVERVIEW: HIGH PERFORMANCE COMPUTING WITH GRAPHICS PROCESSING UNITS FOR CLASSROOM AND RESEARCH USE APREPRINT Nathan George Department of Data Sciences Regis University Denver, CO 80221 [email protected] May 18, 2020 ABSTRACT In this report, I discuss the history and current state of GPU HPC systems. Although high-power GPUs have only existed a short time, they have found rapid adoption in deep learning applications. I also discuss an implementation of a commodity-hardware NVIDIA GPU HPC cluster for deep learning research and academic teaching use. Keywords GPU · Neural Networks · Deep Learning · HPC 1 Introduction, Background, and GPU HPC History High performance computing (HPC) is typically characterized by large amounts of memory and processing power. HPC, sometimes also called supercomputing, has been around since the 1960s with the introduction of the CDC STAR-100, and continues to push the limits of computing power and capabilities for large-scale problems [1, 2]. However, use of graphics processing unit (GPU) in HPC supercomputers has only started in the mid to late 2000s [3, 4]. Although graphics processing chips have been around since the 1970s, GPUs were not widely used for computations until the 2000s. During the early 2000s, GPU clusters began to appear for HPC applications. Most of these clusters were designed to run large calculations requiring vast computing power, and many clusters are still designed for that purpose [5]. GPUs have been increasingly used for computations due to their commodification, following Moore’s Law (demonstrated arXiv:2005.07598v1 [cs.DC] 13 May 2020 in Figure 1), and usage in specific applications like neural networks. -
Thread Scheduling in Multi-Core Operating Systems Redha Gouicem
Thread Scheduling in Multi-core Operating Systems Redha Gouicem To cite this version: Redha Gouicem. Thread Scheduling in Multi-core Operating Systems. Computer Science [cs]. Sor- bonne Université, 2020. English. tel-02977242 HAL Id: tel-02977242 https://hal.archives-ouvertes.fr/tel-02977242 Submitted on 24 Oct 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Ph.D thesis in Computer Science Thread Scheduling in Multi-core Operating Systems How to Understand, Improve and Fix your Scheduler Redha GOUICEM Sorbonne Université Laboratoire d’Informatique de Paris 6 Inria Whisper Team PH.D.DEFENSE: 23 October 2020, Paris, France JURYMEMBERS: Mr. Pascal Felber, Full Professor, Université de Neuchâtel Reviewer Mr. Vivien Quéma, Full Professor, Grenoble INP (ENSIMAG) Reviewer Mr. Rachid Guerraoui, Full Professor, École Polytechnique Fédérale de Lausanne Examiner Ms. Karine Heydemann, Associate Professor, Sorbonne Université Examiner Mr. Etienne Rivière, Full Professor, University of Louvain Examiner Mr. Gilles Muller, Senior Research Scientist, Inria Advisor Mr. Julien Sopena, Associate Professor, Sorbonne Université Advisor ABSTRACT In this thesis, we address the problem of schedulers for multi-core architectures from several perspectives: design (simplicity and correct- ness), performance improvement and the development of application- specific schedulers.