Linux Scheduling
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The Different Unix Contexts
The different Unix contexts • User-level • Kernel “top half” - System call, page fault handler, kernel-only process, etc. • Software interrupt • Device interrupt • Timer interrupt (hardclock) • Context switch code Transitions between contexts • User ! top half: syscall, page fault • User/top half ! device/timer interrupt: hardware • Top half ! user/context switch: return • Top half ! context switch: sleep • Context switch ! user/top half Top/bottom half synchronization • Top half kernel procedures can mask interrupts int x = splhigh (); /* ... */ splx (x); • splhigh disables all interrupts, but also splnet, splbio, splsoftnet, . • Masking interrupts in hardware can be expensive - Optimistic implementation – set mask flag on splhigh, check interrupted flag on splx Kernel Synchronization • Need to relinquish CPU when waiting for events - Disk read, network packet arrival, pipe write, signal, etc. • int tsleep(void *ident, int priority, ...); - Switches to another process - ident is arbitrary pointer—e.g., buffer address - priority is priority at which to run when woken up - PCATCH, if ORed into priority, means wake up on signal - Returns 0 if awakened, or ERESTART/EINTR on signal • int wakeup(void *ident); - Awakens all processes sleeping on ident - Restores SPL a time they went to sleep (so fine to sleep at splhigh) Process scheduling • Goal: High throughput - Minimize context switches to avoid wasting CPU, TLB misses, cache misses, even page faults. • Goal: Low latency - People typing at editors want fast response - Network services can be latency-bound, not CPU-bound • BSD time quantum: 1=10 sec (since ∼1980) - Empirically longest tolerable latency - Computers now faster, but job queues also shorter Scheduling algorithms • Round-robin • Priority scheduling • Shortest process next (if you can estimate it) • Fair-Share Schedule (try to be fair at level of users, not processes) Multilevel feeedback queues (BSD) • Every runnable proc. -
Stclang: State Thread Composition As a Foundation for Monadic Dataflow Parallelism Sebastian Ertel∗ Justus Adam Norman A
STCLang: State Thread Composition as a Foundation for Monadic Dataflow Parallelism Sebastian Ertel∗ Justus Adam Norman A. Rink Dresden Research Lab Chair for Compiler Construction Chair for Compiler Construction Huawei Technologies Technische Universität Dresden Technische Universität Dresden Dresden, Germany Dresden, Germany Dresden, Germany [email protected] [email protected] [email protected] Andrés Goens Jeronimo Castrillon Chair for Compiler Construction Chair for Compiler Construction Technische Universität Dresden Technische Universität Dresden Dresden, Germany Dresden, Germany [email protected] [email protected] Abstract using monad-par and LVars to expose parallelism explicitly Dataflow execution models are used to build highly scalable and reach the same level of performance, showing that our parallel systems. A programming model that targets parallel programming model successfully extracts parallelism that dataflow execution must answer the following question: How is present in an algorithm. Further evaluation shows that can parallelism between two dependent nodes in a dataflow smap is expressive enough to implement parallel reductions graph be exploited? This is difficult when the dataflow lan- and our programming model resolves short-comings of the guage or programming model is implemented by a monad, stream-based programming model for current state-of-the- as is common in the functional community, since express- art big data processing systems. ing dependence between nodes by a monadic bind suggests CCS Concepts • Software and its engineering → Func- sequential execution. Even in monadic constructs that explic- tional languages. itly separate state from computation, problems arise due to the need to reason about opaquely defined state. -
Backup and Restore Cisco Prime Collaboration Provisioning
Backup and Restore Cisco Prime Collaboration Provisioning This section explains the following: • Perform Backup and Restore, page 1 • Back Up the Single-Machine Provisioning Database, page 2 • Restore the Single-Machine Provisioning Database, page 3 • Schedule Backup Using the Provisioning User Interface, page 5 Perform Backup and Restore Cisco Prime Collaboration Provisioning allows you to backup your data and restore it. You can schedule periodic backups using the Provisioning UI (Schedule Backup Using the Provisioning User Interface, on page 5). There are two backup and restore scenarios; select the set of procedures that matches your scenario: • Backup and restore on a single machine, with the same installation or a new installation. For this scenario, see Schedule Backup Using the Provisioning User Interface, on page 5. Note When backing up files, you should place the files on a different file server. Also, you should burn the backup data onto a CD. Cisco Prime Collaboration Provisioning allows you to back up system data and restore it on a different system in the event of total system failure. To restore the backup from another system, the following prerequisites must be met: • Ensure that the server to which data is restored has the same MAC address as that of the system that was backed up (the IP address and the hostname can be different). • If you are unable to assign the MAC address of the original system (the one that was backed up) to another system, contact the Engineering Team for information on a new license file (for a new MAC address). Cisco Prime Collaboration Provisioning Install and Upgrade Guide, 12.3 1 Backup and Restore Cisco Prime Collaboration Provisioning Back Up the Single-Machine Provisioning Database • The procedure to backup and restore data on a different system is the same as the procedure to backup and restore data on the same system. -
FIFI-LS Highlights Robert Minchin FIFI-LS Highlights
FIFI-LS Highlights Robert Minchin FIFI-LS Highlights • Nice face-on galaxies where [CII] traces star formation [CII] traces star formation in M51 Pineda et al. 2018 [CII] in NGC 6946 Bigiel et al. 2020 [CII] particularly important as H2 & SFR tracer in inter-arm regions FIFI-LS Highlights • Nice face-on galaxies where [CII] traces star formation • Galaxies where [CII] doesn’t trace star formation [CII] from shocks & turbulence in NGC 4258 • [CII] seen along X-ray/radio ‘arms’ of NGC 4258, associated with past or present jet activity • [CII] excited by shocks and turbulence associated with the jet impacting the disk • NOT star formation Appleton et al. 2018 Excess [CII] in HE 1353-1917 1011 HE 1353-1917 1% MUSE RGB Images: 0.1 % • HE 1353-1917 1010 iband[OIII] Hα 0.02 % has a large 109 excess in HE 0433-1028 L[CII]/LFIR 8 10 excess 1σ 10 × 2σ /L 3C 326 3σ HE 1029-1831 • AGN ionization [CII] 7 10 HE 1108-2813 L cone intercepts HE 2211-3903 106 the cold SOFIA AGN Hosts galactic disk AGN Hosts High z 105 LINERs [Brisbin+15] Normal Galaxies LIRGs ULIRGs [Decarli+18] 104 108 109 1010 1011 1012 1013 1014 L /L Smirnova-Pinchukova et al. 2019 FIR Shock-excited [CII] in NGC 2445 [CII] in the ring of NGC 2445 is enhanced compared to PAHs, showing a shock excitement origin rather than star formation Fadda & Appleton, 2020, AAS Meeting 235 FIFI-LS Highlights • Nice face-on galaxies where [CII] traces star formation • Galaxies where [CII] doesn’t trace star formation • Galaxies with gas away from the plane Extraplanar [CII] in edge-on galaxies • Edge-on galaxies NGC 891 & NGC 5907 observed by Reach et al. -
APPLYING MODEL-VIEW-CONTROLLER (MVC) in DESIGN and DEVELOPMENT of INFORMATION SYSTEMS an Example of Smart Assistive Script Breakdown in an E-Business Application
APPLYING MODEL-VIEW-CONTROLLER (MVC) IN DESIGN AND DEVELOPMENT OF INFORMATION SYSTEMS An Example of Smart Assistive Script Breakdown in an e-Business Application Andreas Holzinger, Karl Heinz Struggl Institute of Information Systems and Computer Media (IICM), TU Graz, Graz, Austria Matjaž Debevc Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia Keywords: Information Systems, Software Design Patterns, Model-view-controller (MVC), Script Breakdown, Film Production. Abstract: Information systems are supporting professionals in all areas of e-Business. In this paper we concentrate on our experiences in the design and development of information systems for the use in film production processes. Professionals working in this area are neither computer experts, nor interested in spending much time for information systems. Consequently, to provide a useful, useable and enjoyable application the system must be extremely suited to the requirements and demands of those professionals. One of the most important tasks at the beginning of a film production is to break down the movie script into its elements and aspects, and create a solid estimate of production costs based on the resulting breakdown data. Several film production software applications provide interfaces to support this task. However, most attempts suffer from numerous usability deficiencies. As a result, many film producers still use script printouts and textmarkers to highlight script elements, and transfer the data manually into their film management software. This paper presents a novel approach for unobtrusive and efficient script breakdown using a new way of breaking down text into its relevant elements. We demonstrate how the implementation of this interface benefits from employing the Model-View-Controller (MVC) as underlying software design paradigm in terms of both software development confidence and user satisfaction. -
Sleep 2.1 Manual
Sleep 2.1 Manual "If you put a million monkeys at a million keyboards, one of them will eventually write a Java program. The rest of them will write Perl programs." -- Anonymous Raphael Mudge Sleep 2.1 Manual Revision: 06.02.08 Released under a Creative Commons Attribution-ShareAlike 3.0 License (see http://creativecommons.org/licenses/by-sa/3.0/us/) You are free: • to Share -- to copy, distribute, display, and perform the work • to Remix -- to make derivative works Under the following conditions: Attribution. You must attribute this work to Raphael Mudge with a link to http://sleep.dashnine.org/ Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under the same, similar or a compatible license. • For any reuse or distribution, you must make clear to others the license terms of this work. The best way to do this is with a link to the license. • Any of the above conditions can be waived if you get permission from the copyright holder. • Apart from the remix rights granted under this license, nothing in this license impairs or restricts the author's moral rights. Your fair use and other rights are in no way affected by the above. Table of Contents Introduction................................................................................................. 1 I. What is Sleep?...................................................................................................1 II. Manual Conventions......................................................................................2 III. -
Parallel Processing Here at the School of Statistics
Parallel Processing here at the School of Statistics Charles J. Geyer School of Statistics University of Minnesota http://www.stat.umn.edu/~charlie/parallel/ 1 • batch processing • R package multicore • R package rlecuyer • R package snow • grid engine (CLA) • clusters (MSI) 2 Batch Processing This is really old stuff (from 1975). But not everyone knows it. If you do the following at a unix prompt nohup nice -n 19 some job & where \some job" is replaced by an actual job, then • the job will run in background (because of &). • the job will not be killed when you log out (because of nohup). • the job will have low priority (because of nice -n 19). 3 Batch Processing (cont.) For example, if foo.R is a plain text file containing R commands, then nohup nice -n 19 R CMD BATCH --vanilla foo.R & executes the commands and puts the printout in the file foo.Rout. And nohup nice -n 19 R CMD BATCH --no-restore foo.R & executes the commands, puts the printout in the file foo.Rout, and saves all created R objects in the file .RData. 4 Batch Processing (cont.) nohup nice -n 19 R CMD BATCH foo.R & is a really bad idea! It reads in all the objects in the file .RData (if one is present) at the beginning. So you have no idea whether the results are reproducible. Always use --vanilla or --no-restore except when debugging. 5 Batch Processing (cont.) This idiom has nothing to do with R. If foo is a compiled C or C++ or Fortran main program that doesn't have command line arguments (or a shell, Perl, Python, or Ruby script), then nohup nice -n 19 foo & runs it. -
Process Scheduling
PROCESS SCHEDULING ANIRUDH JAYAKUMAR LAST TIME • Build a customized Linux Kernel from source • System call implementation • Interrupts and Interrupt Handlers TODAY’S SESSION • Process Management • Process Scheduling PROCESSES • “ a program in execution” • An active program with related resources (instructions and data) • Short lived ( “pwd” executed from terminal) or long-lived (SSH service running as a background process) • A.K.A tasks – the kernel’s point of view • Fundamental abstraction in Unix THREADS • Objects of activity within the process • One or more threads within a process • Asynchronous execution • Each thread includes a unique PC, process stack, and set of processor registers • Kernel schedules individual threads, not processes • tasks are Linux threads (a.k.a kernel threads) TASK REPRESENTATION • The kernel maintains info about each process in a process descriptor, of type task_struct • See include/linux/sched.h • Each task descriptor contains info such as run-state of process, address space, list of open files, process priority etc • The kernel stores the list of processes in a circular doubly linked list called the task list. TASK LIST • struct list_head tasks; • init the "mother of all processes” – statically allocated • extern struct task_struct init_task; • for_each_process() - iterates over the entire task list • next_task() - returns the next task in the list PROCESS STATE • TASK_RUNNING: running or on a run-queue waiting to run • TASK_INTERRUPTIBLE: sleeping, waiting for some event to happen; awakes prematurely if it receives a signal • TASK_UNINTERRUPTIBLE: identical to TASK_INTERRUPTIBLE except it ignores signals • TASK_ZOMBIE: The task has terminated, but its parent has not yet issued a wait4(). The task's process descriptor must remain in case the parent wants to access it. -
Scheduling: Introduction
7 Scheduling: Introduction By now low-level mechanisms of running processes (e.g., context switch- ing) should be clear; if they are not, go back a chapter or two, and read the description of how that stuff works again. However, we have yet to un- derstand the high-level policies that an OS scheduler employs. We will now do just that, presenting a series of scheduling policies (sometimes called disciplines) that various smart and hard-working people have de- veloped over the years. The origins of scheduling, in fact, predate computer systems; early approaches were taken from the field of operations management and ap- plied to computers. This reality should be no surprise: assembly lines and many other human endeavors also require scheduling, and many of the same concerns exist therein, including a laser-like desire for efficiency. And thus, our problem: THE CRUX: HOW TO DEVELOP SCHEDULING POLICY How should we develop a basic framework for thinking about scheduling policies? What are the key assumptions? What metrics are important? What basic approaches have been used in the earliest of com- puter systems? 7.1 Workload Assumptions Before getting into the range of possible policies, let us first make a number of simplifying assumptions about the processes running in the system, sometimes collectively called the workload. Determining the workload is a critical part of building policies, and the more you know about workload, the more fine-tuned your policy can be. The workload assumptions we make here are mostly unrealistic, but that is alright (for now), because we will relax them as we go, and even- tually develop what we will refer to as .. -
Unix Quickref.Dvi
Summary of UNIX commands Table of Contents df [dirname] display free disk space. If dirname is omitted, 1. Directory and file commands 1994,1995,1996 Budi Rahardjo ([email protected]) display all available disks. The output maybe This is a summary of UNIX commands available 2. Print-related commands in blocks or in Kbytes. Use df -k in Solaris. on most UNIX systems. Depending on the config- uration, some of the commands may be unavailable 3. Miscellaneous commands du [dirname] on your site. These commands may be a commer- display disk usage. cial program, freeware or public domain program that 4. Process management must be installed separately, or probably just not in less filename your search path. Check your local documentation or 5. File archive and compression display filename one screenful. A pager similar manual pages for more details (e.g. man program- to (better than) more. 6. Text editors name). This reference card, obviously, cannot de- ls [dirname] scribe all UNIX commands in details, but instead I 7. Mail programs picked commands that are useful and interesting from list the content of directory dirname. Options: a user's point of view. 8. Usnet news -a display hidden files, -l display in long format 9. File transfer and remote access mkdir dirname Disclaimer make directory dirname The author makes no warranty of any kind, expressed 10. X window or implied, including the warranties of merchantabil- more filename 11. Graph, Plot, Image processing tools ity or fitness for a particular purpose, with regard to view file filename one screenfull at a time the use of commands contained in this reference card. -
Scheduling Light-Weight Parallelism in Artcop
Scheduling Light-Weight Parallelism in ArTCoP J. Berthold1,A.AlZain2,andH.-W.Loidl3 1 Fachbereich Mathematik und Informatik Philipps-Universit¨at Marburg, D-35032 Marburg, Germany [email protected] 2 School of Mathematical and Computer Sciences Heriot-Watt University, Edinburgh EH14 4AS, Scotland [email protected] 3 Institut f¨ur Informatik, Ludwig-Maximilians-Universit¨at M¨unchen, Germany [email protected] Abstract. We present the design and prototype implementation of the scheduling component in ArTCoP (architecture transparent control of parallelism), a novel run-time environment (RTE) for parallel execution of high-level languages. A key feature of ArTCoP is its support for deep process and memory hierarchies, shown in the scheduler by supporting light-weight threads. To realise a system with easily exchangeable components, the system defines a micro-kernel, providing basic infrastructure, such as garbage collection. All complex RTE operations, including the handling of parallelism, are implemented at a separate system level. By choosing Concurrent Haskell as high-level system language, we obtain a prototype in the form of an executable specification that is easier to maintain and more flexible than con- ventional RTEs. We demonstrate the flexibility of this approach by presenting implementations of a scheduler for light-weight threads in ArTCoP, based on GHC Version 6.6. Keywords: Parallel computation, functional programming, scheduling. 1 Introduction In trying to exploit the computational power of parallel architectures ranging from multi-core machines to large-scale computational Grids, we are currently developing a new parallel runtime environment, ArTCoP, for executing parallel Haskell code on such complex, hierarchical architectures. -
Scheduling Weakly Consistent C Concurrency for Reconfigurable
SUBMISSION TO IEEE TRANS. ON COMPUTERS 1 Scheduling Weakly Consistent C Concurrency for Reconfigurable Hardware Nadesh Ramanathan, John Wickerson, Member, IEEE, and George A. Constantinides Senior Member, IEEE Abstract—Lock-free algorithms, in which threads synchronise These reorderings are invisible in a single-threaded context, not via coarse-grained mutual exclusion but via fine-grained but in a multi-threaded context, they can introduce unexpected atomic operations (‘atomics’), have been shown empirically to behaviours. For instance, if another thread is simultaneously be the fastest class of multi-threaded algorithms in the realm of conventional processors. This article explores how these writing to z, then reordering two instructions above may algorithms can be compiled from C to reconfigurable hardware introduce the behaviour where x is assigned the latest value via high-level synthesis (HLS). but y gets an old one.1 We focus on the scheduling problem, in which software The implication of this is not that existing HLS tools are instructions are assigned to hardware clock cycles. We first wrong; these optimisations can only introduce new behaviours show that typical HLS scheduling constraints are insufficient to implement atomics, because they permit some instruction when the code already exhibits a race condition, and races reorderings that, though sound in a single-threaded context, are deemed a programming error in C [2, §5.1.2.4]. Rather, demonstrably cause erroneous results when synthesising multi- the implication is that if these memory accesses are upgraded threaded programs. We then show that correct behaviour can be to become atomic (and hence allowed to race), then existing restored by imposing additional intra-thread constraints among scheduling constraints are insufficient.