DOCSLIB.ORG

Performance Problem Diagnostics by Systematic Experimentation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Performance Problem Diagnostics by Systematic Experimentation Performance Problem Diagnostics by Systematic Experimentation Zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften von der Fakultät für Informatik des Karlsruher Instituts für Technologie (KIT) genehmigte Dissertation von Alexander Wert aus Kopejsk, Russland Tag der mündlichen Prüfung: 15.07.2015 Erstgutachter: Prof. Dr. Ralf Reussner Zweitgutachter: Prof. Dr. Wilhelm Hasselbring Abstract The performance of enterprise software systems has a direct impact on the success of business. Recent studies have shown that software performance affects customer satisfaction as well as operational costs of software. Hence, software performance constitutes an essential competitive and differentiating factor for software vendors and operators. In industrial practice, it is still a challenging task to detect software performance problems before they are faced by end users. Diagnostics of performance problems requires deep expertise in performance engineering and still entails a high manual effort. As a consequence, performance evaluations are postponed to the last minute of the development process, or even are completely omitted. Instead of proactively avoiding performance problems, problems are fixed in a reactive manner when they first emerge in operations. Since reactive, operation-time resolution of performance problems is very expensive and has a negative impact on the reputation of software vendors, performance problems need to be diagnosed and resolved in the process of software development. Existing approaches addressing performance problem diagnostics either assume the existence of a performance model, are limited to problem detection without analyzing root causes, or are applied as reactive approaches during the operations phase and, thus, cannot be effective applied during development for performance problem diagnostics. In this thesis, we introduce an automatic, experiment-based approach for performance problem diagnostics in enterprise software systems. We describe a method to derive a taxonomy on recurrent types of performance problems and introduce a systematic experimentation concept. Using the taxonomy as a search tree, the proposed approach systematically searches for root causes of detected performance problems by executing series of systematic performance experiments. Based on the measurement data from experiments, detection heuristics decide on the presence of performance problems in the target system. Furthermore, we develop a domain-specific description language to specify the information required for automatic performance problem diagnostics. Finally, we create and evaluate a representative set of detection heuristics. We validate our approach by means of five studies including end-to-end case studies, a controlled experiment and an empirical study. The results of the validation show that our approach is applicable to a wide range of contexts and is able to fully automatically and accurately detect performance problems in medium-size and large-scale applications. External users of the provided approach evaluated it as a useful support for diagnostics of performance problems and exposed their willingness to use the approach for their own software development projects. Explicitly designed for automatic, development-time testing, our approach can be incorporated into continuous integration. In this way, our approach allows regular, automatic diagnostics of performance problems involving minimal manual effort. Furthermore, by encapsulating and automating expert knowledge on performance engineering, our approach enables developers who are non-performance experts to conduct performance problem diagnostics. iii Kurzfassung In heutigen Unternehmen nehmen betriebliche Informationssysteme eine zentrale Rolle ein. Sie eröffnen neue Vertriebskanäle, ermöglichen effizientere Betriebsprozesse und tragen maßgeblich zur Nutzung von Skaleneffekten bei. Dienstgüteeigenschaften betrieblicher Informationssysteme haben einen direkten Einfluss auf den Geschäftserfolg der Unternehmen. Studien haben gezeigt, dass die Performance solcher Systeme sich maßgeblich auf deren Betriebskosten sowie die Kun- denzufriedenheit auswirkt. Somit ist die Performance betrieblicher Software ein entscheidender Wettbewerbsfaktor und ein Differenzierungsmerkmal für Software-Anbieter und Software-Betreiber. Performance-Probleme, die bis zum Betrieb der Software unerkannt bleiben, stellen nicht nur ein finanzielles Risiko dar, sondern können auch einen Schaden in der Reputation verursachen. In der industriellen Praxis der Software-Entwicklung ist das frühzeitige Erkennen und die Dia- gnose von Performance-Problemen immer noch eine große Herausforderung. Die Diagnose von Performance-Problemen erfordert nicht nur ein tiefgründiges Expertenwissen in der Disziplin des Performance Engineering, sondern bringt einen großen manuellen Aufwand mit sich. Dies hat zur Folge, dass Performance-Analysen bis in die späten Phasen der Software-Entwicklung aufgescho- ben oder gänzlich ausgelassen werden. Performance-Probleme werden meist reaktiv angegangen, wenn sie zum ersten Mal im Betrieb der Software auftauchen, anstatt sie proaktiv während der Software-Entwicklung zu erkennen und zu lösen. Da das reaktive Lösen von Performance-Problemen vergleichsweise sehr aufwändig ist, müssen Performance-Probleme noch während des Software- Entwicklungsprozesses diagnostiziert werden. Existierende Ansätze zur Erkennung von Performance-Problemen nehmen entweder die Verfügbar- keit eines Systemmodells an, sind auf das Erkennen von Problemen beschränkt, ohne eine Diagnose der Ursachen durchzuführen, oder sind für den reaktiven Einsatz während des Software-Betriebs ausgelegt. Somit bieten existierende Ansätze nicht die notwendigen Mittel, um eine proaktive Er- kennung und Diagnose von Performance-Problemen effektiv während der Software-Entwicklung durchzuführen. In der vorliegenden Arbeit wird ein Ansatz zur automatischen, experimentbasierten Diagnose von Performance-Problemen in betrieblichen Software-Systemen vorgestellt. Basierend auf einer Taxono- mie von wiederkehrenden Performance-Problemtypen führt der vorgestellte Ansatz eine Erkennung von Performance-Problemen durch sowie eine systematische Suche nach deren Ursachen. Dabei wird eine Reihe zielgerichteter Performance-Experimente durchgeführt. Die dabei gesammelten Messdaten werden mit Hilfe einer Menge an Regeln und Algorithmen analysiert, um Aussagen über die Existenz entsprechender Performance-Problemtypen in dem Zielsystem zu treffen. Die vorliegende Arbeit umfasst die folgenden wissenschaftlichen Beiträge: v Taxonomie der Performance-Problemtypen Die vorliegende Arbeit führt eine Methode zur expliziten Erfassung von strukturiertem Wissen über Performance-Probleme ein. Dazu wird ein Klas- sifikationsschema für bestehende, wiederkehrende Typen von Performance-Problemen eingeführt. Die Klassifikation erfasst zum einen verschiedene Charakteristiken von Performance-Problemen sowie die Beziehungen zwischen unterschiedlichen Performance-Problemtypen. Auf Basis der Klassifikation wird eine Taxonomie der wiederkehrenden Performance-Problemtypen abgeleitet. Die statische Taxonomie wird schließlich um die dynamische Information zur experimentbasierten Diagnose von Performance-Problemen erweitert. Im Rahmen der vorliegenden Arbeit wurde die eingeführte Methode auf 27 Performance-Problembeschreibungen angewendet. Das Ergebnis ist ein Evaluationsplan für Performance-Probleme, der zur Koordination der automatischen Performance- Problemdiagnose eingesetzt werden kann und darüber hinaus eine Unterstützung bei der manuellen Diagnose bietet. Systematisches Experimentier- und Diagnosekonzept Messbasierte Performance-Ana- lyseverfahren haben das inhärente Problem, dass Messdaten auf Grund des Mehraufwands der Messung nicht gleichzeitig genau, umfassend und mit hohem Detailgrad erfasst werden können. Die vorliegende Arbeit beschreibt unter anderem ein systematisches, selektives Experimentierkonzept, dass es erlaubt durch eine systematische Ausführung von Experimenten sowohl genaue als auch detaillierte Messdaten zu erfassen. Dabei wird für jedes einzelne Experiment eine zielgerichtete, selektive Instrumentierung der Zielanwendung durchgeführt, sodass der durch Messung verursachte Mehraufwand in jedem Experiment gering gehalten wird. Die Instrumentierungsanweisungen werden zwischen einzelnen Experimenten dynamisch verändert und erlauben so, detaillierte Daten bei hoher Genauigkeit zu erfassen. Zur automatischen Diagnose von Performance-Problemen wird das systema- tische Experimentierkonzept mit der beschriebenen Taxonomie kombiniert, um eine systematische Suche nach Ursachen von Performance-Problem zu ermöglichen. Die Taxonomie wird dabei als ein Suchbaum verwendet, während für jeden Knoten in der Taxonomie ein zugeschnittenes Experiment mit selektiver Instrumentierung durchgeführt wird. In der vorliegenden Arbeit werden die Vorteile und die Einschränkungen des systematischen Experimentierkonzepts für die automatische Diagnose von Performance-Problemen untersucht. Des Weiteren wird die Anwendbarkeit des vorgestellten Experimentierkonzepts über die Performance-Problemdiagnose hinaus untersucht. Beschreibungssprache für Performance-Problemdiagnose Um eine system- und techno- logieunabhängige und dennoch automatische Diagnose von Performance-Problemen zu ermöglichen, wird in dieser Arbeit ein Metamodell eingeführt, welches eine Sprache zur Beschreibung von Szenarien der Performance-Problemdiagnose definiert. Die Sprache umfasst vier Subsprachen.
Load more

Recommended publications
  • Synchronization Spinlocks - Semaphores
    CS 4410 Operating Systems Synchronization Spinlocks - Semaphores Summer 2013 Cornell University 1 Today ● How can I synchronize the execution of multiple threads of the same process? ● Example ● Race condition ● Critical-Section Problem ● Spinlocks ● Semaphors ● Usage 2 Problem Context ● Multiple threads of the same process have: ● Private registers and stack memory ● Shared access to the remainder of the process “state” ● Preemptive CPU Scheduling: ● The execution of a thread is interrupted unexpectedly. ● Multiple cores executing multiple threads of the same process. 3 Share Counting ● Mr Skroutz wants to count his $1-bills. ● Initially, he uses one thread that increases a variable bills_counter for every $1-bill. ● Then he thought to accelerate the counting by using two threads and keeping the variable bills_counter shared. 4 Share Counting bills_counter = 0 ● Thread A ● Thread B while (machine_A_has_bills) while (machine_B_has_bills) bills_counter++ bills_counter++ print bills_counter ● What it might go wrong? 5 Share Counting ● Thread A ● Thread B r1 = bills_counter r2 = bills_counter r1 = r1 +1 r2 = r2 +1 bills_counter = r1 bills_counter = r2 ● If bills_counter = 42, what are its possible values after the execution of one A/B loop ? 6 Shared counters ● One possible result: everything works! ● Another possible result: lost update! ● Called a “race condition”. 7 Race conditions ● Def: a timing dependent error involving shared state ● It depends on how threads are scheduled. ● Hard to detect 8 Critical-Section Problem bills_counter = 0 ● Thread A ● Thread B while (my_machine_has_bills) while (my_machine_has_bills) – enter critical section – enter critical section bills_counter++ bills_counter++ – exit critical section – exit critical section print bills_counter 9 Critical-Section Problem ● The solution should ● enter section satisfy: ● critical section ● Mutual exclusion ● exit section ● Progress ● remainder section ● Bounded waiting 10 General Solution ● LOCK ● A process must acquire a lock to enter a critical section.
    [Show full text]
  • Synchronization: Locks
    Last Class: Threads • Thread: a single execution stream within a process • Generalizes the idea of a process • Shared address space within a process • User level threads: user library, no kernel context switches • Kernel level threads: kernel support, parallelism • Same scheduling strategies can be used as for (single-threaded) processes – FCFS, RR, SJF, MLFQ, Lottery... Computer Science CS377: Operating Systems Lecture 5, page 1 Today: Synchronization • Synchronization – Mutual exclusion – Critical sections • Example: Too Much Milk • Locks • Synchronization primitives are required to ensure that only one thread executes in a critical section at a time. Computer Science CS377: Operating Systems Lecture 7, page 2 Recap: Synchronization •What kind of knowledge and mechanisms do we need to get independent processes to communicate and get a consistent view of the world (computer state)? •Example: Too Much Milk Time You Your roommate 3:00 Arrive home 3:05 Look in fridge, no milk 3:10 Leave for grocery store 3:15 Arrive home 3:20 Arrive at grocery store Look in fridge, no milk 3:25 Buy milk Leave for grocery store 3:35 Arrive home, put milk in fridge 3:45 Buy milk 3:50 Arrive home, put up milk 3:50 Oh no! Computer Science CS377: Operating Systems Lecture 7, page 3 Recap: Synchronization Terminology • Synchronization: use of atomic operations to ensure cooperation between threads • Mutual Exclusion: ensure that only one thread does a particular activity at a time and excludes other threads from doing it at that time • Critical Section: piece of code that only one thread can execute at a time • Lock: mechanism to prevent another process from doing something – Lock before entering a critical section, or before accessing shared data.
    [Show full text]
  • INF4140 - Models of Concurrency Locks & Barriers, Lecture 2
    Locks & barriers INF4140 - Models of concurrency Locks & barriers, lecture 2 Høsten 2015 31. 08. 2015 2 / 46 Practical Stuff Mandatory assignment 1 (“oblig”) Deadline: Friday September 25 at 18.00 Online delivery (Devilry): https://devilry.ifi.uio.no 3 / 46 Introduction Central to the course are general mechanisms and issues related to parallel programs Previously: await language and a simple version of the producer/consumer example Today Entry- and exit protocols to critical sections Protect reading and writing to shared variables Barriers Iterative algorithms: Processes must synchronize between each iteration Coordination using flags 4 / 46 Remember: await-example: Producer/Consumer 1 2 i n t buf, p := 0; c := 0; 3 4 process Producer { process Consumer { 5 i n t a [N ] ; . i n t b [N ] ; . 6 w h i l e ( p < N) { w h i l e ( c < N) { 7 < await ( p = c ) ; > < await ( p > c ) ; > 8 buf:=a[p]; b[c]:=buf; 9 p:=p+1; c:=c+1; 10 }} 11 }} Invariants An invariant holds in all states in all histories of the program. global invariant: c ≤ p ≤ c + 1 local (in the producer): 0 ≤ p ≤ N 5 / 46 Critical section Fundamental concept for concurrency Critical section: part of a program that is/needs to be “protected” against interference by other processes Execution under mutual exclusion Related to “atomicity” Main question today: How can we implement critical sections / conditional critical sections? Various solutions and properties/guarantees Using locks and low-level operations SW-only solutions? HW or OS support? Active waiting (later semaphores and passive waiting) 6 / 46 Access to Critical Section (CS) Several processes compete for access to a shared resource Only one process can have access at a time: “mutual exclusion” (mutex) Possible examples: Execution of bank transactions Access to a printer or other resources ..
    [Show full text]
  • Spinlock Vs. Sleeping Lock Spinlock Implementation(1)
    Semaphores EECS 3221.3 • Problems with the software solutions. Operating System Fundamentals – Complicated programming, not flexible to use. – Not easy to generalize to more complex synchronization problems. No.6 • Semaphore (a.k.a. lock): an easy-to-use synchronization tool – An integer variable S Process Synchronization(2) – wait(S) { while (S<=0) ; S-- ; Prof. Hui Jiang } Dept of Electrical Engineering and Computer – signal(S) { Science, York University S++ ; } Semaphore usage (1): Semaphore usage (2): the n-process critical-section problem as a General Synchronization Tool • The n processes share a semaphore, Semaphore mutex ; // mutex is initialized to 1. • Execute B in Pj only after A executed in Pi • Use semaphore flag initialized to 0 Process Pi do { " wait(mutex);! Pi Pj " critical section of Pi! ! … … signal(mutex);" A wait (flag) ; signal (flag) ; B remainder section of Pi ! … … ! } while (1);" " " Spinlock vs. Sleeping Lock Spinlock Implementation(1) • In uni-processor machine, disabling interrupt before modifying • Previous definition of semaphore requires busy waiting. semaphore. – It is called spinlock. – spinlock does not need context switch, but waste CPU cycles wait(S) { in a continuous loop. do { – spinlock is OK only for lock waiting is very short. Disable_Interrupt; signal(S) { • Semaphore without busy-waiting, called sleeping lock: if(S>0) { S-- ; Disable_Interrupt ; – In defining wait(), rather than busy-waiting, the process makes Enable_Interrupt ; S++ ; system calls to block itself and switch to waiting state, and return ; Enable_Interrupt ; put the process to a waiting queue associated with the } return ; semaphore. The control is transferred to CPU scheduler. Enable_Interrupt ; } – In defining signal(), the process makes system calls to pick a } while(1) ; process in the waiting queue of the semaphore, wake it up by } moving it to the ready queue to wait for CPU scheduling.
    [Show full text]
  • Processes and Threads
    Interprocess Communication Yücel Saygın |These slides are based on your text book and on the slides prepared by Andrew S. Tanenbaum 1 Inter-process Communication • Race Conditions: two or more processes are reading and writing on shared data and the final result depends on who runs precisely when • Mutual exclusion : making sure that if one process is accessing a shared memory, the other will be excluded form doing the same thing • Critical region: the part of the program where shared variables are accessed 2 Interprocess Communication Race Conditions Two processes want to access shared memory at same time 3 Critical Regions (1) Four conditions to provide mutual exclusion 1. No two processes simultaneously in critical region 2. No assumptions made about speeds or numbers of CPUs 3. No process running outside its critical region may block another process 4. No process must wait forever to enter its critical region 4 Critical Regions (2) Mutual exclusion using critical regions 5 Mutual Exclusion via Disabling Interrupts • Process disables all interrupts before entering its critical region • Enables all interrupts just before leaving its critical region • CPU is switched from one process to another only via clock or other interrupts • So disabling interrupts guarantees that there will be no process switch • Disadvantage: – Should give the power to control interrupts to user (what if a user turns off the interrupts and never turns them on again?) – Does not work in case of multiple CPUs. Only the CPU that executes the disable instruction is effected. • Not suitable approach for the general case but can be used by the Kernel when needed 6 Mutual Exclusion with Busy Waiting: (1)Lock Variables • Testing a variable until some value appears is called busy waiting.
    [Show full text]
  • Semaphores (Dijkstra 1965)
    Semaphores (Dijkstra 1965) n Semaphores have a non-negative integer value, and support two operations: n semaphore->P(): an atomic operation that waits for semaphore Semaphores & Monitors to become positive, then decrements it by 1 n semaphore->V(): an atomic operation that increments semaphore by 1, waking up a waiting P, if any. n Semaphores are like integers except: Arvind Krishnamurthy (1) non-negative values; Spring 2004 (2) only allow P&V --- can’t read/write value except to set it initially; (3) operations must be atomic: -- two P’s that occur together can’t decrement the value below zero. -- thread going to sleep in P won’t miss wakeup from V, even if they both happen at about the same time. Implementing semaphores Using interrupts P means “test” (proberen in Dutch) V means “increment” (verhogen in Dutch) class Semaphore { int value = 0; } n Binary semaphores: Semaphore::P() { n Like a lock; can only have value 0 or 1 (unlike the previous Disable interrupts; Semaphore::V() { “counting semaphore” which can be any non-negative integers) while (value == 0) { Disable interrupts; Enable interrupts; value++; Disable interrupts; Enable interrupts; } } n How to implement semaphores? Standard tricks: value = value - 1; Enable interrupts; n Can be built using interrupts disable/enable } n Or using test-and-set n Use a queue to prevent unnecessary busy-waiting n Question: Can we build semaphores using just locks? Using test&set How to use semaphores n Binary semaphores can be used for mutual exclusion: class Semaphore { int value = 0; initial value of 1; P() is called before the critical section; and V() is called after the int guard = 0; } critical section.
    [Show full text]
  • 9 Mutual Exclusion
    System Architecture 9 Mutual Exclusion Critical Section & Critical Region Busy-Waiting versus Blocking Lock, Semaphore, Monitor November 24 2008 Winter Term 2008/09 Gerd Liefländer © 2008 Universität Karlsruhe (TH), System Architecture Group 1 Overview Agenda HW Precondition Mutual Exclusion Problem Critical Regions Critical Sections Requirements for valid solutions Implementation levels User-level approaches HW support Kernel support Semaphores Monitors © 2008 Universität Karlsruhe (TH), System Architecture Group 2 Overview Literature Bacon, J.: Operating Systems (9, 10, 11) Nehmer,J.: Grundlagen moderner BS (6, 7, 8) Silberschatz, A.: Operating System Concepts (4, 6, 7) Stallings, W.: Operating Systems (5, 6) Tanenbaum, A.: Modern Operating Systems (2) Research papers on various locks © 2008 Universität Karlsruhe (TH), System Architecture Group 3 Atomic Instructions To understand concurrency, we need to know what the underlying indivisible HW instructions are Atomic instructions run to completion or not at all It is indivisible: it cannot be stopped in the middle and its state cannot be modified by someone else in the middle Fundamental building block: without atomic instructions we have no way for threads to work together properly load, store of words are usually atomic However, some instructions are not atomic VAX and IBM 360 had an instruction to copy a whole array © 2008 Universität Karlsruhe (TH), System Architecture Group 4 Mutual Exclusion © 2008 Universität Karlsruhe (TH), System Architecture Group 5 Mutual Exclusion Problem Assume at least two concurrent activities 1. Access to a physical or to a logical resource or to shared data has to be done exclusively 2. A program section of an activity, that has to be executed indivisibly and exclusively is called critical section CS1 3.
    [Show full text]
  • Models of Concurrency & Synchronization Algorithms
    Models of concurrency & synchronization algorithms Lecture 3 of TDA384/DIT391 Principles of Concurrent Programming Nir Piterman Chalmers University of Technology | University of Gothenburg SP1 2021/2022 Models of concurrency & synchronization algorithms Today's menu • Modeling concurrency • Mutual exclusion with only atomic reads and writes • Three failed attempts • Peterson’s algorithm • Mutual exclusion with strong fairness • Implementing mutual exclusion algorithms in Java • Implementing semaphores Modeling concurrency State/transition diagrams We capture the essential elements of concurrent programs using state/transition diagrams (also called: (finite) state automata, (finite) state machines, or transition systems). • states in a diagram capture possible program states • transitions connect states according to execution order Structural properties of a diagram capture semantic properties of the corresponding program. States A state captures the shared and local states of a concurrent program: States A state captures the shared and local states of a concurrent program: When unambiguous, we simplify a state with only the essential information: Initial states The initial state of a computation is marked with an incoming arrow: Final states The final states of a computation – where the program terminates – are marked with double-line edges: Transitions A transition corresponds to the execution of one atomic instruction, and it is an arrow connecting two states (or a state to itself): A complete state/transition diagram The complete state/transition diagram for the concurrent counter example explicitly shows all possible interleavings: State/transition diagram with locks? The state/transition diagram of the concurrent counter example using locks should contain no (states representing) race conditions: Locking Locking and unlocking are considered atomic operations.
    [Show full text]
  • Preview – Interprocess Communication
    Preview – Interprocess Communication Race Condition Mutual Exclusion with Busy Waiting Disabling Interrupts Lock Variables Strict Alternation Peterson’s Solution Test and Set Lock – help from hardware Sleep and Wakeup The Producer-Consumer Problem Race Condition in Producer-Consumer Problem Semaphores The producer-consumer problem using semaphores Mutexes Monitors CS 431 Operating Systems 1 Interprocess Communication Critical section (critical region) – The part of program where the shared memory is accessed. Three issues in InterProcess Communication (IPC) 1. How one process can pass information to another 2. How to make sure two or more processes do not get into the critical region at the same time 3. Proper sequencing when dependencies are present (e.g. A creates outputs, B consumes the outputs) CS 431 Operating Systems 2 Race Condition A situation where two or more processes are reading or writing some shared data and the final result depends on who runs precisely when, is called race condition. Network Printer Spooler Directory CS 431 Operating Systems 3 Race Condition Process B will never Process A tries to send a job to receive any output from spooler, Process A reads the printer !! in = 7, process A times out and goes to “ready” state before updating in = in + 1. CPU switches to Process B. Process B also tries to send a job to spooler. Process B reads in = 7, writes its job name in slot 7, updates i to 8 and then goes to “ready” state. CPU switches to Process A. Process A starts from the place it left off. Process writes its job name in slot 7, update i to 8.
    [Show full text]
  • Pthreads: Busy Wait, Mutexes, Semaphores, CondiOns, and Barriers Bryan Mills Chapter 4.2-4.8
    Pthreads: Busy Wait, Mutexes, Semaphores, Condi9ons, and Barriers Bryan Mills Chapter 4.2-4.8 Spring 2017 A Shared Memory System Copyright © 2010, Elsevier Inc. All rights Reserved Pthreads • Pthreads is a POSIX standard for describing a thread model, it specifies the API and the seman9cs of the calls. • Model popular –prac9cally all major thread libraries on Unix systems are Pthreads- compable • The Pthreads API is only available on POSIXR systems — Linux, MacOS X, Solaris, HPUX, … Preliminaries • Include pthread.h in the main file • Compile program with –lpthread – gcc –o test test.c –lpthread – may not report compilaon errors otherwise but calls will fail • Check return values on common func9ons Include #include <stdio.h> #include <pthread.h> This includes the pthreads library. #define NUM_THREADS 10 void *hello (void *rank) { printf("Hello Thread”); } int main() { pthread_t ids[NUM_THREADS]; for (int i=0; i < NUM_THREADS; i++) { pthread_create(&ids[i], NULL, hello, &i); } for (int i=0; i < NUM_THREADS; i++) { pthread_join(ids[i], NULL); } } C Libraries • Libraries are created from many source files, and are either built as: – Archive files (libmine.a) that are stacally linked. – Shared object files (libmine.so) that are dynamically linked • Link libraries using the gcc command line op9ons -L for the path to the library files and -l to link in a library (a .so or a .a): gcc -o myprog myprog.c -L/home/bnm/lib –lmine • You may also need to specify the library path: -I /home/bnm/include Run9me Linker • Libraries that are dynamically linked need to be able to find the shared object (.so) • LD_LIBRARY_PATH environment variable is used to tell the system where to find these files.
    [Show full text]
  • Chapter 12 (Concurrency) of Programming Language Pragmatics
    Contents 12 Concurrency 1 12.1BackgroundandMotivation........................... 2 12.1.1ALittleHistory............................. 2 12.1.2TheCaseforMulti-ThreadedPrograms................ 4 12.1.3MultiprocessorArchitecture....................... 8 12.2 Concurrent Programming Fundamentals . 11 12.2.1CommunicationandSynchronization.................. 12 12.2.2LanguagesandLibraries......................... 13 12.2.3ThreadCreationSyntax......................... 14 12.2.4ImplementationofThreads....................... 22 12.3SharedMemory.................................. 26 12.3.1Busy-WaitSynchronization....................... 27 12.3.2SchedulerImplementation........................ 30 12.3.3Scheduler-BasedSynchronization.................... 33 12.3.4ImplicitSynchronization......................... 41 12.4MessagePassing................................. 43 12.4.1NamingCommunicationPartners.................... 43 12.4.2Sending.................................. 47 12.4.3 Receiving . 50 12.4.4 RemoteProcedureCall......................... 56 SummaryandConcludingRemarks.......................... 58 ReviewQuestions.................................... 60 Exercises........................................ 61 BibliographicNotes.................................. 66 From Programming Language Pragmatics, by Michael L. Scott. Copyright c 2000, Morgan Kaufmann Publishers; all rights reserved. This material may not be copied or distributed without permission of the publisher. i Chapter 12 Concurrency The bulk of this text has focused, implicitly, on sequential
    [Show full text]
  • Semaphores and Other Wait-And-Signal Mechanisms
    Semaphores and other Wait-and-Signal mechanisms Carsten Griwodz University of Oslo (including slides by Otto Anshus and Kai Li) Critical Regions Four conditions to provide mutual exclusion 1. No two threads simultaneously in critical region 2. No assumptions made about speeds or numbers of CPUs 3. No thread running outside its critical region may block another thread 4. No thread must wait forever to enter its critical region Critical Regions Mutual exclusion using critical regions Recall Processes and Threads • Process • Thread – Address space – Program counter – Program text and data – Registers – Open files – Stack – Child process IDs – Alarms – Signal handlers – Accounting information • Implemented in kernel or in user space • Implemented in kernel • Threads are the scheduled entities Producer-Consumer Problem • Main problem description – Two threads – Different actions in the critical region – The consumer can not enter the CR more often than the producer • Two sub-problems – Unbounded PCP: the producer can enter the CR as often as it wants – Bounded PCP: the producer can enter the CR only N times more often than the consumer Unbounded PCP Q PUT (msg) GET (buf) Rules for the queue Q: •No Get when empty Consumer Producer •Q shared, so must have mutex between Put and Get Recall Mutexes • Can be acquired and released – Only one thread can hold one mutex at a time – A second thread trying to acquire must wait • Mutexes – Can be implemented using busy waiting – Simpler with advanced atomic operations • Disable interrupts, TSL, XCHG, …
    [Show full text]