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OS05: Mutual Exclusion

Based on Chapter 4 of [Hai19]

(Usage hints for this presentation)

Computer Structures and Operating Systems 2023
Dr. Jens Lechtenbörger (License Information)

1. Introduction

1.1. OS Plan

OS course plan, summer 2022

1.2. Today’s Core Questions

  • What can go wrong with concurrent computations?
    • What is a race condition?
  • How to avoid subtle programming bugs related to timing issues?
    • What mechanisms does the OS provide to help?

1.3. Learning Objectives

  • Recognize and explain race conditions
  • Explain notions of critical section and mutual exclusion
  • Use mutexes and semaphores (and monitors after upcoming lectures) to enforce mutual exclusion

1.4. Retrieval Practice

1.4.1. Informatik 1

You have seen this advice before. It is repeated here for emphasis:

1.4.2. Recall: Datenmanagement

  • Give examples for dirty read and lost update anomalies.
  • What is a database transaction?
  • What does each of the four ACID guarantees mean?
  • Explain serializability as notion of consistency.

The above is covered in this introduction to transaction processing.

Table of Contents

2. Race Conditions

2.1. Central Challenge: Races

Ferrari Kiss

Ferrari Kiss” by Antoine Valentini under CC BY-SA 2.0; from flickr

2.2. Races (1/2)

  • Race (condition): a technical term
  • Previous picture
    • Cars are activities
    • Street segments represent shared resources
    • Timing determines whether a crash occurs or not
    • Crash = misjudgment = missing synchronization

2.3. Races (2/2)

  • DBMS
    • SQL commands are activities
    • Database objects are shared resources
    • Update anomalies indicate missing synchronization
      • Serializability requires synchronization and avoids anomalies
  • OS
    • Threads are activities
    • Variables, memory, files are shared resources
    • Missing synchronization is a bug, leading to anomalies just as in database systems

2.4. Self-Study Tasks

2.4.1. The Deadlock Empire, Part 1

  • Play “Tutorial 1,” “Tutorial 2,” and the three games for “Unsynchronized Code” at
    • The games make use of C#
      • (Which you do not need to know; the games include lots of explanations, also mouse-over helps)
  • General idea
    • The game is about mutual exclusion and critical sections, to be discussed next
      • At any point in time just one thread is allowed to execute under mutual exclusion inside a critical section
      • If you manage to lead two threads into a critical section simultaneously (or, in some levels, to execute Assert(false)), you demonstrate a race condition
    • You win a game if you demonstrate a race condition
  • Really, start playing now! (Nothing to submit here)

2.4.2. Transfer of Deadlock Empire

Consider the following piece of Java code (from Sec. 4.2 of [Hai19]) to sell tickets as long as seats are available. (That code is embedded in this sample program, which you can execute to see races yourself.)

if (seatsRemaining > 0) {
   seatsRemaining = seatsRemaining - 1;
} else displaySorrySoldOut();

Inspired by the Deadlock Empire, find and explain a race condition involving the counter seatsRemaining, which leads to single tickets being sold several times (to be revisited in exercises).

2.5. Non-Atomic Executions

  • Races generally result from non-atomic executions
    • Even “single” instructions such as i += 1 are not atomic
      • Execution via sequences of machine instructions
        • Load variable’s value from RAM
        • Perform add in ALU
        • Write result to RAM
    • A context switch may happen after any of these machine instructions, i.e., “in the middle” of a high-level instruction
      • Intermediate results accessible elsewhere
      • Demo: Play a game as instructed previously

3. Critical Sections and Mutual Exclusion

3.1. Goal and Solutions (1/3)

  • Goal
    • Concurrent executions that access shared resources should be isolated from one another
  • Conceptual solution
    • Declare critical sections (CSs)
      • CS = Block of code with potential for race conditions on shared resources
        • Cf. transaction as sequence of operations on shared data
    • Enforce mutual exclusion (MX) on CSs
      • At most one thread inside CS at any point in time
        • This avoids race conditions
        • Cf. serializability for transactions

3.2. MX with CSs: Ticket example

Interleaved execution of threads with MX for code from Sec. 4.2 of book "Operating Systems and Middleware – Supporting Controlled Interaction" by Max Hailperin, CC BY-SA 3.0. image/svg+xml Interleaved execution of threads with MX for code from Sec. 4.2 of book "Operating Systems and Middleware – Supporting Controlled Interaction" by Max Hailperin, CC BY-SA 3.0. 2017, 2018 Jens Lechtenbörger Thread T1 Thread T2 time // T1 enters CS with MXif (seatsRemaining > 0)// T1 preempted// T2 dispatched // T2 tries to enter CS// As T1 in CS, T2 blocked by MX// OS dispatches other thread, say T1 dispenseTicket();seatsRemaining -= 1// T1 finishes, leaves CS// T2 can continue if (seatsRemaining > 0)// T2 finishes

Interleaved execution of threads with MX for code from Sec. 4.2 of book by Max Hailperin, CC BY-SA 3.0.” by Jens Lechtenbörger under CC BY-SA 4.0; from GitLab

3.3. Goal and Solutions (2/3)

  • New goal
    • Implementations/mechanisms for MX on CS
  • Solutions, to be discussed in detail
    • Locks, also called mutexes
      • Cf. 2PL for database transactions
      • Acquire lock/mutex at start of CS, release it at end
        • Choices: Busy waiting (spinning) or blocking when lock/mutex not free?
    • Semaphores
      • Abstract datatype, generalization of locks, blocking, signaling
    • Monitors
      • Abstract datatype, think of Java class, methods as CS with MX
      • Keyword synchronized turns Java method into CS with MX!

3.4. Challenges

  • Above solutions restrict entry to CS
    • Thus, they restrict access to the resource CPU
  • Major synchronization challenges arise
    • Starvation (related to (un-) fairness)
    • Deadlock (discussed in later presentation)
      • Set of threads is stuck
      • Circular wait for additional locks/semaphores/resources/messages
    • In addition, programming errors, e.g., races involving seatsRemaining
      • Difficult to locate, time-dependent
      • Difficult to reproduce, “non-determinism”

3.5. Goal and Solutions (3/3)

  • Recall above loose definition
    • MX = At most one thread inside CS at any point in time
      • This avoids race conditions
  • Stricter definitions also address deadlocks, starvation, failures
    • Our definition: Solution ensures MX if
      • At most one thread inside CS at any point in time
      • Deadlocks are ruled out
    • (Not our focus: Starvation does not occur)
      • (E.g., requests granted under fairness guarantees such as first-come first-serve or with “luck” based on randomness)
    • ([Lam86] provides detailed discussion, also addressing failures)

4. Locking

4.1. Mutexes



Figure © 2016 Julia Evans, all rights reserved; from julia's drawings. Displayed here with personal permission.

4.2. Locks and Mutexes

  • Lock = mutex = object with methods
    • lock() or acquire(): Lock/acquire/take the object
      • A lock can only be lock()’ed by one thread at a time
      • Further threads trying to lock() need to wait for unlock()
    • unlock() or release(): Unlock/release the object
      • Can be lock()’ed again afterwards
    • E.g., interface java.util.concurrent.locks.Lock in Java.

Figure 4.4 of cite:Hai17

Figure 4.4 of [Hai17]” by Max Hailperin under CC BY-SA 3.0; converted from GitHub

4.3. Use of Locks/Mutexes

  • Programming discipline required to prevent races
    • Create one (shared) lock for each shared data structure
    • Take lock before operating on shared data structure
    • Release lock afterwards
  • ([Hai19] has sample code following POSIX standard)
  • Ticket example modified (leading to MX behavior):
if (seatsRemaining > 0) {
   seatsRemaining = seatsRemaining - 1;
} else displaySorrySoldOut();

4.4. Quiz on MX Vocabulary

5. Semaphores

5.1. Semaphore Origin

  • Proposed by Dijkstra, 1965
    • Based on waiting (sleeping) for signals (wake-up calls)
      • Thread waiting for signal is blocked
  • Abstract data type
    • Non-negative integer
      • Number of available resources; 1 for MX on CPU
    • Queue for blocked threads
    • Atomic operations
      • Initialize integer
      • acquire (wait, sleep, down, P [passeren, proberen]): entry into CS
        • Decrement integer by 1
        • As integer must be non-negative, block when 0
      • release (signal, wakeup, up, V [vrijgeven, verlaten]): exit from CS
        • Increment integer by 1 (value may grow without bound)
        • Potentially unblock blocked thread

5.2. Use of Semaphores for MX

  • Programming discipline required similarly to locks
    • Create semaphore for shared data structure
    • acquire() before CS, release() after
  • Ticket example modified with seatSem initialized to 1 (leading to MX behavior):
    • (The semaphore initialized to 1 behaves exactly like a lock here)
if (seatsRemaining > 0) {
   seatsRemaining = seatsRemaining - 1;
} else displaySorrySoldOut();

5.2.1. Semaphores for Capacity Control

  • Semaphores initialized to other values than 1 are typically used to model resource capacities
    • Example from stack overflow: bouncer in nightclub
      • Nightclub has limited capacity, i.e., number of people allowed in club at any moment: n
        • Bouncer (semaphore initialized to n) makes sure that no more than n people can be inside
        • If club is full, a queue collects waiting people
      • Guests (threads) call acquire() on bouncer/semaphore to enter
      • Guests (threads) call release() on bouncer/semaphore to leave
    • Example later on: SemaphoreBoundedBuffer
    • (Semaphores are defined above; as approximation, think of a semaphore initialized to n as a set of n mutexes/locks; each one protecting a different resource or part thereof)

5.3. Self-Study Tasks

5.3.1. The Deadlock Empire – Remarks

5.3.2. The Deadlock Empire, Part 2

Play the following games at

  • “Locks” and “Semaphores”
    • Incorrect use of both, sometimes leading to deadlocks. For locks, Enter() and Exit() represent lock() and unlock()
  • “Condition Variables”
    • Here, if (queue.Count == 0) is meant to avoid removal attempts from empty queues. However, PulseAll() wakes up all waiting (blocked) threads (similarly to notifyAll() for Java later on)

6. Implementation Aspects

6.1. Atomic Instructions

  • Typical building block for MX: Atomic machine instruction
    • Several memory accesses with guarantee of isolation/no interference
  • E.g., exchange, which exchanges contents of register and memory location

6.2. Mutex with Simplistic Spinlock Implementation

  • Single memory location called mutex
    • Value 1: unlocked
    • Value 0: locked
  • Operations
    • unlock(): Store 1 into mutex
    • lock(): Atomically check for 1 and store 0 as follows ([Hai19]):
to lock mutex:
  let temp = 0
    atomically exchange temp and mutex
  until temp = 1

See [Hai19] for a cache-conscious spinlock variant (beyond scope of class).

6.3. On Spinlocks

  • Spinlock: Thread spins actively in loop on CPU while waiting for lock to be released
    • Busy waiting
    • Avoids overhead of scheduling and context switch coming with blocking locks
  • Note: Spinning thread keeps CPU core busy
    • No blocking by OS
    • Waste of CPU resources unless lock periods are short

6.4. Mutex with Queue

  • Mutex as OS mechanism
    • When lock() fails on a locked mutex, OS blocks thread
    • Blocked threads are collected in queue
    • No busy waiting
      • Thus, CPU time not wasted for long waiting periods
      • However, scheduling with its own overhead required
        • Wasteful, if waiting periods are short
  • Different variants of unlock()
    1. Unblock thread in FIFO order from queue (if one exists)
      • And reassign mutex to that thread
    2. Make all threads runnable without reassigning mutex
  • Pseudocode in [Hai19]

6.5. Quiz on Locking

7. Outlook

7.1. Producer/Consumer problems

  • Classical synchronization problems, revisited in next presentation
    • One or more producers
      • Generate data
        • Records, messages, tasks
      • Place data into buffer (shared resource)
        • Two buffer variants: unbounded or bounded
        • Producer blocks, if bounded buffer is full
    • One or more consumers
      • Consume data
        • Take data out of buffer
        • Consumer blocks, if buffer is empty
  • Synchronization for buffer manipulations necessary

7.2. Use of Semaphores to Track Resources

import java.util.concurrent.Semaphore;
  This code is based on Figure 4.18 of the following book:
  Max Hailperin, Operating Systems and Middleware – Supporting
  Controlled Interaction, revised edition 1.3, 2017.

  In Figure 4.18, synchronizedList() is used, whereas here a
  plain LinkedList is used, which is protected by the additional
  semaphore mutex.
  Also, the class here is renamed and implements a new interface.
public class SemaphoreBoundedBuffer implements BoundedBuffer {
  private java.util.List<Object> buffer =
      new java.util.LinkedList<Object>();

  private static final int SIZE = 20; // arbitrary

  private Semaphore mutex = new Semaphore(1);
  private Semaphore occupiedSem = new Semaphore(0);
  private Semaphore freeSem = new Semaphore(SIZE);

  /* invariant: occupiedSem + freeSem = SIZE
     buffer.size() = occupiedSem
     buffer contains entries from oldest to youngest */

  public void insert(Object o) throws InterruptedException {
    // Called by producer thread

  public Object retrieve() throws InterruptedException {
    // Called by consumer thread
    Object retrieved = buffer.remove(0);
    return retrieved;

  public int size() {
    return buffer.size();

8. Pointers beyond class topics

8.1. GNU/Linux: Futex

  • Fast user space mutex
    • No system call for single user (fastpath)
    • System calls for blocking/waiting (slowpath)
  • Meant as building block for libraries
  • Like semaphore: Integer with up() and down()
    • Assembler code with atomic instructions for integer access
  • Documentation

8.2. Lock-free Data Structures

  • Core idea: Handle critical sections without locks
  • Typically based on hardware support for atomicity guarantees
    • Atomic instructions as explained above
    • Transactional memory, see [LK08]
  • See Section 4.9 of [Hai19]

8.3. “Safer” Programming Languages

  • High-level programming languages may help with MX
  • See [JJK+21] for introduction to Rust
    • Strong type system allows to detect common bugs at compile time
      • Thread safety (absence of race conditions) for shared data structures with compile-time checks
    • Ongoing research into safety proofs
  • (Besides, the OS Redox is implemented in Rust)

8.4. Massively Parallel Programming

  • For massively parallel (big data) processing in clusters or cloud environments, specialized frameworks exist

9. Conclusions

9.1. Summary

  • Parallelism is a fact of life
    • Multi-core, multi-threaded programming
    • Race conditions arise
    • Synchronization is necessary
  • Mutual exclusion for critical section
    • Locking
    • Monitors
    • Semaphores


License Information

This document is part of an Open Educational Resource (OER) course on Operating Systems. Source code and source files are available on GitLab under free licenses.

Except where otherwise noted, the work “OS05: Mutual Exclusion”, © 2017-2023 Jens Lechtenbörger, is published under the Creative Commons license CC BY-SA 4.0.

No warranties are given. The license may not give you all of the permissions necessary for your intended use.

In particular, trademark rights are not licensed under this license. Thus, rights concerning third party logos (e.g., on the title slide) and other (trade-) marks (e.g., “Creative Commons” itself) remain with their respective holders.