The Truth of Sisyphus
  • Introduction
  • Deep Learning
    • Basics
      • Hinge Loss
      • Regularizations
      • Linear Classification
      • Multi-Class and Cross Entropy Loss
      • Batch Norm and other Normalizations
      • Optimization
      • Optimization Functions
      • Convolution im2col
      • Activation Functions
      • Derivatives
        • Derivatives of Softmax
        • A Smooth (differentiable) Max Function
      • Model Ensemble
      • Layers Python Implementation
    • Classification
      • Mobile friendly networks
      • Non-local Neural Networks
      • Squeeze-and-Excitation Networks
      • Further Attention Utilization -- Efficience & Segmentation
      • Group Norm
      • ShuffleNet V2
    • Segmentation
      • Several Instance Segmentation
      • A Peek at Semantic Segmentation
      • Design Choices for Mobile Friendly Deep Learning Models, Semantic Segmentation
      • Efficient Video Object Segmentation via Network Modulation
      • BiSeNet
      • DeepLabV3+
    • Detection
      • CornerNet
      • IoU-Net
      • Why smooth L1 is popular in BBox Regression
      • MTCNN-NCNN
      • DetNet
      • SSD Illustration
    • RNN Related
      • GRU vs LSTM
      • BERT
    • Reinforcement Learning
      • AutoML in Practice Review
      • DRL for optimal execution of profolio transaction
    • Multi-task
      • Multi-task Overview
      • What are the tricks in Multi-Task network design?
    • Neural Network Interpretation
      • Neuron Visualization
    • Deep Learning Frameworks
      • How does Caffe work
      • [Gluon] When to use (Hybrid)Sequential and (Hybrid)Block
      • Gluon Hybrid Intro
      • Gluon HybridBlocks Walk-Through
      • A quick tour of Torch internals
      • NCHW / NHWC in Pytorch
      • Static & Dynamic Computation Graph
    • Converting Between DL Frameworks
      • Things To Be Considered When Doing Model Converting
      • Caffe to TensorFlow
    • Computation Graph Optimization
      • Two ways of TensorRT to optimize Neural Network Computation Graph
      • Customized Caffe Memory Optimization
      • NCNN Memory Optimization
      • Symbolic Programs Advantages: More Efficient, Reuse Intermediate Memory, Operation Folding
    • Deep Learning Debug
      • Problems caused by dead ReLU
      • Loss jumps to 87.3365
      • Common Causes of NANs During Training
    • Deployment
      • Efficient Convolution Operation
      • Quantization
    • What I read recently
      • Know Google the Paper Way
      • ECCV 2018
      • Neural Machine Translation
      • Street View OCR Extraction System
      • Teaching Machines to Draw
      • Pixel to Graph
      • Burst Image Deblurring
      • Material for Masses
      • Learning to Separate Object Sounds by Watching Unlabeled Video
    • Papers / Posts to be read
    • Dummy thoughts
  • Machine Learning
    • Classification
    • Regression
    • Clustering
    • Dimension Reduction
    • Metrics
    • Regularization
    • Bayesian Example
    • Machine Learning System Design
    • Recommendation
    • Essentials of Machine Learning
    • Linear Regression
    • Logistic Regression
      • Logistic Function
    • Gaussian Discriminant Analysis
    • Naive Bayes
    • SVM
    • MLE vs MAP
    • Boosting
    • Frequent Questions
    • Conclusion of Machine Learning
  • Python notes
    • Python _ or __ underscores usage
    • Python Multiprocess and Threading Differences
    • Heapq vs. Q.PriorityQueue
    • Python decorator
    • Understanding Python super()
    • @ property
    • Python __all__
    • Is Python List a Linked List or Array
    • What is the "u" in u'Hello world'
    • Python "self"
    • Python object and class
    • Python Class' Instance method, Class method, and Static Methods Demystified
    • Python WTF
    • Python find first value index in a list: [list].index(val)
    • Sort tuples, and lambda usecase
    • Reverse order of range()
    • Python check list is empty
    • Python get ASCII value from character
    • An A-Z of useful Python tricks
    • Python nested function variable scope
    • Python reverse a list
    • Python priority queue -- heapq
  • C++ Notes
    • Templates
    • std::string (C++) and char* (or c-string "string" for C)
    • C++ printf and cout
    • Class Member Function
    • Inline
    • Scope Resolution Operator ::
    • Constructor
    • Destructor
    • Garbage Collection is Critical
    • C++ Question Lists
  • Operating System
    • Basics
    • Mutex & Semaphore
    • Ticket Selling System
    • OS and Memory
    • Sort implementation in STL
    • Compile, link, loading & run
    • How to understand Multithreading and Multiprocessing from the view of Operating System
  • Linux & Productivity
    • Jupyter Notebook on Remote Server
    • Nividia-smi monitoring
  • Leetcode Notes
    • Array
      • 11. Container With Most Water
      • 35. Search Insert Position
    • Linked List
      • Difference between Linked List and Array
      • Linked List Insert
      • Design of Linked List
      • Two Pointers
        • 141. Linked List Cycle
        • 142. Linked List Cycle II
        • 160. Intersection of two Linked List
        • 19. Remove N-th node from the end of linked list
      • 206. Reverse Linked List
      • 203. Remove Linked List Elements
      • 328. Odd Even Linked List
      • 234. Palindrome Linked List
      • 21. Merge Two Sorted Lists
      • 430. Flatten a Multilevel Doubly Linked List
      • 430. Flatten a Multilevel Doubly Linked List
      • 708. Insert into a Cyclic Sorted List
      • 138. Copy List with Random Pointer
      • 61. Rotate List
    • Binary Tree
      • 144. Binary Tree Preorder Traversal
      • 94. Binary Tree Iterative In-order Traverse
    • Binary Search Tree
      • 98. Validate Binary Search Tree
      • 285. Inorder Successor in BST
      • 173. Binary Search Tree Iterator
      • 700. Search in a Binary Search Tree
      • 450. Delete Node in a BST
      • 701. Insert into a Binary Search Tree
      • Kth Largest Element in a Stream
      • Lowest Common Ancestor of a BST
      • Contain Duplicate III
      • Balanced BST
      • Convert Sorted Array to Binary Search Tree
    • Dynamic Programming
      • 198. House Robber
      • House Robber II
      • Unique Path
      • Unique Path II
      • Best time to buy and sell
      • Partition equal subset sum
      • Target Sum
      • Burst Ballons
    • DFS
      • Clone Graph
      • General Introduction
      • Array & String
      • Sliding Window
  • Quotes
    • Concert Violinist Joke
    • 船 Ship
    • What I cannot create, I do not understand
    • Set your course by the stars
    • To-do list
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On this page
  • Mutex
  • Semaphore
  • Mutex vs Binary Semaphore
  1. Operating System

Mutex & Semaphore

Mutex

Is a key to a toilet. One person can have the key - occupy the toilet - at the time. When finished, the person gives (frees) the key to the next person in the queue.

Officially: "Mutexes are typically used to serialize access to a section of re-entrant code that cannot be executed concurrently by more than one thread. A mutex object only allows one thread into a controlled section, forcing other threads which attempt to gain access to that section to wait until the first thread has exited from that section." Ref: Symbian Developer Library

(A mutex is really a semaphore with value 1.)

Semaphore

Is the number of free identical toilet keys. Example, say we have four toilets with identical locks and keys. The semaphore count - the count of keys - is set to 4 at beginning (all four toilets are free), then the count value is decremented as people are coming in. If all toilets are full, ie. there are no free keys left, the semaphore count is 0. Now, when eq. one person leaves the toilet, semaphore is increased to 1 (one free key), and given to the next person in the queue.

Officially: "A semaphore restricts the number of simultaneous users of a shared resource up to a maximum number. Threads can request access to the resource (decrementing the semaphore), and can signal that they have finished using the resource (incrementing the semaphore)." Ref: Symbian Developer Library

Mutex vs Binary Semaphore

They are NOT the same thing. They are used for different purposes! While both types of semaphores have a full/empty state and use the same API, their usage is very different.

They have different purposes. Mutex is for exclusive access to a resource. A Binary semaphore should be used for Synchronization (i.e. "Hey Someone! This occurred!"). The Binary "giver" simply notifies whoever the "taker" that what they were waiting for happened.

Mutual Exclusion Semaphores

Mutual Exclusion semaphores are used to protect shared resources (data structure, file, etc..).

A Mutex semaphore is "owned" by the task that takes it. If Task B attempts to semGive a mutex currently held by Task A, Task B's call will return an error and fail.

Mutex always use the following sequence:

  - SemTake
  - Critical Section
  - SemGive

Here is a simple example:

  Thread A                     Thread B
   Take Mutex
     access data
     ...                        Take Mutex  <== Will block
     ...
   Give Mutex                     access data  <== Unblocks
                                  ...
                                Give Mutex

Binary Semaphore

Binary Semaphore address a totally different question:

  • Task B is pended waiting for something to happen (a sensor being tripped for example).

  • Sensor Trips and an Interrupt Service Routine runs. It needs to notify a task of the trip.

  • Task B should run and take appropriate actions for the sensor trip. Then go back to waiting.


   Task A                      Task B
   ...                         Take BinSemaphore   <== wait for something
   Do Something Noteworthy
   Give BinSemaphore           do something    <== unblocks

Note that with a binary semaphore, it is OK for B to take the semaphore and A to give it. Again, a binary semaphore is NOT protecting a resource from access. The act of Giving and Taking a semaphore are fundamentally decoupled. It typically makes little sense for the same task to so a give and a take on the same binary semaphore.

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Last updated 6 years ago

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