Distributed programming is a field that orchestrates a network of computers to perform tasks collaboratively, enhancing performance and reliability. It encompasses concurrency, distribution, and various programming models like message-passing and shared memory. The text delves into strategies for achieving system reliability and security, highlighting practical applications and development tools such as Hadoop and TensorFlow.
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Distributed programming involves a network of computers working together to execute tasks
Efficient Resource Use
Distributed programming utilizes multiple computing resources to improve performance
Fault Tolerance
Distributed programming ensures system reliability by utilizing multiple computers
Effective Network Communication
Distributed programming addresses challenges in network communication
Distributed programming includes concurrent and distributed programming paradigms with distinct techniques
Concurrency enables multiple tasks to be executed simultaneously
Distribution refers to the operation of interconnected computers collaborating to perform tasks
Definition of Synchronization
Synchronization mechanisms manage concurrent tasks to avoid issues such as deadlocks and race conditions
Types of Synchronization Mechanisms
Locks, monitors, semaphores, and atomic operations are used to regulate access to shared resources and coordinate task execution
The message-passing model involves processes communicating by sending and receiving messages
The shared memory model allows multiple threads to access a common memory space
The data parallel model is effective for tasks that require the same operation to be performed on separate data segments
Parallel programming occurs on multi-core processors within a single machine
Distributed computing involves a network of separate computers working collectively to accomplish a shared objective
Divide and Conquer
The Divide and Conquer approach segments a problem into smaller parts, solves them independently, and then combines the results
Pipeline Processing
Pipeline processing sequentially executes a series of computational operations, enhancing throughput and supporting modularity and scalability
Error Detection and Correction
Error detection and correction techniques help maintain system stability and data accuracy
Data Replication
Data replication is used to ensure system reliability
Consistency Protocols
Consistency protocols help maintain data accuracy in distributed systems
Authentication and Authorization Protocols
Authentication and authorization protocols regulate access to distributed systems
Encrypted Communication Channels
Encrypted communication channels protect data during transmission
Data Encryption
Data encryption is used for secure storage in distributed systems
Distributed programming is applied in fields such as distributed search engines, online gaming, and scientific research
Apache Hadoop
Apache Hadoop enables large-scale data processing
TensorFlow
TensorFlow supports distributed machine learning
Message Passing Interface (MPI)
MPI standardizes communication for parallel computing
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In ______ programming, techniques like synchronization and message-passing are vital for building scalable and secure systems.
distributed
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Concurrency in distributed systems
Allows multiple tasks to run in parallel, improving processing power utilization and system efficiency.
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Role of distribution in distributed systems
Connects computers to collaborate on tasks, enhancing load balancing, scalability, and fault tolerance.
