Parallel Computing vs Distributed Computing

Parallel Computing

• Parallel computing involves breaking down a problem into smaller parts and solving them simultaneously using multiple processors within a single machine.
• Example: Imagine calculating the sum of numbers from 1 to 100. In parallel computing, you could assign different sections of the range to different processors.
• For instance, one processor handles 1-25, another 26-50, and so on.
• All processors work concurrently, speeding up the overall calculation.

Distributed Computing

• Distributed computing, on the other hand, involves solving a problem by dividing it among multiple computers or nodes connected via a network.
• Each node works on a portion of the problem independently.
• Example: Consider a large dataset that needs analysis.
• In distributed computing, each node may process a subset of the data.
• For instance, one node analyzes data from January, another from February, and so forth.
• The results are then combined for a comprehensive analysis.

Parallel vs. Distributed Computing

Parallel Computing

• Processing on a Single Machine: Parallel computing involves solving a problem by breaking it into smaller tasks.
• Parallel computing involves High Communication, Low Coordination between processors.
• Shared memory architecture, where all processors have access to a common memory pool.
• Limited fault tolerance as a failure in one component can affect the entire system.
• Limited scalability as the number of processors is constrained by the capacity of a single machine.

Distributed Computing

• Processing on Multiple Machines: Distributed computing involves solving a problem by distributing tasks across multiple computers or nodes connected through a network.
• Distributed computing requires High Communication, High Coordination between nodes.
• Each node has its own local memory, and data sharing is achieved through message passing.
• Improved fault tolerance as tasks are distributed, and the failure of one node doesn't necessarily impact the entire system.
• Higher scalability as additional nodes can be added to the network to handle increased workloads.

Elements of Parallel Computing

1. Task Decomposition

• Breaking down a complex problem into smaller tasks that can be solved independently.
• Example: For image processing, task decomposition involves dividing the image into smaller sections.

2. Data Decomposition

• Dividing the data associated with a problem into smaller parts that can be processed simultaneously.
• Example: In weather simulation, data decomposition involves dividing geographical areas into smaller regions.

3. Concurrency

• The ability to execute multiple tasks or processes simultaneously.
• Example: In a parallel computing scenario, multiple processors concurrently execute different parts of a program, improving efficiency and reducing the overall computation time.

4. Synchronization

• Ensuring proper coordination and communication between different processors to maintain order and consistency.
• Example: In parallel computing, synchronization is crucial when tasks depend on each other's results.

5. Memory Access

• Managing how processors access and share memory to avoid conflicts.
• Example: In parallel computing, processors may share data in memory.
• Efficient memory access strategies are crucial to prevent data inconsistencies and ensure accurate computations.

6. Communication

• Establishing efficient communication channels between processors for exchanging information.
• Example: In a parallel system, processors may need to share intermediate results.
• Effective communication ensures timely sharing without significant delays.

7. Load Balancing

• Ensuring an equal distribution of tasks among processors to maximize overall efficiency.
• Example: In a parallel computing environment, load balancing ensures that no processor remains idle while others are overloaded.
• Tasks are distributed evenly for optimal performance.

Conclusion