Common Parallelization Strategies

 

Parallelization is a fundamental technique in high-performance computing that aims to divide a computational task into smaller, independent subtasks that can be executed concurrently on multiple processors or computers 

key parallelization strategies:

 1. Data Parallelism: 
 In data parallelism, multiple processors or machines work on different portions of a large dataset simultaneously.
 
Advantages: 
o Can be easily extended to more processors for significant performance gains as the dataset size increases. 
Disadvantages:  Requires each processor to have enough memory to hold its assigned data chunk. 
2. Model Parallelism: In model parallelism, a large computational model (e.g., a deep learning neural network) is partitioned across multiple processors.
 
Advantages: 
 Can be used for models that wouldn't fit on a single processor's memory. 
 Disadvantages: 
Communication overhead can become a significant bottleneck if not
 carefully optimized. 
3. Task Parallelism:
 In task parallelism, a computational task is broken down into smaller, independent subtasks that can be executed concurrently. 
Advantages: 
 Allows for fine-grained control over task scheduling and execution. 
 
Disadvantages: 
 May involve overhead in creating and managing tasks, especially for large numbers of small tasks.