Parallelism is a computing concept where multiple tasks are executed simultaneously to improve performance and efficiency. It’s widely used in modern software development to handle complex computations and large-scale data processing.

Key Concepts of Parallelism

  • Concurrency vs. Parallelism: Concurrency is about handling multiple tasks in a way that they appear to run at the same time, while parallelism actually executes them simultaneously.
  • Thread Management: Using threads to divide work into smaller units, such as Multi_Thread or Thread_Pool.
  • Distributed Computing: Leveraging multiple machines or nodes (e.g., Distributed_Computing) to share the workload.

Use Cases

  • Data Processing: Analyzing large datasets efficiently (e.g., Parallel_Processing).
  • Game Development: Simulating real-time physics and rendering (e.g., Game_Threads).
  • Scientific Computing: Accelerating simulations and calculations (e.g., Scientific_Calculations).

Best Practices

  • Avoid shared state conflicts (e.g., Thread_Safety).
  • Use appropriate synchronization mechanisms (e.g., Mutex, Lock).
  • Optimize task granularity for better resource utilization.

For deeper insights, check our tutorial on Concurrency to understand how it differs from parallelism.

Parallelism Overview
Multithreading Example
Distributed Computing