When preparing for technical interviews, you will often encounter questions surrounding concurrency and parallelism. Typical questions might include:

• "What is the difference between concurrency and parallelism?"
• "How do you handle concurrency in your applications?"
• "What challenges did you face with parallelism, and how did you overcome them?"

These questions evaluate your understanding of handling multiple tasks simultaneously, your practical experience, and your strategies to address common challenges in concurrent and parallel systems.

Concurrency vs. Parallelism

• Concurrency: Refers to multiple tasks making progress simultaneously. It doesn't necessarily mean they are executing at the same time but manage the tasks in overlapping time periods.
• Parallelism: Involves the simultaneous execution of tasks; multiple tasks are performed exactly at the same time using multiple processors or cores.

Why It Matters: Understanding the difference allows you to choose the best approach based on the system's needs, resource availability, and the nature of the tasks.

Concurrency Control Techniques

• Locks and Mutexes: Prevent simultaneous access to shared resources.
• Semaphores: Manage access via signaling mechanisms.
• Transactional Memory: Allows concurrent read/write operations in a controlled manner.
• Concurrent Data Structures: Such as java.util.concurrent in Java, provide built-in thread safety.

Why It Matters: Handling concurrency effectively ensures data integrity and system stability. Incorrect concurrency control can lead to issues like race conditions or deadlocks.

Challenges with Parallelism

• Data Dependency: When tasks depend on the results of each other, it becomes difficult to parallelize without significant overhead.
• Load Balancing: Ensuring that tasks are distributed optimally across resources to avoid bottlenecks.