What problems are you familiar with regarding parallelization?

Race Conditions: Multiple threads access shared data simultaneously, and the order of access affects the outcome.

Deadlock: Two or more threads wait for resources held by others, forming a cycle of dependencies.

Starvation: A low-priority or poorly scheduled thread cannot access necessary resources, while others use them.

Testing and Debugging: Parallel systems are harder to debug due to unpredictable thread execution order and timing-dependent results.

Resource Load: Excessive threads can lead to high memory, CPU usage, and increased context switching overhead.

Non-determinism: The results of a parallel program can vary with each run due to different thread execution orders, complicating error reproduction.

Data Sharing and Synchronization: Proper data partitioning and synchronization mechanisms (mutexes, semaphores, barriers) are essential to prevent issues, but incorrect use can cause other problems.

Scalability: Efficient parallelism on more cores/processors does not always scale linearly due to synchronization and coordination overheads.

Overhead of Thread Creation and Management: Creating, starting, and terminating threads require time and resources.

Compatibility with Libraries and Frameworks: Some libraries are not thread-safe, requiring caution when used in parallel code.