Parallel Histogram Aggregation

Parallel histogram aggregation with Python threading

This is a medium-difficulty concurrency problem that tests whether you can decompose a data-parallel task, coordinate work across multiple threads, and safely merge partial results. It mirrors real GPU/multi-core workflows where each processor builds a local aggregate and the results are combined at the end.

The core challenge is partitioning the input fairly, launching worker threads that each maintain their own dictionary without synchronization overhead, and then merging all local histograms into a single global result. Interviewers focus on whether your thread management is clean, your partitioning logic handles edge cases, and you understand when synchronization is necessary and when it is not.

• Thread spawning and joining with Python's threading module
• Data partitioning and load distribution
• Thread-safe accumulation and merging strategies
• Handling empty input and thread-count edge cases