Explain the Difference Between Multithreading and Multiprocessing

Concept

Both multithreading and multiprocessing aim to execute multiple tasks concurrently, but they differ in how they use CPU resources and memory.

Multithreading runs multiple threads within a single process — sharing memory space.
Multiprocessing runs multiple processes — each with its own memory and system resources.

Understanding their trade-offs is crucial for building scalable, performant systems.

1. Multithreading

Multiple threads share the same memory space within one process.
Lightweight and faster to create and switch between.
Ideal for I/O-bound tasks (e.g., web servers, GUI responsiveness).
Requires synchronization to prevent race conditions.

Example (safe for MDX):

Thread 1 → read file
Thread 2 → compress data
Thread 3 → write result

All threads share global variables and heap memory.

2. Multiprocessing

Each process runs in its own address space.
More memory-intensive but provides true parallelism on multi-core CPUs.
Ideal for CPU-bound tasks (e.g., scientific computation, ML model training).
Safer — process crashes don’t affect others.

Example (safe for MDX):

Process 1 → handles API requests
Process 2 → performs analytics
Process 3 → manages background jobs

Each process has its own copy of data and runs independently.

3. Key Differences

Aspect	Multithreading	Multiprocessing
Memory	Shared within one process	Separate per process
Communication	Via shared variables	Via IPC (pipes, sockets, queues)
Performance	Lightweight, less overhead	Heavyweight, more isolation
Parallelism	Limited by GIL (in some languages like Python)	True parallelism across cores
Fault Isolation	Low — crash affects all threads	High — one process crash isolated
Use Case	I/O-bound tasks	CPU-bound computations

4. Real-World Analogy

Multithreading: Multiple employees working on different tasks in the same office (shared resources, quick communication).
Multiprocessing: Employees in separate offices (independent work, higher safety, slower communication).

5. Performance Considerations

Threading Advantages:
- Lower memory footprint.
- Easier data sharing.
- Faster context switching.
Threading Risks:
- Race conditions, deadlocks, data corruption.
Multiprocessing Advantages:
- Full CPU utilization on multi-core systems.
- Fault isolation.
Multiprocessing Risks:
- IPC overhead.
- Higher memory consumption.

6. Practical Use Cases

Scenario	Recommended Model
Web servers handling concurrent requests	Multithreading or async I/O
Image/video processing pipelines	Multiprocessing
Background job execution (Celery, Resque)	Multiprocessing
Interactive desktop apps	Multithreading
Machine learning model training	Multiprocessing or distributed computing

7. In Popular Languages

Language	Thread Model	Process Model
Java	True multithreading	Separate JVM instances
Python	Threading limited by GIL; use multiprocessing for CPU tasks	`multiprocessing` module
C/C++	POSIX threads (`pthread`)	`fork()` or multiple executables
Go	Goroutines (lightweight threads)	Multiprocessing via OS processes

8. Interview Tip

Clarify that threads share memory, while processes do not.
Mention trade-offs: safety (multiprocessing) vs efficiency (multithreading).
If asked about Python, highlight the GIL limitation.
Provide concrete examples: “Web servers → threads, data pipelines → processes.”

Summary Insight

Multithreading is about sharing work; multiprocessing is about dividing it. Threads cooperate, processes isolate — both are pillars of scalable system design.