Async vs Threads vs Parallelism

See the fundamental differences in how they execute tasks

⚡ Async, Threads & Parallelism: What's the Difference?

These three concepts are often confused, but they solve different problems. Understanding when to use each is crucial for writing efficient, scalable applications.

🔄 Async/Await

Single thread that yields during I/O operations.

✓ Low overhead (no thread creation)
✓ Scales to 10,000+ concurrent tasks
✗ CPU-bound work blocks everything

🧵 Threads

OS-managed execution units with preemptive scheduling.

✓ True preemption (OS decides)
✓ Can utilize multiple cores
✗ Context switch overhead

🚀 Parallelism

True simultaneous execution on multiple CPU cores.

✓ Fastest for CPU-bound work
✓ Linear speedup (ideally)
✗ Limited by number of cores

🎬 What to Watch

Notice how async I/O tasks complete while other work continues,threads take turns on a single core, andparallel execution finishes fastest by running all tasks at once.

Async/Await

Single thread - yields during I/O, blocks during CPU

• I/O tasks run "concurrently“ (cooperative)
• CPU tasks block everything
• Great for I/O-bound work

Threads (1 Core)

Multiple threads - preemptive scheduling, one at a time

• OS switches between threads
• Context switch overhead
• No parallelism on single core

Parallel (Multi-Core)

True parallelism - all tasks run simultaneously

• Each task on separate core
• Fastest for CPU-bound work
• Limited by core count

When to Use Each

Aspect	Async/Await	Threads	Parallel
Best for	I/O-bound (network, files)	Mixed workloads	CPU-bound computation
Concurrency	Cooperative (yields)	Preemptive (OS decides)	True simultaneous
Overhead	Very low	Medium	Higher
Scale	10,000+ tasks	100s of threads	# of CPU cores
Race conditions	Between awaits	Everywhere	Everywhere