Concurrency vs Parallelism

Concurrency is about structure — designing a program to handle multiple tasks.

Parallelism is about execution — actually running multiple tasks simultaneously.

"Concurrency is about dealing with lots of things at once. Parallelism is about doing lots of things at once."

— Rob Pike

The Distinction

Aspect	Concurrency	Parallelism
What	Managing multiple tasks	Executing multiple tasks
How	Interleaving, switching	Simultaneous on multiple cores
Requires	Design/structure	Hardware (multiple CPUs)
Analogy	One chef juggling multiple dishes	Multiple chefs cooking at once

Key Insight

Concurrency without parallelism: One CPU switching between tasks (your "reading book and checking phone" example — one brain, multiple tasks)
Parallelism without concurrency: SIMD — same instruction on multiple data, no task management needed
Both: Multiple CPUs, each handling concurrent tasks

Where Async Fits

Async is a programming model for concurrency — it lets you express "start this, don't wait, continue" without blocking.

Model	Mechanism
Threads	OS-managed, preemptive switching
Async/Await	Programmer-managed, cooperative yielding
Actors	Message-driven, isolated state

All three are ways to achieve concurrency. Parallelism is whether they run on multiple cores.

Your Pipeline Context

Your arbitrage engine is concurrent (multiple data sources, multiple comparisons) and can be parallel (goroutines scheduled across cores). The sharding strategy determines how concurrency maps to parallelism.