Concurrency vs Parallelism: Interleaving vs Simultaneous Execution
Overview Concurrency and parallelism are often used interchangeably, but they describe different things: concurrency is about interleaving multiple tasks so a program can make progress on all of them, while parallelism is about simultaneous execution of tasks on separate hardware. A single-core CPU can be concurrent but never truly parallel; a multi-core CPU can be both. Comparison Diagram ConcurrencyParallelism1 CPU CoreABACBtime (single lane, tasks interleave)only one task runs at any instantCore 1Core 2Core 3ABCtime (all run at once)tasks execute simultaneously Comparison Table Aspect Concurrency Parallelism Core definition Structuring a program to make progress on multiple tasks by interleaving them Executing multiple tasks or subtasks at the literal same instant Hardware requirement Works on a single core via context switching Requires multiple cores, processors, or SIMD units Execution pattern Tasks take turns; interleaved progress, order not guaranteed Tasks run simultaneously on separate execution units Task independence Tasks often share state and need coordination to interleave safely Tasks are usually split to run independently, minimizing interference Primary goal Improve responsiveness and structure for handling many things at once Improve throughput by doing more work in the same time Typical workload I/O-bound: network calls, file access, user events CPU-bound: numerical computation, data processing Common pitfalls Race conditions, deadlocks, callback/coordination complexity Synchronization overhead, diminishing returns (Amdahl’s law) Language/runtime constructs Event loops, coroutines, async/await, green threads OS threads, multiprocessing, GPU kernels, SIMD Key Differences Concurrency is a way of structuring code to deal with multiple tasks; it doesn’t require them to run at the same instant. Parallelism requires multiple cores executing work simultaneously, while concurrency runs fine on a single core. Concurrent code can run without being parallel, and parallel code (like SIMD data processing) can run without concurrent structure. Concurrency’s main hazard is a race condition from shared state; parallelism’s main cost is synchronization overhead. Concurrency optimizes for responsiveness; parallelism optimizes for raw computational throughput. When to Use Each Concurrency ...