A process is a running program together with its operating-system-managed resources; a thread is a path of execution scheduled within a process. Threads in one process can share data and other resources, which can make coordination direct but also makes synchronization essential. Separate processes offer a stronger isolation boundary and can still communicate through explicit mechanisms. Neither model is universally faster: the right choice depends on the workload, runtime, communication needs, and failure boundaries.
What is a process?
An application can consist of one or more processes. A process is an executing program with its own execution context and assigned resources; it may contain one or many threads. The process provides the context in which those threads operate, rather than being a single instruction stream itself. Microsoft Learn’s overview of processes and threads describes this relationship.
Processes are commonly used as separation boundaries: one process does not ordinarily have direct access to another process’s private state. That separation can help limit accidental interference, but it does not make communication impossible. Processes can exchange information through inter-process communication (IPC) or use deliberately shared memory.
What is a thread?
A thread is an execution path within a process. The operating system schedules threads to run and allocates processor time to them. Microsoft Learn puts it directly: “A thread is the basic unit to which the operating system allocates processor time.”
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A process can have multiple threads, allowing different parts of its work to make progress within the same process context. Threads in a process share important resources, including global data and heap memory, while each thread has its own stack. The Linux man-pages project documents this sharing model for POSIX threads in pthreads(7).
Do threads share memory?
Threads in the same process share the process’s resources, including access to global memory and heap-allocated data. Each thread has its own stack, so its function calls and local execution state are not simply one shared stack. This combination—shared process data, separate per-thread execution state—is the key distinction to keep in mind.
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Shared memory makes it possible for threads to work directly with common data, but it also creates a coordination obligation. If multiple threads read and change shared state without suitable synchronization, they can observe inconsistent values or interfere with one another. The Python execution model describes this general hazard and the need to coordinate access to shared resources: Python execution model.
Concurrency is not the same as parallelism
Concurrency means multiple tasks can make progress over overlapping periods; it does not necessarily mean they execute at precisely the same instant. Physical parallelism—work running simultaneously on multiple processors—depends on the host, available processors, scheduler, and runtime. Python’s execution-model documentation makes this distinction explicitly. The terms therefore describe different properties: concurrency is about overlapping progress, while parallelism is about simultaneous execution.
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Process vs. thread: the practical trade-offs
| Question | Threads in one process | Separate processes |
|---|---|---|
| How is state accessed? | Threads share important process resources, including global memory and heap data; each has its own stack. | Processes are more isolated by default. Data exchange requires IPC or an explicit shared-memory mechanism. |
| What coordination is needed? | Shared mutable state needs deliberate synchronization to avoid races and inconsistent observations. | Communication is more explicit, but the application must manage the IPC or shared-memory design. |
| What does isolation provide? | Threads collaborate within one process context, so they do not provide the same separation boundary as separate processes. | Separate process contexts can reduce accidental sharing and provide a stronger separation boundary. |
| What determines performance? | Workload, I/O waits, runtime behavior, operating system, and implementation details all matter; there is no universal speed advantage. | Those same factors matter, as do process creation and communication costs, which vary by system and workload. |
When should you use threads vs. processes?
Threads may fit when shared data and close collaboration matter
Threads can be appropriate when tasks need frequent direct access to shared in-process data and the program can safely coordinate access to it. They are also a way to structure concurrent work within a process. Whether they improve responsiveness or throughput depends on what the tasks do and how the runtime handles them.
Processes may fit when separation or process-based parallelism matters
Separate processes are worth considering when stronger separation between workers is useful, or when the runtime and workload make process-based parallelism a good fit. Choose them with the understanding that data exchange is more explicit: processes may communicate through queues, other IPC, or shared memory, but do not share ordinary process memory in the same way threads do.
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Make the choice against the workload, not a slogan
- State sharing: Do workers need frequent direct access to the same mutable data, or can they exchange messages?
- Isolation: Is a separate process boundary useful, or does the work depend on close in-process collaboration?
- Coordination: Can the program correctly synchronize shared data, or is explicit communication preferable?
- Workload and runtime: Is the work CPU-bound or often waiting for I/O? What does the language runtime permit, and how does the target operating system behave?
- Lifecycle and portability: How are workers created, started, and cleaned up on the systems where the application must run?
These questions matter more than a blanket claim that threads are lighter or processes are faster. Costs and benefits depend on the particular runtime, operating system, workload, and implementation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Python example: multiprocessing and the GIL
Python’s multiprocessing package provides process-based parallelism and can sidestep the Global Interpreter Lock (GIL) by using subprocesses, allowing a program to use multiple processors. This is a Python-specific runtime detail, not a general rule about operating systems or other languages. The package’s API intentionally resembles threading, but processes still require decisions about communication, shared state, lifecycle, and cleanup. See the Python multiprocessing documentation.
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Do not assume one process start method applies everywhere. Python documents platform-specific caveats and advises library authors to let callers provide a multiprocessing context, since start-method choices can affect compatibility. If you are building a library, follow that guidance rather than silently imposing a context on an application.
Further reading
For a broader course in operating-system concepts—including processes, memory, threads, and concurrency—the authors’ official Operating Systems: Three Easy Pieces site identifies Version 1.10 and provides the book online for free. A print copy is optional, not necessary to understand the material.
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