a2ys/Savant

Threads vs. Processes: When to Use Each and Why

Aayush Shukla's avatar
Aayush Shukla
May 22, 2025 • 5 min read

Introduction

In our previous posts, we’ve explored how operating systems manage processes and the cost of switching between them. Today, we’re diving into a fundamental design decision in software development: should your application use multiple processes, multiple threads, or a combination of both?

This choice affects everything from performance and resource usage to fault isolation and programming complexity. By understanding the trade-offs, you’ll gain insight into why your favorite applications are structured the way they are and how modern software makes the most of multi-core processors.

Table of Contents

Understanding the Basics: Processes vs. Threads

Let’s start with clear definitions:

Processes: Independent Islands

A process is an instance of a program in execution, complete with:

  • Its own private memory space
  • Resource allocations (file handles, network sockets)
  • Security context (user permissions)
  • At least one thread of execution

Processes are isolated from each other by the operating system’s memory protection mechanisms. One process cannot directly access another process’s memory without explicit inter-process communication (IPC) mechanisms.

Threads: Collaborative Workers

A thread is a unit of execution within a process. A process can have multiple threads, all of which:

  • Share the same memory space
  • Have access to the same files and resources
  • Run in the same security context
  • Execute concurrently

Threads within a process can directly access each other’s memory, making communication between threads much simpler than communication between processes.

The Architectural Differences Visualized

Think of processes like separate apartments in a building, while threads are like roommates sharing a single apartment:

Process Architecture

Process A       Process B       Process C
----------      ----------      ----------
| Memory |      | Memory |      | Memory |
| Files  |      | Files  |      | Files  |
| Thread |      | Thread |      | Thread |
----------      ----------      ----------

Thread Architecture

Process A
      -------------------------
      |   Shared Memory       |
      |   Shared Files        |
      |                       |
      |  Thread A  Thread B   |
      |                       |
      -------------------------

This architecture illustrates how processes are isolated from each other, while threads within a process share the same resources.

What This Means in Practice

Process isolation
  • Each process has its own virtual memory space (typically 4GB on 32-bit systems)
  • A crash in Process A cannot directly affect Process B
  • Communication requires explicit mechanisms (pipes, sockets, shared memory)
Thread sharing
  • All threads see the same memory addresses and can access the same variables
  • One thread’s memory corruption can crash the entire process
  • Communication is as simple as reading/writing shared variables (with proper synchronization)

Types of Threads

Threads can be implemented in different ways, each with its own characteristics:

Kernel-Level Threads

The operating system kernel manages threads directly:

  • Pros:

    • True parallelism on multi-core systems
    • One thread blocking doesn’t block others
    • Kernel can schedule threads individually

  • Cons:

    • Higher overhead (thread operations require system calls)
    • Limited scalability (thousands, not millions)

Examples: POSIX threads (pthreads), Windows threads

User-Level Threads

Threads managed by a runtime library without kernel involvement:

  • Pros:

    • Lower creation/context switch overhead
    • More control over scheduling
    • Massive scalability (potentially millions)

  • Cons:

    • Blocking calls can stop all threads
    • No true parallelism without additional mechanisms

Examples: Go goroutines, Erlang processes

Hybrid Threading Models

Combine user and kernel-level approaches:

  • Pros:

    • Balance between performance and parallelism
    • Better scalability than pure kernel threads
    • Can map many user threads to fewer kernel threads

  • Cons:

    • More complex implementation
    • May still suffer from some limitations of both models

Examples: Java threads, .NET thread pool

Thread Models

Checkout images of thread models at ResearchGate

Thread models define how threads are scheduled and managed within a process. The two main models are:

Many-to-One Model

In this model, many user-level threads are mapped to a single kernel thread. The kernel is unaware of the user threads, which can lead to inefficiencies:

  • Pros:

    • Low overhead for thread management
    • Fast context switching
    • Simple implementation
    • Good for I/O-bound applications
    • No kernel involvement

  • Cons:

    • Blocking a single thread blocks the entire process
    • Limited to a single CPU core (no true parallelism)
    • Difficult to utilize multi-core systems

Examples: Green threads in Java, some implementations of user-level threads

One-to-One Model

In this model, each user-level thread is mapped to a kernel thread. This allows for true parallelism and better resource utilization:

  • Pros:

    • True parallelism on multi-core systems
    • Blocking a thread doesn’t block the entire process
    • Kernel can schedule threads individually
    • Better for CPU-bound applications

  • Cons:

    • Higher overhead for thread management
    • More complex implementation
    • Limited by the number of kernel threads available

Examples: POSIX threads (pthreads), Windows threads, Java threads

Many-to-Many Model

In this model, many user-level threads are mapped to many kernel threads. This allows for a flexible and efficient use of system resources:

  • Pros:

    • True parallelism on multi-core systems
    • Blocking a thread doesn’t block the entire process
    • Kernel can schedule threads individually
    • Better for both CPU-bound and I/O-bound applications
    • Scalable and efficient
    • Can utilize a large number of threads

  • Cons:

    • More complex implementation
    • Higher overhead for thread management
    • Requires a sophisticated scheduler

Common Patterns and Use Cases

Different scenarios call for different approaches:

When to Use Multiple Processes

  1. Security isolation is critical

    • Web browsers (separate processes for each tab)
    • Financial applications handling sensitive data
    • Enterprise applications with strict access controls

  2. Fault isolation is essential

    • Critical systems where one component failure shouldn’t bring down the whole system
    • Applications where different components have different stability profiles
    • Long-running services that need to survive partial failures

  3. Different programming languages need to work together

    • Legacy components integrating with modern code
    • Specialized libraries only available in certain languages
    • Cross-platform scenarios with platform-specific components

  4. Independent scaling is needed

    • Microservices architectures
    • Server components with different resource needs
    • Stateless vs. stateful component separation

When to Use Multiple Threads

  1. Shared data access is frequent

    • Database engines
    • In-memory caches
    • Data processing pipelines

  2. Low-latency communication is required

    • Real-time applications
    • Gaming engines
    • High-frequency trading systems

  3. Resource constraints are tight

    • Mobile applications
    • Embedded systems
    • Resource-intensive applications that need efficiency

  4. Programming simplicity is valued

    • Applications where direct data sharing simplifies design
    • When the application is inherently partitioned into concurrent tasks
    • When synchronization needs are minimal or well-structured

When to Use Both

Many modern applications use a hybrid approach:

  1. Process-per-major-component, threads-within-process

    • Web servers (process per worker, threads for connections)
    • Multimedia applications (process per major function, threads for tasks)
    • IDEs (separate processes for compile/debug, threads for UI)

  2. Process for isolation, threads for parallelism

    • Content creation tools (separate process for rendering, threads for UI)
    • Scientific computing (process per job, threads for parallelism)
    • Server applications (process per user, threads for tasks)

Coming Up Next

Now that we understand the differences between processes and threads, our next post will explore how modern operating systems make the most of multi-core processors. We’ll dive into Multi-core Magic: How Modern OSes Handle Multiple CPUs and examine how your favorite applications leverage multiple cores.