Concurrency vs. Parallelism: What’s the Difference?

OpenCL vs OpenGL

Concurrency vs. Parallelism: What’s the Difference?

Concurrency and parallelism are two essential concepts in computer science that often need to be explained.

Concurrency refers to the capability of running multiple computations at once, which can be accomplished using a single processing unit. This is achieved through interleaving processes on the central processing unit (CPU) or context switching, which increases the amount of work completed at once.

Parallelism, on the other hand, involves running multiple computations simultaneously that cannot be accomplished with a single processing unit. Parallelism requires multiple central processing units (CPUs) to increase the system’s throughput and computational speed. Furthermore, this approach uses a deterministic control flow approach with simpler debugging than concurrency.

Concurrency refers to the simultaneous execution of multiple tasks, while parallelism refers to performing many operations at once. Both concepts play a vital role in designing and optimizing computer systems, so understanding their distinctions will enable developers to choose the most suitable approach for their applications.

Let’s break them down in further detail below.

Concurrency vs. Parallelism: Side-by-Side Comparison

DefinitionRunning and managing multiple computations simultaneouslyExecuting multiple computations simultaneously
Achieved byInterleaving processes on one CPUMultiple CPUs
Processing UnitCan be accomplished using a single processing unitNeeds multiple processing units
Work FinishedIncreases productivity by increasing work done simultaneouslyEnhances throughput and computational speed of the system
ApproachNon-deterministic control flow approachDeterministic control flow approach
DebuggingDebugging is very hardDebugging is also hard but simpler than concurrency

Concurrency vs. Parallelism: What’s the Difference?

Concurrency and parallelism are two concepts in computer science that often get lumped together, yet their differences could influence your decisions when designing or optimizing a system. Here, we’ll take a closer look at each concept to highlight their distinctions.

Approach to Handling Multiple Computations

Concurrency and parallelism are two distinct approaches for handling multiple computations. The primary distinction lies in how they manage and execute multiple tasks simultaneously.

Concurrency is the concept of running and managing multiple computations simultaneously on the CPU by interleaving their operations. In other words, concurrency is achieved through context switching, wherein the CPU switches back and forth between different processes to give the illusion that multiple tasks are running at once.

Conversely, parallelism refers to the practice of running multiple computations simultaneously using multiple CPUs. Each CPU is assigned a distinct task, and all these operations occur concurrently, giving rise to the true parallel execution of tasks.

Number of Processing Units Required

Concurrency and parallelism differ in the number of processors necessary to execute multiple tasks simultaneously.

One processing unit, such as a single-core CPU, can achieve concurrency by interleaving processes on the chip. This enables it to execute multiple tasks simultaneously with only one CPU.

Parallelism, on the other hand, requires multiple processing units to execute multiple tasks at once. Multiple CPUs can be utilized simultaneously for various tasks to ensure true parallel execution of jobs.

Control Flow Approach and Debugging

Concurrency and parallelism differ in their control flow approach and the difficulty of debugging issues.

Concurrency relies on a non-deterministic control flow model, making it impossible to predict the order of task execution. This makes debugging more challenging as it becomes difficult to pinpoint exactly when tasks are being executed.

Conversely, a deterministic control flow approach emphasizes parallelism, allowing you to anticipate tasks ahead of time and simplify debugging since you know precisely the order in which your tasks will execute.

Debugging can be challenging in both concurrency and parallelism, though it tends to be simpler in parallel due to its deterministic control flow approach.

Resource Management

Resource management is an integral aspect of both concurrency and parallelism. Concurrency, when multiple tasks are running on one processor, requires efficient resource management to guarantee each task gets its fair share of resources. Context switching allows the CPU to quickly switch between different tasks at regular intervals, but mishandling it can lead to unnecessary overhead and decreased performance.

On the other hand, parallelism involves multiple processors or cores, and each core can handle its own task simultaneously. Resource management in parallelism is simpler than concurrency since each core can operate independently without the need for context switching. Furthermore, parallelism makes better use of available resources, which leads to faster execution times and improved performance overall.

Fault Tolerance

Fault tolerance refers to the ability of a system to continue functioning even when one or more components fail. With concurrency, since multiple tasks are running concurrently on one processor, any failure in one task could impact all processes. Debugging and fixing such errors is often difficult since it’s difficult to pinpoint their cause.

Parallelism allows each core to focus on its own task, so a failure in one core does not necessarily impact all others. Parallelism also offers redundancy, as multiple cores can handle the same task simultaneously. This ensures that even if one core fails, the others can continue to execute it and keep your system functioning optimally.

Programming Model

Concurrency and parallelism necessitate different programming models in order to produce desired results. Task execution in concurrency occurs in an unpredictable fashion, meaning that no predeterminism exists regarding task order. This can result in race conditions, where the success or failure of the program depends on when tasks complete.

Concurrency requires programming models that utilize locks, semaphores, or other synchronization mechanisms to coordinate task execution. Unfortunately, this makes the code more complex and challenging to debug.

Parallelism allows tasks to execute in an ordered fashion, eliminating the need for synchronization mechanisms to coordinate task execution. This simplifies programming models since no synchronization mechanisms are needed to guarantee task consistency.

Moreover, parallel programming models can take advantage of the inherent parallelism in a problem domain, leading to simpler and more efficient code. However, parallelism may introduce new challenges like load balancing and communication between cores.

opencl vs cuda
Concurrency refers to multiple computations being run at the same time using one CPU, while parallelism is the the simultaneous running of computations requiring more than one processing unit.

Memory Utilization

Concurrent programs tend to use more memory due to the operating system needing to keep track of each process or thread running simultaneously, leading to higher overhead in terms of RAM usage and potentially restricting how many concurrent processes or threads can run on one machine.

On the contrary, parallelism can improve memory efficiency by assigning each task or thread its own processing unit or core. This reduces context switching and better utilizes available memory resources depending on which form of parallelism is employed and how those resources are allocated.

Programming Paradigms

Concurrency and parallelism differ in terms of the programming paradigms employed. Concurrency often implies asynchronous programming, in which multiple tasks or threads can run independently and communicate with one another via message passing or shared memory. This flexibility and responsiveness allows for more flexible applications but may lead to complex code that’s difficult to debug.

On the contrary, parallel programming often evokes images of parallelism, where multiple tasks or threads collaborate on a single problem or task. While this requires more coordination and synchronization between them, it can result in more efficient and scalable programs. Parallel programming often relies on specialized libraries or frameworks which offer tools for managing parallel tasks or threads.


Concurrency and parallelism differ in terms of granularity. Granularity refers to the size and complexity of executed tasks or threads, with each performing a small unit before yielding to another task or thread. While this provides for fine-tuned control over program execution, it can also lead to higher overhead due to frequent context switching, which reduces performance overall.

Parallelism, on the other hand, often involves larger and more intricate tasks or threads designed to collaborate on a common problem or task. While this can result in better utilization of processing resources and higher performance, it may also prove more challenging to manage and debug. The level of granularity needed depends on both program requirements as well as available hardware resources.

Concurrency vs. Parallelism: 7 Must-Know Facts

  • Concurrency refers to the management and execution of multiple computations at once, while parallelism refers to running multiple computations simultaneously.
  • Concurrency is achieved by interleaving processes on a central processing unit (CPU) or context switching; parallelism relies on multiple CPUs.
  • Debugging concurrency is a particularly challenging problem, while parallelism presents similar difficulties but is simpler to resolve.
  • Concurrency can be achieved using just one processing unit, while parallelism requires multiple processors.
  • Concurrency allows more work to be finished at once, while parallelism boosts the throughput and computational speed of the system.
  • Parallelism executes numerous tasks simultaneously, while concurrency involves handling multiple tasks at once.
  • Concurrency is a non-deterministic control flow approach, while parallelism takes on a deterministic nature.

Concurrency vs. Parallelism: Which One is Better?

For years, computer scientists have been debating the merits of concurrency and parallelism. Both techniques have advantages and disadvantages; ultimately, which best suits your application depends on specific needs.

Concurrency is the ability to manage multiple computations simultaneously, often through interleaving operations on one processor. Though this increases throughput, debugging in this non-deterministic control flow approach can prove challenging.

Parallelism, on the other hand, involves running multiple calculations simultaneously through multiple central processing units. It improves system throughput and computational speed through its deterministic control flow approach. However, this requires hardware with multiple processing units, so debugging may prove challenging.

Concurrency and parallelism are distinct concepts. Concurrency refers to dealing with many things simultaneously while parallelism entails doing multiple things at once. Applications can be either concurrent or parallel, depending on their requirements.

When selecting which technique to utilize, it is essential to consider the application’s individual requirements, including task size, hardware availability, and required degree of determinism. Both approaches can significantly enhance performance for a given application; however, understanding their distinctions and drawbacks helps you make an informed decision.

Frequently Asked Questions

What are some real-world applications of concurrency and parallelism in computing?

Concurrency can be seen in many scenarios, such as when a web browser loads multiple tabs simultaneously, or multiple users access the same file on a shared network drive. Parallelism is frequently employed for computationally intensive tasks like image processing, video rendering, or scientific simulations, where the workload can be divided into smaller sub-tasks and processed simultaneously across multiple CPU cores.

Which is better, concurrency or parallelism?

The answer to this question depends on the task at hand and available resources. Concurrency can be achieved with just one CPU, making it ideal for situations with many I/O-bound tasks that benefit from interleaving, such as web servers. Parallelism requires multiple CPUs and is more effective when dealing with computationally intensive tasks that can be divided into smaller sub-tasks.

Can concurrency and parallelism be used together?

Yes, concurrency and parallelism can be combined in a system with multiple CPUs. With such an arrangement, multiple tasks can be executed concurrently on each CPU and then work in parallel to complete larger projects. This approach improves throughput and responsiveness of the system overall.

What are some of the challenges associated with programming for concurrency and parallelism?

Concurrency and parallelism present new difficulties in software development, such as race conditions, deadlocks, and synchronization problems. Debugging these systems may prove tricky due to their nondeterministic behavior which depends on timing and order of execution. Furthermore, designing algorithms and data structures that take advantage of multiple CPUs requires careful consideration and requires expertise.

How does concurrency impact performance?

Concurrency can improve a system’s efficiency by enabling multiple tasks to run simultaneously and taking advantage of idle time. Unfortunately, it also introduces overhead due to context switching and synchronization, which may slow down the system if not managed effectively. The performance benefits of concurrency depend on both the workload and available resources.

How does parallelism affect performance?

Parallelism can significantly boost performance for tasks that can be divided into smaller sub-tasks and executed on multiple CPUs simultaneously. Unfortunately, parallelism also introduces overhead due to interprocessor communication, synchronization, and load balancing, which limits system scalability. The performance advantages of parallelism depending on the number of CPUs used and the workload being processed.

Can concurrency and parallelism be applied in distributed systems?

Yes. Distributed systems that have multiple nodes and processors can benefit from concurrency and parallel processing by breaking tasks up into smaller sub-tasks that can be distributed to different nodes for processing in parallel. Alternatively, tasks may be interleaved between nodes for easier concurrency. However, distributed systems present new challenges, such as communication overhead, network latency, and fault tolerance.

How can concurrency and parallelism be measured?

Concurrency and parallelism can be measured using various metrics, such as throughput, response time, and scalability. Throughput measures the number of tasks completed per unit time while response time measures the time between the submission of a task until its completion; scalability indicates the system’s capacity for handling increasing workloads with additional resources. These indicators allow the evaluation of different concurrency/parallelism approaches while identifying bottlenecks or performance issues.

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