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Arpit Sharma
Arpit Sharma

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Async vs Parallelism: Why Your Code Isn’t Faster (Yet)

#architecture #programming #computerscience #performance

A common response to performance issues is to “make the code async.” Blocking calls are replaced with non-blocking ones, async/await is introduced, and the expectation is that the application will run faster. But after the refactor, CPU usage remains the same, response times barely improve, and the code is now more complex. This happens because async programming does not increase CPU throughput or enable work to run in parallel across cores. Instead, it improves efficiency for I/O-bound workloads by allowing a thread to make progress while waiting. Parallelism addresses a different class of problems entirely by executing CPU-bound work concurrently.
This article explains how async and parallelism differ, when each one helps, and how to choose the right approach for real-world systems.

Async: Handling I/O Efficiently

Async programming in C# allows threads to perform other work while waiting for I/O operations, such as web requests or file access, to complete.

async Task FetchDataAsync()
{
    using HttpClient client = new();
    string result = await client.GetStringAsync("https://example.com");
    Console.WriteLine(result.Length);
}
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  • await releases the thread while waiting for the HTTP response.
  • Ideal for I/O-bound operations.

Parallelism: Using CPU Cores

Parallelism allows CPU-bound tasks to run simultaneously across multiple cores. Async alone cannot speed up CPU-heavy operations.

Parallel.For(0, 4, i =>
{
    double sum = 0;
    for (int j = 0; j < 1_000_000; j++)
        sum += Math.Sqrt(j);
});
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  • Executes heavy computation concurrently.
  • Each iteration can run on a separate core.

When to Use What!

Async and parallelism serve different types of workloads, so choosing the right approach depends on the task. For web requests, API calls, or file I/O, async is the best choice because it frees threads while waiting for operations to complete. Heavy CPU-bound computations, like math simulations or image processing, benefit from parallelism since it utilizes multiple cores simultaneously. In workloads that mix both I/O and CPU-intensive tasks, combining the two approaches—using async for I/O and parallelism for computation—yields the most efficient results.

Common Pitfalls

  • Using async for CPU-bound tasks → no speedup.
  • Blocking on async (.Result or .Wait()) → deadlocks.
  • Parallelism for I/O-bound tasks → wastes threads.
  • Mixing async and parallelism without understanding workload -> subtle bugs.

Conclusion

Async and parallelism solve different performance problems. Async keeps I/O-bound applications responsive, while parallelism maximizes CPU-bound work. Choosing the right approach ensures your code is both efficient and maintainable.

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