Parallel.ForEach or Task.Factory.StartNew: Which is Better for Parallel Processing?

Parallel.ForEach and Task.Factory.StartNew: A Comparative Analysis for Parallel Programming

When parallelizing operations on collections, Parallel.ForEach and Task.Factory.StartNew are frequent choices. Both utilize the thread pool, but their approaches differ significantly, impacting performance.

Parallel.ForEach: Optimized Batch Processing

Parallel.ForEach employs a Partitioner<T> to efficiently divide the collection into smaller, manageable chunks. This batching minimizes overhead by avoiding the creation of a task for each individual item.

Task.Factory.StartNew: One Task Per Item

Unlike Parallel.ForEach, Task.Factory.StartNew creates a separate task for every item in the collection. While seemingly simple, this leads to substantial overhead, especially with large datasets, resulting in slower execution times.

Performance Tuning with Parallel.ForEach

Parallel.ForEach offers superior control through customizable partitioners, enabling optimized performance for diverse scenarios.

Synchronous vs. Asynchronous Execution

A key runtime difference lies in their execution models: Parallel.ForEach executes synchronously, while Task.Factory.StartNew operates asynchronously. To combine the efficiency of Parallel.ForEach's partitioner with asynchronous behavior, use this pattern:

<code class="language-csharp">Task.Factory.StartNew( () => Parallel.ForEach<Item>(items, item => DoSomething(item)));</code>

This approach retains the benefits of batch processing while allowing asynchronous operation.

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