When should I use Parallel instead of async/await?

Use Parallel or PLINQ for CPU-bound tasks like complex calculations or data processing. Use async/await for I/O-bound operations like database calls, file access, or network requests.

Can I limit the number of CPU cores used?

Yes, the skill implements ParallelOptions and WithDegreeOfParallelism to control the MaxDegreeOfParallelism, preventing resource exhaustion in shared environments.

Does it support ordered results in parallel queries?

Yes, it includes patterns for using .AsOrdered() in PLINQ to ensure that results are returned in the same sequence as the source collection while still processing in parallel.

How does this skill prevent race conditions?

The skill provides patterns for using ConcurrentCollections (like ConcurrentBag and ConcurrentDictionary) and locking mechanisms to ensure data integrity during simultaneous writes.

.NET Parallel Processing Expert

Name: .NET Parallel Processing Expert
Author: christian289

bychristian289

•

API Development

Implements high-performance parallel processing patterns in .NET using Parallel, PLINQ, and thread-safe collections.

About

This skill empowers Claude to architect and implement efficient parallel processing for CPU-bound tasks within .NET applications. It provides domain-specific guidance on using the Parallel class, PLINQ, and ConcurrentCollections to maximize multi-core utilization while maintaining thread safety. By distinguishing between CPU-bound and I/O-bound operations, it helps developers avoid common performance bottlenecks, manage shared state without race conditions, and implement advanced patterns like early termination and thread-local storage for optimized resource handling.

Key Features

Optimizes LINQ queries using PLINQ (AsParallel) and order preservation
Implements Parallel.For and Parallel.ForEach with configurable parallelism
Manages thread-local variables with ThreadLocal to reduce contention
Integrates thread-safe collections like ConcurrentDictionary and ConcurrentBag
6 GitHub stars
Provides best practices for distinguishing CPU-bound and I/O-bound tasks

Use Cases

Parallelizing complex calculations across multi-core processors
Refactoring legacy loops into thread-safe, high-performance parallel operations
Optimizing heavy data transformation and image processing pipelines

About

Key Features

Optimizes LINQ queries using PLINQ (AsParallel) and order preservation
Implements Parallel.For and Parallel.ForEach with configurable parallelism
Manages thread-local variables with ThreadLocal to reduce contention
Integrates thread-safe collections like ConcurrentDictionary and ConcurrentBag
6 GitHub stars
Provides best practices for distinguishing CPU-bound and I/O-bound tasks

Use Cases

Parallelizing complex calculations across multi-core processors
Refactoring legacy loops into thread-safe, high-performance parallel operations
Optimizing heavy data transformation and image processing pipelines