C# Dev #24: Parallel Programming - Use All Your CPU Cores with Parallel.For, ForEach & PLINQ
This is it - the final video in the C# for Beginners series! We're talking parallel programming: how to actually use all those CPU cores sitting in your machine. Parallel.For, Parallel.ForEach, and PLINQ. Let's make them work for their keep.
What you'll learn:
• Parallel vs async - they solve different problems
• Async = I/O waits, Parallel = CPU work
• Parallel.For basics - transforming sequential loops
• Parallel.ForEach - the collection-friendly version
• ParallelOptions - controlling thread count
• CancellationToken support in parallel operations
• Thread safety is YOUR responsibility
• Why regular collections aren't thread-safe
• ConcurrentBag pattern for collecting results
• PLINQ - just add .AsParallel()
• WithDegreeOfParallelism, WithCancellation, AsOrdered
• When PLINQ helps (and when it doesn't)
• Real-world example: Image processing
• Real-world example: Monte Carlo simulation
• Local state pattern for high performance
• Decision framework: ForEach vs For vs PLINQ vs Async
Key insights:
• Async for I/O waits, Parallel for CPU work - don't confuse them
• If the CPU isn't busy, parallel won't help
• Each parallel iteration should be independent
• Use ConcurrentBag, not List, for thread-safe results
• PLINQ doesn't preserve order by default
• Small datasets + simple operations = parallel overhead exceeds benefit
• More cores = faster results (for CPU-bound work)
Common mistakes we'll avoid:
• Using parallel for database/API calls (use async!)
• Sharing mutable state between parallel iterations
• Adding to collections from multiple threads
• Using PLINQ on tiny datasets
• Forgetting thread safety entirely
Previous Video: Async Best Practices
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🎉 SERIES COMPLETE! Thanks for sticking with me through all 24 videos!
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0:00 Parallel Programming
0:17 What We're Covering
0:33 Parallel vs Async: Not the Same Thing
1:17 When to Use Parallel
1:33 Parallel.For: The Basics
2:01 Parallel.ForEach: Collections Made Parallel
2:20 Controlling Parallelism
2:42 The Thread Safety Problem
3:10 Thread-Safe Pattern: Local Results
3:30 PLINQ: Parallel LINQ
3:55 PLINQ in Action
4:26 PLINQ Options
4:48 When PLINQ Helps (and When It Doesn't)
5:13 Real-World Example: Image Processing
5:40 Real-World Example: Monte Carlo Simulation
6:07 Choosing Your Approach
6:38 Series Wrap-Up
7:15 Thanks for Watching