Part 2: 15 More Advanced Techniques to Optimize C# Applications

After implementing the first optimizations, you may notice significant improvements in your application’s performance. However, it’s essential to explore additional advanced techniques to reach an even higher level of efficiency. In this second part, I will present 15 more techniques focusing on efficient array manipulation, asynchronous operation optimization, and customization of collections and data structures. These practices are vital to further refining your application’s performance.

1. Custom Struct Layouts

Use the StructLayout attribute to control the layout of data in structs, improving memory efficiency and cache locality.

Example: In interoperation with native code, explicitly defining the layout of a struct’s fields can reduce marshaling overhead.

Suggestion: Use StructLayout only when necessary, as changes in layout can introduce complexity and affect interoperability with other parts of the code.

2. ValueTask

Replace Task with ValueTask in asynchronous methods when the result is frequently completed synchronously, avoiding the allocation of a new Task.

Example: In an API that frequently returns instant results from a cache, ValueTask can avoid unnecessary Task allocation.

Suggestion: Use ValueTask carefully, as it can introduce complexity in managing the task lifecycle, especially in scenarios involving multiple awaits.

3. Pre-JIT

Consider precompiling critical parts of the code using NGen or Crossgen to reduce JIT overhead during application execution.

Example: In a desktop application that needs to start quickly, using Pre-JIT can significantly improve startup time.

Suggestion: Assess the impact of Pre-JIT in scenarios where final binary size may be a concern, as this can increase the application’s size.

4. Manual Looping

In critical scenarios, replace LINQ queries with manual loops to eliminate LINQ iteration overhead, especially in very large collections.

Example: Instead of using .Where().Select() it on a large collection, manually implement the filtering and projection logic in a single loop to maximize performance.

Suggestion: Use manual loops sparingly, as they can make the code more error-prone and less readable, especially if the loop is complex.

5. Custom Collection Types

Instead of using standard .NET collections, consider implementing custom collections for specific cases where memory usage or access performance is critical.

Example: If your application requires a list of items that are rarely modified after creation but frequently read, a custom collection optimized for read access may be more efficient.

Suggestion: Test custom collections rigorously to ensure that there are no performance regressions compared to standard collections.

6. Weak References

Use WeakEventManager to prevent event management from causing memory leaks by holding references to objects that should be garbage collected.

Example: In an application that uses events for notifications, WeakEventManager can be used to automatically register and unregister event handlers, preventing memory leaks.

Suggestion: Understand the impact of using weak references, as they can introduce unexpected behavior if not managed correctly.

7. Expression Trees

Instead of using MethodInfo.Invoke, consider using expression trees (Expression<TDelegate>) to compile and dynamically invoke methods with performance close to direct calls.

Example: When building a rule engine that applies various dynamic business rules, use Expression Trees to compile and execute these rules efficiently.

Suggestion: Expression Trees are powerful but complex. Use them when the performance gain justifies the additional complexity.

8. Source Generators

Use source generators to create code at compile-time, avoiding reflection or heavy computations at runtime.

Example: A source generator can be used to generate interface implementations based on attributes, eliminating the need for runtime reflection.

Suggestion: Always validate the generated code to ensure it meets performance requirements and does not introduce maintenance issues.

9. Optimized Reflection

When reflection is inevitable, cache the results of reflective operations (such as obtaining methods, properties, etc.) to avoid costly repetitions.

Example: In an ORM that frequently uses reflection to map object properties to database columns, caching PropertyInfo can significantly reduce overhead.

Suggestion: Reflection is a powerful tool, but it should be used cautiously. Prefer other approaches when possible.

10. Lock-Free Programming

Whenever possible, use lock-free algorithms such as Interlocked for atomic operations or ConcurrentDictionary for thread-safe collections.

Example: Use Interlocked.CompareExchange to implement thread-safe access counters without the need for locks, improving performance in highly concurrent systems.

Suggestion: Lock-free code is challenging to implement correctly. Ensure you fully understand the implications before opting for this approach.

11. ReaderWriterLockSlim

In scenarios where there are many more reads than writes, use ReaderWriterLockSlim to allow concurrent access by multiple reading threads.

Example: In a caching system where data is read much more frequently than it is updated, ReaderWriterLockSlim can significantly improve performance.

Suggestion: Monitor the performance of ReaderWriterLockSlim in high-load scenarios to ensure it is providing the expected benefits.

12. Utf8JsonReader/Utf8JsonWriter

Use Utf8JsonReader and Utf8JsonWriter for high-performance JSON manipulation, avoiding unnecessary string allocations and other serialization/deserialization overheads.

Example: In an API that handles large volumes of JSON data, using Utf8JsonReader can significantly reduce the overhead of serialization and deserialization.

Suggestion: This approach requires a shift in how code handles JSON, so evaluate whether the performance improvement justifies the additional complexity.

13. Hybrid Data Structures

Combine different types of collections to optimize both data access and manipulation.

Example: Use a Dictionary for quick lookups and a List to maintain the order of elements, creating a hybrid structure that offers the best of both worlds.

Suggestion: Combining different data structures can introduce additional complexity. Use this approach in scenarios where performance advantages outweigh the complexity.

14. SIMD-Optimized Libraries

Use libraries that leverage SIMD (Single Instruction, Multiple Data) and other hardware-specific instructions for mathematical and scientific operations.

Example: In an image processing application, using SIMD-optimized libraries can speed up operations such as filter convolution.

Suggestion: SIMD optimizations require the code to run on compatible hardware. Ensure that the infrastructure where your application will run supports these optimizations.

15. Temporal Data Structures

Use data structures optimized for accessing temporal (time-based) data efficiently, such as linked lists with time intervals.

Example: In a network monitoring system, a temporal data structure can be used to store and quickly access performance metrics collected over time.

Suggestion: Evaluate whether a temporal data structure is truly necessary before implementing it, as the added complexity may not be justified in all scenarios.

Having explored these additional 15 advanced techniques, you’ve gained insights into fine-tuning specific areas of your C# applications, such as custom struct layouts, efficient array handling, and lock-free programming. These optimizations help you to push the boundaries of performance, especially in scenarios with high concurrency or large datasets. As you implement these techniques, remember to balance performance gains with code maintainability. In the final part, we’ll look at even more powerful strategies to supercharge your code, making it ready for the most demanding environments.