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World Congress 2026 Europe - Virtual Stage • Jul 2, 2026 • Session details

From Loops to Lightning: .NET with SIMD

Laszlo Deak

Why settle for slow sequential loops when you can process data in parallel? Discover how to leverage SIMD vectorization in .NET to triple your execution speed.

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#1 about 2 min

Identifying the problem of character search in text files

Finding placeholders in a large text file requires efficient character searching algorithms.

#2 about 4 min

Comparing built-in string methods against manual loop iterations

Benchmarking the native string searching method reveals significant performance advantages over manual iteration.

#3 about 3 min

Understanding SIMD architecture through a grocery shopping analogy

Processing multiple data items concurrently eliminates the overhead of sequential task execution.

#4 about 2 min

Hardware instruction sets and vector registers in modern processors

Advanced vector extensions allow central processing units to perform mathematical operations on larger data chunks.

#5 about 2 min

Utilizing hardware intrinsics and vector abstractions in .NET

The framework provides both low-level architecture checks and high-level cross-platform vector types.

#6 about 2 min

Implementing an array sorting check with standard sequential loops

Iterating sequentially through an array provides a baseline approach for validating sorted element order.

#7 about 2 min

Designing a vectorized approach for evaluating sorted integer arrays

Loading array segments into registers allows pairwise comparisons to quickly identify out-of-order elements.

#8 about 3 min

Writing C# implementations using unsafe loads and vector comparisons

Utilizing vector comparison operations isolates unsorted elements across multiple memory addresses simultaneously.

#9 about 4 min

Managing loop bounds and array remainders in SIMD operations

Adjusting iteration limits prevents memory access violations while evaluating trailing elements outside the vector length.

#10 about 2 min

Benchmarking vectorized sorting logic against scalar baseline implementations

Executing formal performance tests demonstrates the execution time reduction achieved by applying vector extensions.

#11 about 3 min

Evaluating performance tradeoffs and code complexity of vectorized operations

The benefits of faster execution times must be weighed against increased implementation difficulty and register loading costs.

Matching moments

3:31 min

Evaluating WebAssembly use cases and SIMD performance benchmarking

Paweł Aniszewski Paweł Aniszewski · Europe 2026 Virtual

4:30 min

Implementing high-speed vector operations for independent data hashing

Szymon Kulec Szymon Kulec · WWC Europe 2026

2:33 min

Introduction to speeding up code with vector operations

Jodie Burchell · LIVE

3:49 min

Leveraging WebAssembly SIMD and 64-bit memory architectures

Chris Heilmann +3 · LIVE

3:19 min

Performance differences between for and foreach loops

Paweł Łukaszuk · LIVE

36 sec

Optimizing math calculations with the vector API

Ron Veen Ron Veen · WWC 2025

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