Boost .NET Performance with SIMD (`System.Numerics.Vector`)

If you work with numerical-heavy workloads in .NET — like financial calculations, trading engines, or scientific simulations — performance matters. One of the most powerful tools available is SIMD (Single Instruction Multiple Data) via System.Numerics.Vector.

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What is SIMD?

SIMD allows a single CPU instruction to operate on multiple data points at once.

Traditional code:

float sum = 0;
for (int i = 0; i < data.Length; i++)
    sum += data[i];

With SIMD, multiple values are processed in parallel, significantly speeding up numeric operations.

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Benefits of Using SIMD

• Performance Boost: Loops can run 3–5x faster.
• Reduced Memory Pressure: Fewer allocations, lower GC overhead.
• Low Latency: Perfect for real-time trading engines, market feeds, or signal processing.
• Hardware Acceleration: Leverages AVX, SSE, or ARM Neon instructions automatically.

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How to Use SIMD in .NET

using System;
using System.Numerics;

class Program
{
    static void Main()
    {
        float[] data = new float[100_000_000];
        for (int i = 0; i < data.Length; i++)
            data[i] = 1.0f;

        Console.WriteLine($"Sum: {SimdSum(data)}");
    }

    static float SimdSum(float[] data)
    {
        int vectorSize = Vector<float>.Count;
        var simdSum = new Vector<float>(0f);

        int i = 0;
        for (; i <= data.Length - vectorSize; i += vectorSize)
            simdSum += new Vector<float>(data, i);

        float total = 0f;
        for (int j = 0; j < vectorSize; j++)
            total += simdSum[j];

        // Remaining elements
        for (; i < data.Length; i++)
            total += data[i];

        return total;
    }
}

Tips:

• Vector<float>.Count automatically adapts to CPU vector width.
• Use with large arrays or heavy loops for best performance.
• Always handle remaining elements outside the vector loop.

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When to Use SIMD

Ideal for:

• Trading indicators & financial calculations
• Image or signal processing
• CPU-based ML inference
• Scientific simulations
• Real-time analytics pipelines

Pro Tip: Combine SIMD with Span<T> to avoid extra allocations.

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Conclusion

Using SIMD via System.Numerics.Vector in .NET provides:

• Parallel processing of multiple values per CPU instruction
• Reduced memory allocations & GC pressure
• Higher throughput & lower latency

It’s a must-know optimization for any .NET developer working on high-performance or real-time systems.

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References

• System.Numerics.Vector - Microsoft Docs
• Introducing Hardware Intrinsics in .NET

I’m Morteza Jangjoo and “Explaining things I wish someone had explained to me”