Performance Optimization

Overview

Performance is critical for emulation. This guide covers profiling tools, optimization techniques, and performance analysis for Ryujinx development.

Measure first, optimize second. Always profile before optimizing to avoid premature optimization.

Build for Performance

Release Configuration

# Build optimized release version
dotnet build -c Release

# Publish optimized for specific platform
dotnet publish -c Release -r win-x64

Optimization Flags

From src/ARMeilleure/ARMeilleure.csproj:

<PropertyGroup>
  <AllowUnsafeBlocks>True</AllowUnsafeBlocks>
  <Optimize>True</Optimize>
</PropertyGroup>

JIT Optimizations

Ryujinx uses ARMeilleure (ARM JIT compiler) for CPU emulation:

// From src/ARMeilleure/Optimizations.cs
public static class Optimizations
{
    public static bool AllowLcqInFunctionTable { get; set; } = true;
    public static bool UseUnmanagedDispatchLoop { get; set; } = true;
}

These optimizations are disabled during testing for faster test execution (from src/Ryujinx.Tests/Cpu/CpuTest.cs:65-66).

Profiling Tools

Built-in .NET Profilers

dotnet-counters
dotnet-trace
dotnet-dump

Real-time performance metrics:

# Install
dotnet tool install -g dotnet-counters

# Monitor running process
dotnet-counters monitor --process-id <PID>

# Monitor specific counters
dotnet-counters monitor --process-id <PID> \
  System.Runtime[cpu-usage,working-set,gc-heap-size]

Collect performance traces:

# Install
dotnet tool install -g dotnet-trace

# Collect trace
dotnet-trace collect --process-id <PID> \
  --providers Microsoft-DotNETCore-SampleProfiler

# Analyze with PerfView or Visual Studio

Analyze memory dumps:

# Install
dotnet tool install -g dotnet-dump

# Capture dump
dotnet-dump collect --process-id <PID>

# Analyze dump
dotnet-dump analyze dump.dmp

Visual Studio Profiler

Start profiling session

Debug → Performance Profiler or Alt+F2

Select profiling tools

CPU Usage: Find hot paths
.NET Object Allocation: Memory allocations
Instrumentation: Detailed timing
GPU Usage: Graphics performance

Start profiling

Click Start to launch with profiling

Analyze results

Review flame graphs, call trees, and hot paths

JetBrains dotTrace

Profile application

Run → Profile in Rider

Choose profiling mode

Sampling: Low overhead, statistical
Tracing: Accurate, higher overhead
Line-by-line: Most detailed

Analyze timeline

View CPU usage over time and identify spikes

Inspect call tree

Find methods consuming most CPU time

PerfView (Free, Windows)

# Download from https://github.com/microsoft/perfview

# Collect trace
PerfView.exe collect

# Analyze trace
PerfView.exe <trace-file>.etl

Benchmarking

BenchmarkDotNet

The gold standard for .NET micro-benchmarking:

Install package

<PackageReference Include="BenchmarkDotNet" Version="0.13.12" />

Create benchmark class

using BenchmarkDotNet.Attributes;
using BenchmarkDotNet.Running;

[MemoryDiagnoser]
public class ShaderCacheBenchmarks
{
    private ShaderCache _cache;
    
    [GlobalSetup]
    public void Setup()
    {
        _cache = new ShaderCache();
    }
    
    [Benchmark]
    public void GetShaderProgram()
    {
        _cache.GetProgram(0x1234);
    }
    
    [Benchmark]
    public void CompileShader()
    {
        _cache.CompileShader(shaderCode);
    }
}

Run benchmarks

class Program
{
    static void Main(string[] args)
    {
        BenchmarkRunner.Run<ShaderCacheBenchmarks>();
    }
}

Benchmark Attributes

[Benchmark] - Mark method to benchmark

[Benchmark]
public void MyMethod() { }

[GlobalSetup] - Run once before all benchmarks

[GlobalSetup]
public void Setup() { }

[IterationSetup] - Run before each iteration

[IterationSetup]
public void IterationSetup() { }

[MemoryDiagnoser] - Track allocations

[MemoryDiagnoser]
public class MyBenchmarks { }

[Params] - Test multiple values

[Params(100, 1000, 10000)]
public int Size { get; set; }

Simple Performance Measurement

using System.Diagnostics;

// Quick measurement
var sw = Stopwatch.StartNew();
DoWork();
sw.Stop();
Logger.Info?.Print(LogClass.Application, 
    $"Operation took {sw.ElapsedMilliseconds}ms");

// High-resolution timing
long start = Stopwatch.GetTimestamp();
DoWork();
long end = Stopwatch.GetTimestamp();
double elapsedMs = (end - start) * 1000.0 / Stopwatch.Frequency;

Optimization Techniques

Memory Allocation Optimization

Use Span<T>
Object Pooling
Reduce Boxing
Avoid Allocations in Loops

// Bad: allocates array
byte[] buffer = new byte[1024];
ProcessData(buffer);

// Good: stack allocation
Span<byte> buffer = stackalloc byte[1024];
ProcessData(buffer);

using System.Buffers;

// Rent from pool instead of allocating
byte[] buffer = ArrayPool<byte>.Shared.Rent(1024);
try
{
    ProcessData(buffer);
}
finally
{
    ArrayPool<byte>.Shared.Return(buffer);
}

// Bad: boxes value type
object obj = 42;

// Good: use generics
T Value<T>(T value) => value;

// Bad: allocates every iteration
for (int i = 0; i < 1000; i++)
{
    var temp = new StringBuilder();
    temp.Append(i);
}

// Good: allocate once
var temp = new StringBuilder();
for (int i = 0; i < 1000; i++)
{
    temp.Clear();
    temp.Append(i);
}

CPU Optimization

Inline Methods
SIMD Operations
Unsafe Code
Branch Prediction

using System.Runtime.CompilerServices;

[MethodImpl(MethodImplOptions.AggressiveInlining)]
private int FastMethod(int x)
{
    return x * 2;
}

using System.Runtime.Intrinsics;
using System.Runtime.Intrinsics.X86;

if (Avx2.IsSupported)
{
    // Use AVX2 SIMD operations
    Vector256<int> vec = Avx2.LoadVector256(ptr);
}

unsafe
{
    fixed (byte* ptr = &buffer[0])
    {
        // Fast pointer operations
        *ptr = 42;
    }
}

// Help branch predictor
if (likely_condition) // Most common path
{
    FastPath();
}
else
{
    SlowPath();
}

Data Structure Optimization

// Use appropriate collections

// Fast lookup: O(1)
var dict = new Dictionary<int, string>();

// Fast iteration
var list = new List<int>();

// Concurrent access
var concurrent = new ConcurrentDictionary<int, string>();

// Memory-efficient
var span = new Span<byte>(buffer);

GPU Performance

Shader Compilation

From src/Ryujinx.Graphics.Gpu/Shader/ShaderCache.cs:

// Cache compiled shaders to avoid recompilation
private readonly Dictionary<ulong, CachedShaderProgram> _programCache;

public ShaderProgram GetProgram(ulong address)
{
    if (_programCache.TryGetValue(address, out var cached))
    {
        return cached.Program; // Fast path
    }
    
    // Compile and cache
    var program = CompileShader(address);
    _programCache[address] = new CachedShaderProgram(program);
    return program;
}

Texture Caching

Reuse texture resources
Compress textures when possible
Use appropriate texture formats
Implement mipmap generation efficiently

Memory Performance

Memory Profiling

Take memory snapshot

Visual Studio: Debug → Memory Usage → Take Snapshot

Perform operation

Execute the code you want to analyze

Take second snapshot

Compare snapshots to see allocations

Analyze differences

Identify objects that weren’t garbage collected

Common Memory Issues

Memory Leaks

Symptom: Memory usage grows over timeCauses:

Event handlers not unsubscribed
Static collections holding references
IDisposable not called

Fix:

// Unsubscribe events
obj.Event -= Handler;

// Use weak references
var weakRef = new WeakReference<T>(obj);

// Dispose properly
using var resource = new Resource();

Excessive Allocations

Symptom: High GC pressure, frequent Gen0 collectionsFix:

Use object pooling
Use Span<T> and stackalloc
Reuse buffers

Large Object Heap Fragmentation

Symptom: Memory usage higher than expectedFix:

Avoid allocating >85KB objects
Use array pooling
Use GC.TryStartNoGCRegion() for critical sections

Concurrency and Threading

Parallel Processing

using System.Threading.Tasks;

// Parallel loops
Parallel.For(0, count, i =>
{
    ProcessItem(i);
});

// Parallel LINQ
var results = items.AsParallel()
    .Where(x => x.IsValid)
    .Select(x => Transform(x))
    .ToList();

// Task-based parallelism
var tasks = new Task[10];
for (int i = 0; i < 10; i++)
{
    int index = i;
    tasks[i] = Task.Run(() => ProcessItem(index));
}
await Task.WhenAll(tasks);

Lock-Free Programming

using System.Threading;

// Interlocked operations
Interlocked.Increment(ref counter);
Interlocked.CompareExchange(ref value, newValue, comparand);

// Concurrent collections
var queue = new ConcurrentQueue<T>();
var dict = new ConcurrentDictionary<K, V>();

Performance Monitoring

Built-in Performance Counters

using System.Diagnostics;

// CPU usage
var cpuCounter = new PerformanceCounter(
    "Processor", "% Processor Time", "_Total");
float cpuUsage = cpuCounter.NextValue();

// Memory usage
long memoryUsage = GC.GetTotalMemory(false);

Custom Metrics

public class PerformanceMetrics
{
    private long _frameCount;
    private Stopwatch _fpsTimer = Stopwatch.StartNew();
    
    public void RecordFrame()
    {
        Interlocked.Increment(ref _frameCount);
        
        if (_fpsTimer.ElapsedMilliseconds >= 1000)
        {
            long fps = _frameCount;
            Logger.Info?.Print(LogClass.Application, $"FPS: {fps}");
            
            Interlocked.Exchange(ref _frameCount, 0);
            _fpsTimer.Restart();
        }
    }
}

Performance Testing

Load Testing

[Test]
public void LoadTest()
{
    const int operations = 1000000;
    var sw = Stopwatch.StartNew();
    
    for (int i = 0; i < operations; i++)
    {
        DoOperation();
    }
    
    sw.Stop();
    double opsPerSecond = operations / sw.Elapsed.TotalSeconds;
    
    TestContext.WriteLine($"Operations/sec: {opsPerSecond:N0}");
    Assert.That(opsPerSecond, Is.GreaterThan(100000));
}

Optimization Checklist

Before optimizing, verify:

Profiled to identify actual bottlenecks
Measured baseline performance
Focused on hot paths (80/20 rule)
Tested in Release configuration
Considered algorithmic improvements first
Avoided premature optimization
Benchmarked changes before/after
Tested on target hardware

Common Bottlenecks in Emulation

CPU Emulation

JIT compilation overhead
Instruction decoding
Register state management
Memory access translation

GPU Emulation

Shader compilation/translation
Texture uploads/downloads
Draw call overhead
GPU synchronization

Memory Management

Page table lookups
Memory mapping/unmapping
Cache invalidation
GC pressure from allocations

I/O Operations

File system access
Save state serialization
Shader cache persistence
Log file writes

Performance Tips

Profile on target hardware - Performance characteristics vary significantly between systems

Optimize algorithms first - A better algorithm beats micro-optimizations

Cache expensive operations - Especially JIT compilation and shader translation

Use async/await correctly - Don’t block threads unnecessarily

Monitor GC metrics - Excessive GC pauses hurt emulation smoothness

Resources

Next Steps

Testing

Benchmark your optimizations

Debugging

Profile and debug issues

Contributing

Submit performance improvements

Contributing

Development

​Overview

​Build for Performance

​Release Configuration

​Optimization Flags

​JIT Optimizations

​Profiling Tools

​Built-in .NET Profilers

​Visual Studio Profiler

​JetBrains dotTrace

​PerfView (Free, Windows)

​Benchmarking

​BenchmarkDotNet

​Benchmark Attributes

​Simple Performance Measurement

​Optimization Techniques

​Memory Allocation Optimization

​CPU Optimization

​Data Structure Optimization

​GPU Performance

​Shader Compilation

​Texture Caching

​Memory Performance

​Memory Profiling

​Common Memory Issues

​Concurrency and Threading

​Parallel Processing

​Lock-Free Programming

​Performance Monitoring

​Built-in Performance Counters

​Custom Metrics

​Performance Testing

​Load Testing

​Optimization Checklist

​Common Bottlenecks in Emulation

CPU Emulation

GPU Emulation

Memory Management

I/O Operations

​Performance Tips

​Resources

​Next Steps

Testing

Debugging

Contributing

Build docs developers (and LLMs) love

Overview

Build for Performance

Release Configuration

Optimization Flags

JIT Optimizations

Profiling Tools

Built-in .NET Profilers

Visual Studio Profiler

JetBrains dotTrace

PerfView (Free, Windows)

Benchmarking

BenchmarkDotNet

Benchmark Attributes

Simple Performance Measurement

Optimization Techniques

Memory Allocation Optimization

CPU Optimization

Data Structure Optimization

GPU Performance

Shader Compilation

Texture Caching

Memory Performance

Memory Profiling

Common Memory Issues

Concurrency and Threading

Parallel Processing

Lock-Free Programming

Performance Monitoring

Built-in Performance Counters

Custom Metrics

Performance Testing

Load Testing

Optimization Checklist

Common Bottlenecks in Emulation

Performance Tips

Resources

Next Steps