[Perf] Linux/x64: 1 Improvement on 9/11/2026 9:25:03 AM +00:00

### Run Information

Name | Value
-- | --
Architecture | x64
OS | ubuntu 22.04
Queue | ViperUbuntu
Baseline | [f8a811758057986566da27d06d8f03a7e5e226bf](https://github.com/dotnet/sdk/commit/f8a811758057986566da27d06d8f03a7e5e226bf)
Compare | [c9b72963eb22ce3f8eb2ad8ebedf9ecef57615da](https://github.com/dotnet/sdk/commit/c9b72963eb22ce3f8eb2ad8ebedf9ecef57615da)
Diff | [Diff](https://github.com/dotnet/sdk/compare/f8a811758057986566da27d06d8f03a7e5e226bf...c9b72963eb22ce3f8eb2ad8ebedf9ecef57615da)
Configs | CompilationMode:tiered, RunKind:imagesharp

### Improvements in SixLabors.ImageSharp.Benchmarks.Codecs.DecodeTga

Benchmark | Baseline | Test | Test/Base | Test Quality | Edge Detector | Baseline IR | Compare IR | IR Ratio
-- | -- | -- | -- | -- | -- | -- | -- | --
|

• [TgaImageSharp - Duration of single invocation]()
• 📝 - [Benchmark Source]()
• [ADX - Test Multi Config Graph]()

![graph]()
[Test Report]()

### Repro
General Docs link: https://github.com/dotnet/performance/blob/main/docs/benchmarking-workflow-dotnet-runtime.md

```cmd
git clone https://github.com/dotnet/performance.git
python3 .\performance\scripts\benchmarks_ci.py -f net8.0 --filter 'SixLabors.ImageSharp.Benchmarks.Codecs.DecodeTga*'
```

### SixLabors.ImageSharp.Benchmarks.Codecs.DecodeTga.TgaImageSharp(TestImage: "Tga/targa_24bit.tga")

#### ETL Files

#### Histogram

#### JIT Disasms

### Docs

[Profiling workflow for dotnet/runtime repository](https://github.com/dotnet/performance/blob/master/docs/profiling-workflow-dotnet-runtime.md)
[Benchmarking workflow for dotnet/runtime repository](https://github.com/dotnet/performance/blob/master/docs/benchmarking-workflow-dotnet-runtime.md)

Start with src/benchmarks/real-world/ImageSharp/Codecs/DecodeTga.cs#L26-L31 and reproduce the SixLabors.ImageSharp.Benchmarks.Codecs.DecodeTga filter using the provided benchmarks_ci.py command. Compare the benchmark and test reports for the supplied baseline and test commits. Done means the reported improvement is understood and validated against the benchmark data.

Written by the indexing model from the issue text.