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docs: update README architecture, build guidance, TLSF benchmark, and examples

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Signed-off-by: Nitload <YoumuLiu_NSI@whv0t.onmicrosoft.com>
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Nitload
2026-08-09 16:54:53 +08:00
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ReadMe.md
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@@ -2,11 +2,11 @@
![StgSharp Logo](STG%23LOGO.png)
StgSharp is a modern C# framework that combines game engine capabilities with high-performance computing features. Originally designed as a next-generation STG (Shoot 'em Up) game engine, it has evolved to include advanced mathematical libraries, parallel computing capabilities, and comprehensive graphics APIs.
StgSharp is an experimental .NET framework for graphics, numerical computing, language analysis, and other performance-sensitive applications. It began as the foundation of a next-generation STG (shoot 'em up) engine, but the current repository focuses on reusable low-level infrastructure rather than a complete, ready-to-use game engine.
## Overview
StgSharp provides both low-level and high-level bindings to OpenGL, multimedia control, and device management, offering everything needed to create STG games and high-performance applications. The framework is built on .NET 8 and supports Windows and Linux platforms.
The framework combines managed C# APIs with a C native library. Its current work includes OpenGL bindings and rendering infrastructure, generic matrix and vector types, SIMD-accelerated native kernels, custom allocators and collections, regular-language analysis, and state-oriented UI infrastructure. StgSharp targets .NET 8; platform and architecture coverage varies by module, with Windows and Linux as the principal native targets.
## Core Modules
@@ -14,12 +14,10 @@ StgSharp provides both low-level and high-level bindings to OpenGL, multimedia c
The foundational library containing shared functionality:
- **Mathematics**: High-performance linear algebra, matrix operations, and mathematical utilities
- **Graphics**: OpenGL bindings, rendering pipeline, and graphics framework
- **High Performance**: SIMD operations, memory management, and parallel computing
- **High Performance**: Native-memory allocators and processor abstractions
- **Collections**: Specialized data structures and containers
- **MVVM**: Model-View-ViewModel framework for UI applications
- **Entities**: Game entity system and behavior management
- **Threading and Timing**: Synchronization primitives, task helpers, and time providers
- **Pipeline and State**: Processing pipelines and reusable state-machine primitives
- **Environment**: Platform abstraction and system integration
### StgSharp.Mathematics (Numeric)
@@ -32,28 +30,31 @@ Numerics and linear algebra built on top of the core library:
### StgSharp.RegularAnalysis
Regular-language and parsing infrastructure (replaces the former Script module):
Next-generation regular analysis (NGRA) infrastructure for recognizing and processing regular languages:
- **Regex/Automata Core**: Regular expression processing and automata utilities
- **Parsing Primitives**: Reusable tokenizer and grammar building blocks
- **Analysis Pipeline**: Structured processing for language-like inputs
NGRA is a foundation for higher-level language tooling. It does not replace `StgSharp.Script`; the planned Script implementation depends on NGRA and has not started yet. The source currently under `StgSharp.Script` is retained from the earlier EXPRESS implementation and is not part of the active core solution.
### StgSharp.Graphics
Managed graphics infrastructure built on Common and Mathematics:
- **OpenGL Bindings**: Broad OpenGL 3.3-4.6 API bindings and context management
- **Rendering Infrastructure**: Render streams, viewports, textures, framebuffers, and geometry
- **Shader Support**: Shader compilation, program management, and shader-generation utilities
- **Window Integration**: GLFW-based window and input access
### StgSharp.Native
Native library integration:
- **GLFW Integration**: Cross-platform window and input management
- **GLFW Integration**: Native loading and interoperation used by the graphics layer
- **SIMD Operations**: Hardware-accelerated vector operations
- **Matrix Kernels**: Optimized matrix computation routines
- **Context Management**: Graphics context and resource management
### StgSharp.Device
Low-level device and port abstractions:
- **Command Interfaces**: Command shapes and serialization contracts
- **Port Control**: Stream-oriented device access
- **Decoder/Response Models**: Protocol-level data handling
- **Platform Runtime**: Native thread and platform support for Windows and Linux
### StgSharp.TerminalDialogue
@@ -63,7 +64,7 @@ Terminal-based user interface application and components:
- **Dialogue System**: User interaction and input handling
- **Terminal Graphics**: Text-based UI rendering
> Note: The legacy Script module has been superseded by RegularAnalysis. The Model module is under refactoring and is currently out of the core framework.
> Note: `StgSharp.Script` is planned as a consumer of NGRA and has not started yet. Its existing EXPRESS sources belong to the earlier implementation. `StgSharp.Model` is currently an empty placeholder while its design is being reconsidered.
## Key Features
@@ -71,51 +72,49 @@ Terminal-based user interface application and components:
- **Parallel Matrix Operations**: Multi-threaded matrix computations with optimized scheduling
- **SIMD Acceleration**: Hardware-optimized vector operations
- **Memory Management**: Custom allocators including TLSF (Two Layer Segregated Fit)
- **Thread Pool Management**: Efficient task scheduling and execution
- **Memory Management**: Arena, slab, and TLSF (Two-Layer Segregated Fit) allocators
- **Parallel Runtime**: Dedicated scheduling and worker infrastructure for matrix operations
#### HLSF Benchmark Snapshot (BenchmarkDotNet)
#### TLSF Benchmark Snapshot (BenchmarkDotNet)
```
BenchmarkDotNet v0.15.8, Windows 10 (10.0.19045.7548)
Proxmox VE guest, 30 vCPUs, host CPU reported as Intel Xeon Gold 6242R 3.10GHz
.NET SDK 10.0.301
[Host] : .NET 8.0.28 (8.0.28, 8.0.2826.26413), X64 RyuJIT x86-64-v4
BenchmarkDotNet v0.14.0, Windows 11 (10.0.26100.7623)
AMD Ryzen 7 6800H with Radeon Graphics, 1 CPU, 16 logical and 8 physical cores
.NET SDK 10.0.102
[Host] : .NET 8.0.23 (8.0.2325.60607), X64 RyuJIT AVX2
Toolchain=InProcessEmitToolchain InvocationCount=2048 UnrollFactor=1
Toolchain=InProcessEmitToolchain InvocationCount=3 IterationCount=15
LaunchCount=1 UnrollFactor=1 WarmupCount=6
```
| Method | Mean | Error | StdDev | Ratio | RatioSD |
|------- |-----:|-----:|------:|------:|-------:|
| Hlsf | 76.23 µs | 0.355 µs | 0.315 µs | 0.44 | 0.01 |
| Libc | 172.98 µs | 3.042 µs | 2.988 µs | 1.00 | 0.02 |
|------- |-----:|------:|-------:|------:|--------:|
| Libc | 129.96 ns | 10.123 ns | 9.469 ns | 1.00 | 0.10 |
| Tlsf | 81.39 ns | 1.039 ns | 0.971 ns | 0.63 | 0.05 |
This benchmark measures steady-state small-block allocation throughput (64B–4KB),
which represents the dominant allocation pattern in StgSharp's matrix computation pipeline.
HLSF uses a pre-allocated arena with size-class free lists, avoiding kernel-mode allocation calls.
Under this workload, HLSF achieves ~2.3× throughput over system malloc. This is not a general-purpose allocator comparison.
This benchmark was run inside a Proxmox VE virtual machine configured with 30 vCPUs. BenchmarkDotNet reports those virtual CPUs as 30 logical and 30 physical processors; that is the guest topology rather than the physical topology of the Xeon Gold 6242R host CPU, whose nominal topology is 20 cores and 40 threads.
The benchmark reports normalized time per allocation/free operation. Each iteration uses a 512 MB arena and 256 batches of 2,048 allocations, drawing request sizes from 64, 100, 200, 400, 1,024, and 4,096 bytes and freeing them in a deterministic shuffled order. TLSF uses a local coalescing window of four blocks. In this run TLSF used about 63% of the libc baseline time, or approximately 1.60x the throughput. The result describes this fixed small-block workload only; it is not a general-purpose allocator comparison.
### Graphics and Rendering
- **OpenGL Integration**: Complete OpenGL API coverage
- **Cross-Platform Support**: Windows and Linux compatibility
- **OpenGL Integration**: Broad OpenGL 3.3-4.6 bindings
- **Cross-Platform Direction**: Windows and Linux native targets, with feature coverage dependent on platform
- **Rendering Pipeline**: Flexible and extensible graphics framework
- **Shader Management**: Dynamic shader compilation and management
### Game Engine Capabilities
### Framework Infrastructure
- **Entity System**: Flexible game object management
- **State Machine**: Game state and behavior management
- **Input Handling**: Comprehensive input and control systems
- **Resource Management**: Efficient asset loading and management
- **State Machines**: Reusable state and transition primitives
- **Processing Pipelines**: Schedulable nodes and typed connections
- **Window and Input Access**: Low-level GLFW integration
- **Image and Texture Handling**: Image loading and GPU texture infrastructure
### Data Processing
- **STEP File Support**: Industrial data format processing
- **EXPRESS Language**: Domain-specific language compilation
- **Pipeline Processing**: Efficient data transformation workflows
- **NGRA**: Regex analysis, intermediate representation, optimization, and source generation
- **EXPRESS (legacy/inactive)**: The repository retains an earlier experimental EXPRESS parser/compiler under `StgSharp.Script`
- **Script (planned)**: A future language layer intended to build on NGRA; development has not started
## Requirements
@@ -130,8 +129,9 @@ StgSharp is currently in development and requires compilation from source. **NuG
### Prerequisites
- **Windows**: Visual Studio 2022 or later with C++ development tools
- **Linux**: CMake and compatible C++ compiler (planned support)
- CMake 3.16 or later
- Clang and a Ninja-compatible build environment for the supported C targets
- **Windows (optional)**: Visual Studio may open the provided `.vcxproj` for navigation and editing
- .NET 8.0 SDK or later
- Git for source code retrieval
@@ -144,19 +144,23 @@ StgSharp is currently in development and requires compilation from source. **NuG
cd StgSharp
```
2. **Windows (Recommended)**:
- Open `StgSharp.sln` in Visual Studio
- Build the solution (Debug or Release configuration)
- The native components will be automatically compiled
3. **Linux (Experimental)**:
2. **Build the native library (recommended toolchain)**:
```bash
# Build .NET components only
# Note: Native components are not yet supported on Linux
dotnet build
cmake --preset clang-release -S src/StgSharp.Native
cmake --build cmake_build/clang-release
```
The supplied presets use Clang with Ninja and place native output under the repository's platform-specific `bin` directory. Use `clang-debug` for a debug build.
3. **Build the managed projects**:
```bash
dotnet build StgSharp.sln
```
Managed builds copy an existing native binary into their output directory. They do not invoke CMake automatically, so build `StgSharp.Native` first when native functionality is required.
### Build Configuration
- **Debug**: Full debugging symbols, unoptimized
@@ -164,10 +168,10 @@ StgSharp is currently in development and requires compilation from source. **NuG
### Notes
- Windows compilation with Visual Studio is fully supported and tested
- Linux support is experimental and limited to .NET components only
- Native components (StgSharp.Native) are currently Windows-only and require MSVC
- CMake support for native components is not yet implemented
- CMake is the maintained build path for `StgSharp.Native`; the checked-in presets primarily target Clang
- The Visual Studio C project may be opened with MSVC tooling, but MSVC IntelliSense behavior and MSVC-produced binaries are not maintained or guaranteed
- Windows and Linux platform implementations exist; actual feature and architecture coverage may differ
- x86-64 currently has the most complete optimized kernel coverage
- Some features may not be available on all platforms
## Quick Start
@@ -183,27 +187,36 @@ StgSharp is currently in development and requires compilation from source. **NuG
### Matrix Operations
```csharp
using StgSharp.Mathematics;
using StgSharp.HighPerformance.Memory;
using StgSharp.Mathematics.Numeric;
// Create matrices
var matrixA = Matrix<float>.Create(4, 4);
var matrixB = Matrix<float>.Create(4, 4);
// Matrix storage is allocated explicitly from a native-memory arena.
using var allocator = new TwoLayerSegregatedFitAllocator(64 * 1024 * 1024);
// Perform parallel matrix multiplication
var result = matrixA * matrixB;
var matrix = Matrix<float>.Create(
4,
4,
MatrixLayout.DenseRectangle,
allocator);
matrix[0, 0] = 1.0f;
```
General matrix arithmetic does not currently provide operator overloads. Because matrix storage and result ownership involve explicit native-memory control, arithmetic operations are exposed through explicit computation APIs as they are implemented.
### Memory Management
```csharp
using StgSharp.HighPerformance.Memory;
// Create high-performance allocator
using var allocator = new HybridLayerSegregatedFitAllocator(64 * 1024 * 1024);
// Create a 64 MiB TLSF arena
using var allocator = new TwoLayerSegregatedFitAllocator(64 * 1024 * 1024);
// Allocate memory
var handle = allocator.Alloc(1024);
// Use allocated memory
Span<byte> buffer = handle.AsSpan();
buffer[0] = 42;
allocator.Free(handle);
```
@@ -212,8 +225,9 @@ allocator.Free(handle);
### Technical Documentation
- [TLSF Allocator](StgSharp.Common/HighPerformance/Memory/InroductionToHLSF.md) - High-performance memory allocator documentation
- [Native Library Naming](StgSharp.Native/naming.md) - Native library file naming conventions
- [TLSF Allocator](src/StgSharp.Common/HighPerformance/Memory/InroductionToTLSF.md) - Arena layout, allocation behavior, and benchmark methodology
- [Native Library Build](src/StgSharp.Native/README.md) - Native build and source-layout notes
- [Native Library Naming](src/StgSharp.Native/naming.md) - Native library file naming conventions
## Contributing
@@ -237,4 +251,4 @@ StgSharp is licensed under the MIT License. See [LICENSE](LICENSE) for details.
## Support
For questions, issues, or contributions, please refer to the project repository or contact the development team.
For questions, issues, or contributions, please refer to the project repository or contact the development team.