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 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 main library containing core 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
- Collections: Specialized data structures and containers
- MVVM: Model-View-ViewModel framework for UI applications
- Entities: Game entity system and behavior management
- Environment: Platform abstraction and system integration
General-purpose syntax analysis framework:
- Syntax Analysis Framework: Provides a flexible framework for parsing and analyzing various programming languages
- EXPRESS Language Support: Specialized support for ISO 10303-11 EXPRESS language syntax parsing
- Abstract Syntax Trees: Language parsing and analysis infrastructure
- Token Processing: Lexical analysis and token management
- Grammar Parsing: Extensible grammar parsing and validation system
Geometric model loading and interpretation:
- Geometric Model Loading: Responsible for loading and interpreting geometric models from various formats
- STEP Format Support: Limited support for ISO 10303 STEP file format processing
- Entity Management: Model entity parsing and geometric data interpretation
- Data Pipeline: Efficient geometric data processing and transformation
- Schema Validation: Model structure validation and verification
Native library integration:
- GLFW Integration: Cross-platform window and input management
- SIMD Operations: Hardware-accelerated vector operations
- Matrix Kernels: Optimized matrix computation routines
- Context Management: Graphics context and resource management
Terminal-based user interface:
- Interactive Terminal: Command-line interface components
- Dialogue System: User interaction and input handling
- Terminal Graphics: Text-based UI rendering
- Parallel Matrix Operations: Multi-threaded matrix computations with optimized scheduling
- SIMD Acceleration: Hardware-optimized vector operations
- Memory Management: Custom allocators including HLSF (Hybrid Layer Segregated Fit)
- Thread Pool Management: Efficient task scheduling and execution
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
| 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 |
This run shows HLSF significantly faster than libc under the current configuration. See the HLSF document for details and caveats.
- OpenGL Integration: Complete OpenGL API coverage
- Cross-Platform Support: Windows and Linux compatibility
- Rendering Pipeline: Flexible and extensible graphics framework
- Shader Management: Dynamic shader compilation and management
- 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
- STEP File Support: Industrial data format processing
- EXPRESS Language: Domain-specific language compilation
- Pipeline Processing: Efficient data transformation workflows
- .NET 8.0 or later
- Windows 10/11 or Linux
- x64 or ARM64 architecture
- OpenGL 3.3+ support
StgSharp is currently in development and requires compilation from source. NuGet packages are not yet available - you must build the project from source code.
- Windows: Visual Studio 2022 or later with C++ development tools
- Linux: CMake and compatible C++ compiler (planned support)
- .NET 8.0 SDK or later
- Git for source code retrieval
-
Clone the repository:
git clone https://github.com/Nitload-NSI/StgSharp.git cd StgSharp -
Windows (Recommended):
- Open
StgSharp.slnin Visual Studio - Build the solution (Debug or Release configuration)
- The native components will be automatically compiled
- Open
-
Linux (Experimental):
# Build .NET components only # Note: Native components are not yet supported on Linux dotnet build
- Debug: Full debugging symbols, unoptimized
- Release: Optimized for performance
- 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
- Some features may not be available on all platforms
/*To be implemented*/using StgSharp.Mathematics;
// Create matrices
var matrixA = Matrix<float>.Create(4, 4);
var matrixB = Matrix<float>.Create(4, 4);
// Perform parallel matrix multiplication
var result = matrixA * matrixB;using StgSharp.HighPerformance.Memory;
// Create high-performance allocator
using var allocator = new HybridLayerSegregatedFitAllocator(64 * 1024 * 1024);
// Allocate memory
var handle = allocator.Alloc(1024);
// Use allocated memory
allocator.Free(handle);- HLSF Allocator - High-performance memory allocator documentation
- Native Library Naming - Native library file naming conventions
StgSharp is developed by Nitload Space. Contributions are welcome, particularly in:
- Performance optimizations
- Cross-platform compatibility
- Documentation improvements
- New feature implementations
StgSharp is licensed under the MIT License. See LICENSE for details.
- Vulkan Support: Planned for version 2.0
- Enhanced Cross-Platform: Improved Linux and mobile support
- Performance Optimizations: Further SIMD and parallel processing improvements
- Extended Language Support: Additional domain-specific languages
For questions, issues, or contributions, please refer to the project repository or contact the development team.