Changelog
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
[Unreleased]
Added
- Stream Manager for multi-stream concurrent execution
- Batched GEMM operations with stream parallelism
- INT8 quantization support with per-channel quantization
- Quantization calibrator for dynamic quantization
- Comprehensive documentation suite
- Quick Start Guide
- Architecture documentation
- GEMM Optimization detailed guide
- Performance Tuning guide
- API Reference
- Contributing guide
- Configuration file examples (default, high_performance, debug)
[1.0.0] - 2024-12-31
Added
- Complete GEMM optimization implementation
- Level 1: Naive MatMul
- Level 2: Tiled GEMM with shared memory
- Level 3: Memory coalescing optimization
- Level 4: Double buffering for latency hiding
- Level 5: Register blocking with vectorized loads
- Level 6: Kernel fusion (MatMul + Bias + ReLU)
- Level 7: Vectorized GEMM with float4 loads
- Half precision (FP16) GEMM with mixed precision accumulation
- Auto-tuner for automatic kernel selection
- Performance profiler with roofline analysis
- N-dimensional Tensor class with GPU storage
- GPU memory pool with caching
- Thread-safe logging system
- Configuration management system
- InferenceEngine for neural network inference
- MNIST demo application
- Comprehensive benchmark suite
- Test suite with Google Test
Infrastructure
- CMake build system with CUDA support
- Optional test building (BUILD_TESTS flag)
- Support for CUDA architectures 75, 80, 86, 89, 90
[0.1.0] - 2024-12-30
Added
- Initial project structure
- Basic GEMM kernels (Naive, Tiled)
- Core data structures (MatrixDesc, GemmConfig)
- CUDA error handling utilities
- DeviceMemory RAII wrapper
- Basic benchmark framework
Version History Summary
| Version | Date | Highlights |
|---|---|---|
| 1.0.0 | 2024-12-31 | Complete implementation with all optimizations |
| 0.1.0 | 2024-12-30 | Initial release with basic functionality |
Migration Guide
From 0.1.0 to 1.0.0
No breaking changes. New features are additive.
API Changes
None.
Deprecated Features
None.
Known Issues
- Performance on small matrices (< 256) may be suboptimal
- FP16 GEMM requires GPU with Compute Capability 7.0+
- Memory pool may hold onto memory longer than necessary
Roadmap
Planned for 1.1.0
- Tensor Core support (WMMA API)
- Multi-GPU support
- ONNX model loading
Planned for 1.2.0
- INT4 quantization
- Sparse matrix support
- Custom activation functions
Long-term
- Transformer layer support
- Convolution operations
- Graph optimization