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

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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