CUDA GEMM Optimization Tutorial
Learn GPU programming through progressive optimization
This tutorial guides you through 7 levels of CUDA matrix multiplication (GEMM) optimization, from a naive implementation to achieving ~90% of cuBLAS performance.
What You’ll Learn
| Level | Technique | Performance | Key Concept |
|---|---|---|---|
| 1 | Naive | ~10% | Baseline GPU execution |
| 2 | Tiled | ~20% | Shared memory utilization |
| 3 | Coalesced | ~30% | Memory access patterns |
| 4 | Double Buffer | ~40% | Latency hiding |
| 5 | Register Blocked | ~70% | Register-level optimization |
| 6 | Fused | ~80% | Operator fusion |
| 7 | Vectorized | ~89% | Vector memory operations |
Quick Start
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# Clone the repository
git clone https://github.com/LessUp/mini-inference-engine.git
cd mini-inference-engine
# Build the project
cmake --preset release
cmake --build --preset release
# Run benchmarks
./build-release/benchmark
Documentation
Project Overview
This is a learning project designed for:
- Beginners wanting to understand CUDA programming basics
- Intermediate developers looking to optimize GPU kernels
- Students studying high-performance computing
Each optimization level includes:
- Detailed explanation of the technique
- Code implementation with comments
- Performance comparison against baseline
- Common pitfalls and solutions
Performance Results
Tested on RTX 3080 with 1024×1024 matrices:
| Kernel | Time (ms) | GFLOPS | Efficiency |
|---|---|---|---|
| cuBLAS | 0.31 | 6920 | 100% |
| Naive | 3.10 | 694 | 10% |
| Tiled | 1.55 | 1388 | 20% |
| Coalesced | 1.03 | 2082 | 30% |
| Double Buffer | 0.78 | 2768 | 40% |
| Register Blocked | 0.44 | 4870 | 70% |
| Fused | 0.38 | 5630 | 81% |
| Vectorized | 0.35 | 6130 | 89% |