GPU Operator Fusion Decision Engine

High-performance C++/CUDA analytical engine and hardware benchmarking suite for evaluating and optimizing operator fusion layouts in deep learning compute graphs.

Combines an exact Dynamic Programming (DP) solver with a Quantum-Inspired Evolutionary Optimizer (QIEO) to predict optimal fusion boundary points by modeling register allocation, SM warp occupancy, cache residency, and HBM memory traffic.

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🌟 Key Results

Demonstrates 100% decision alignment between the C++ analytical model and physical silicon execution (Tesla T4 + RTX 3090 specs):

1. Case A: Fusion Rejected (Occupancy Cliff)

   • Workload: 6-Op Pipeline (MatMul → BiasAdd → ReLU → LayerNorm → MatMul → BiasAdd)
   • Decision: [0-0] -> [1-1] -> [2-2] -> [3-3] -> [4-4] -> [5-5] (NO FUSION)
   • Why: LayerNorm reduction adds +28 regs/thread, dropping warp occupancy below the 40% latency-hiding threshold. The analytical engine correctly rejects fusion.

2. Case B: Fusion Wins (2.08× Speedup)

   • Workload: 3-Op Memory-Bound Chain (ElemAdd → ReLU → BiasAdd)
   • Decision: [0-2] (FULL FUSION)
   • Analytical prediction: Eliminates 32 MB of intermediate HBM traffic.
   • Physical benchmark (Tesla T4):
     • Unfused (3 kernels): 0.270 ms — 3 HBM round-trips
     • Fused (1 kernel): 0.129 ms — intermediates stay in 32-bit registers
     • Speedup: 2.08× 🚀

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🏗️ Architecture

gpu-operator-fusion-engine/
├── CMakeLists.txt              # Build config (C++17, CUDA 17, -O3)
├── main.cpp                    # CLI driver for analytical simulation
├── include/
│   └── fusion_engine.h         # Public API & CalibrationSummary
├── src/
│   └── fusion_engine.cpp       # Cost model, DP & QIEO solvers
├── benchmarks/
│   └── benchmark_runner.cu     # CUDA hardware micro-benchmarks
└── scripts/
    └── profile.sh              # Build + execute + NCU/NSYS profiling

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⚡ Technical Design

• DAG Validation: Tensor continuity, rank compatibility, producer-consumer ordering enforced before solver execution.
• Op-Type-Aware Cost Model:
  • Elementwise epilogues: +2 to +4 regs/thread (trivial RF forwarding).
  • Heavy reductions (LayerNorm): +28 regs/thread (shared-memory accumulators + spill).
  • Coalescing penalties modeled per 128-byte cache line transaction bounds.
  • SM block limits computed across threads, registers, shared memory, and arch caps.
• Dual Solvers:
  • DP: Exact O(N²) optimal partition search via prefix subproblems.
  • QIEO: Population-based metaheuristic with Q-bit state vectors and annealed rotation gates.
• Mock CUDA Generator: Emits structural __global__ kernel shells for winning cluster schedules.
• Hardware Benchmark: Handwritten CUDA kernels timed with cudaEventRecord() — no host-side overhead contamination.

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📊 Output Samples

Analytical Engine (fusion_cli)

Case A (6-Op Pipeline):
  Plan layout:    [0-0] -> [1-1] -> [2-2] -> [3-3] -> [4-4] -> [5-5]
  Runtime:        0.37312 ms
  HBM saved:      0 bytes
  Verdict:        NO FUSION (occupancy cliff from LayerNorm reduction)

Case B (Elementwise Chain):
  Plan layout:    [0-2]
  Runtime:        0.03585 ms
  HBM saved:      33,554,432 bytes (32 MB)
  Verdict:        FULL FUSION (intermediates stay in registers)

Hardware Benchmark (benchmark_runner, Tesla T4)

Unfused (3 kernels):    0.269688 ms
Fused   (1 kernel):     0.129393 ms
Speedup:                2.08×
HBM eliminated:         ~32 MB
Verdict:                FUSION WINS

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🚀 Build & Run

Prerequisites

• CMake ≥ 3.24
• C++17 compiler (gcc 9+, clang 10+, MSVC 2019+)
• NVIDIA CUDA Toolkit (for hardware benchmarks)

Quick Start

git clone https://github.com/emceeashish/KernelFusion-Engine.git
cd KernelFusion-Engine

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
cmake --build . --config Release -j$(nproc)

./fusion_cli           # Analytical decision engine
./benchmark_runner     # CUDA hardware benchmark (requires GPU)

Automated Profiling

bash scripts/profile.sh