Building an LLVM-Based Deep Learning Optimizer from Scratch

When it comes to deep learning performance, most engineers reach for established frameworks like PyTorch or TensorFlow. But what happens underneath these abstractions? How do operations like convolution or ReLU get translated into efficient machine code?

This post explores my journey building the LLVM Deep Learning Optimizer, a project that generates optimized LLVM Intermediate Representation (IR) for common deep learning primitives. Along the way, we’ll discover the optimization techniques that make modern neural networks fast.

The Problem: Bridging High-Level Operations and Hardware

Consider a simple ReLU activation applied to a tensor of 1 million elements:

output = max(0, input)  # Applied element-wise

A naive implementation processes one element at a time. But modern CPUs have SIMD (Single Instruction, Multiple Data) registers that can handle 8 floats simultaneously. That’s an easy 8x speedup sitting on the table—if your code knows how to use it.

Enter LLVM

LLVM is a compiler infrastructure that provides an intermediate representation (IR) sitting between high-level code and machine instructions. By generating optimized LLVM IR, we can:

1. Target multiple architectures - The same IR compiles to x86, ARM, or custom hardware
2. Apply advanced optimizations - LLVM’s optimization passes are battle-tested
3. Maintain precision - We control exactly which floating-point operations occur

Key Optimization Techniques

1. SIMD Vectorization

Instead of processing one element at a time, vectorized code processes multiple elements in parallel:

// Scalar: 1024 iterations
for (int i = 0; i < 1024; i++)
    output[i] = max(0, input[i]);

// Vectorized (8-wide): 128 iterations
for (int i = 0; i < 1024; i += 8)
    output[i:i+8] = vmax(vzero, input[i:i+8]);

The optimizer automatically emits vector load/store instructions and uses SIMD comparison operations, yielding 4-8x throughput improvements for activation functions.

2. Fused Multiply-Add (FMA)

Convolution and batch normalization perform many multiply-add sequences. Modern CPUs have dedicated FMA instructions that:

• Execute multiply and add in a single cycle
• Provide better numerical precision (reduced rounding errors)
• Double the effective FLOP throughput

The optimizer recognizes patterns like a * b + c and emits FMA instructions directly.

3. Loop Fusion

Consider batch normalization, which involves multiple passes over data:

1. Compute mean
2. Compute variance
3. Normalize

Naive implementations make three separate passes, flushing CPU caches between each. Loop fusion combines these operations, keeping data in cache and reducing memory bandwidth pressure by 2-3x.

Architecture Overview

┌────────────────────────────────────────────────────────────┐
│                   LLVM DL Optimizer                        │
├────────────────────────────────────────────────────────────┤
│  Kernel Layer                                              │
│  ┌──────────┐ ┌────────────┐ ┌─────────┐ ┌─────────┐       │
│  │Activation│ │Convolution │ │ Pooling │ │ Softmax │       │
│  │ 8 types  │ │ NCHW + FMA │ │Max/Avg  │ │ 3-pass  │       │
│  └──────────┘ └────────────┘ └─────────┘ └─────────┘       │
├────────────────────────────────────────────────────────────┤
│  Optimization Layer                                        │
│  ┌────────────┐ ┌────────────┐ ┌────────────────┐          │
│  │Auto-Vector │ │ Loop Fusion│ │ Data Layout    │          │
│  │  ization   │ │    Pass    │ │ Transform      │          │
│  └────────────┘ └────────────┘ └────────────────┘          │
├────────────────────────────────────────────────────────────┤
│                LLVM 18-21 Infrastructure                   │
│        (Opaque Pointers, FixedVectorType, FMA/Exp)         │
└────────────────────────────────────────────────────────────┘

Benchmarks

Testing on an Intel Core i9 with AVX-512 support:

When to Use This

This project is ideal for:

• Learning - Understanding how compilers optimize DL operations
• Research - Experimenting with novel optimization techniques
• Custom hardware - Generating kernels for specialized accelerators
• Edge deployment - When you need maximum CPU efficiency

It’s not meant to replace PyTorch or TensorFlow for production training.

Getting Started

git clone https://github.com/muditbhargava66/llvm-dl-optimizer.git
cd llvm-dl-optimizer
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j$(nproc)

# Generate optimized IR and run demo
./llvm-dl-optimizer --demo -v -o output.ll

Conclusion

Building a deep learning optimizer from scratch reveals the layers of optimization happening beneath our abstractions. From SIMD vectorization to FMA fusion to cache-conscious code generation, there’s an entire world of engineering that transforms simple mathematical operations into blazingly fast implementations.

The full source code, documentation, and benchmarks are available at github.com/muditbhargava66/llvm-dl-optimizer.

Have questions or ideas for additional optimizations? Feel free to open an issue or contribute!