---
title: "Acceleration - Hardware-Aware Optimization"
description: "Optimize ML operations with SIMD, cache-friendly algorithms, and parallel computing"
difficulty: 3
time_estimate: "6-8 hours"
prerequisites: ["Profiling", "Compression"]
next_steps: ["Benchmarking"]
learning_objectives:
  - "Implement cache-friendly algorithms for matrix operations"
  - "Apply SIMD vectorization for parallel data processing"
  - "Design multi-core parallelization strategies for batch operations"
  - "Understand hardware bottlenecks (compute vs memory bandwidth)"
  - "Optimize ML kernels based on profiling data from Module 14"
---

# 18. Acceleration

**⚡ OPTIMIZATION TIER** | Difficulty: ⭐⭐⭐ (3/4) | Time: 6-8 hours

## Overview

Optimize ML operations through hardware-aware programming. This module implements cache-friendly algorithms, SIMD vectorization, and multi-core parallelization to achieve significant speedups based on profiling insights from Module 14.

## Learning Objectives

By completing this module, you will be able to:

1. **Implement cache-friendly algorithms** for matrix multiplication and convolution using blocked algorithms
2. **Apply SIMD vectorization** to parallelize element-wise operations across data
3. **Design multi-core parallelization strategies** for batch processing and data parallelism
4. **Understand hardware bottlenecks** (compute-bound vs memory-bound operations)
5. **Optimize ML kernels** based on actual profiling data, achieving measurable speedups

## Why This Matters

### Production Context

Hardware optimization is critical for production ML:

- **PyTorch** uses custom CUDA kernels and CPU vectorization; 100× faster than naive Python
- **TensorFlow XLA** compiles models to optimized machine code; reduces latency by 2-5×
- **ONNX Runtime** applies hardware-specific optimizations; powers Microsoft/Azure ML serving
- **Apple Neural Engine** uses custom accelerators; enables on-device ML on iPhones

### Historical Context

Hardware optimization evolved with ML scale:

- **Pre-Deep Learning (pre-2010)**: Hand-written assembly for critical loops; library implementations
- **GPU Era (2010-2017)**: CUDA kernels dominate; cuDNN becomes standard; 10-100× speedups
- **Specialized Hardware (2018+)**: TPUs, custom ASICs; compiler-based optimization
- **Modern Systems (2020+)**: ML compilers (TVM, XLA); automated kernel generation and tuning

Understanding hardware optimization separates production engineers from researchers.

## Pedagogical Pattern: Build → Use → Optimize

### 1. Build

Implement from first principles:
- Blocked matrix multiplication for cache efficiency
- SIMD-vectorized element-wise operations
- Multi-threaded batch processing
- Memory-aligned data structures
- Profiling integration

### 2. Use

Apply to real problems:
- Optimize bottlenecks identified in Module 15
- Accelerate attention computation
- Speed up convolutional operations
- Parallelize data loading pipelines
- Measure actual speedups

### 3. Optimize

Production techniques:
- Auto-tuning for different hardware
- Mixed-precision computation (FP16/FP32)
- Operator fusion to reduce memory traffic
- Batch processing for amortized overhead
- Hardware-specific code paths

## Implementation Guide

### Core Patterns

**Cache-Friendly Matrix Multiplication**
- Block matrices into cache-sized tiles
- Reuse data while in cache (temporal locality)
- Access memory sequentially (spatial locality)
- Typical speedup: 2-5× over naive implementation

**SIMD Vectorization**
- Process multiple data elements simultaneously
- Use Numba/Cython for automatic vectorization
- Align data to vector boundaries (16/32/64 bytes)
- Typical speedup: 2-8× for element-wise ops

**Multi-Core Parallelization**
- Divide work across CPU cores
- Use thread pools for batch processing
- Minimize synchronization overhead
- Typical speedup: 0.5-0.8× number of cores (due to overhead)

## Testing

```bash
cd modules/source/16_acceleration
python acceleration_dev.py
tito export 16_acceleration
tito test 16_acceleration
```

## Where This Code Lives

```
tinytorch/
├── acceleration/
│   └── kernels.py              # Optimized implementations
└── __init__.py
```

## Systems Thinking Questions

1. **Roofline Model**: Your operation needs 1000 FLOPs and 100 bytes. At 100 GFLOPs/s compute and 10 GB/s bandwidth, what's the bottleneck?

2. **Amdahl's Law Applied**: You parallelize 90% of code perfectly across 8 cores. What's max speedup? Why not 8×?

3. **Cache Hierarchy**: L1 cache is 10× faster than L2, which is 10× faster than RAM. How does blocking matrix multiplication exploit this?

## Real-World Connections

**PyTorch/TensorFlow**: Custom CUDA kernels for all operations
**ONNX Runtime**: Hardware-specific optimization for production serving
**Apple ML**: Metal shaders and Neural Engine for on-device inference

## What's Next?

In **Module 17: Quantization**, you'll reduce precision for even more speedups:
- INT8 quantization for 4× memory reduction
- Mixed-precision training and inference
- Calibration and accuracy preservation

---

**Ready to optimize for hardware?** Open `modules/source/16_acceleration/acceleration_dev.py` and start implementing.
