name: "acceleration"
title: "Hardware Acceleration and Kernel Optimization"
description: "Learn hardware acceleration principles through cache-friendly algorithms, vectorization, and backend systems"
learning_objectives:
  - "Understand CPU cache hierarchy and memory access performance bottlenecks"
  - "Implement cache-friendly blocked matrix multiplication algorithms"  
  - "Build vectorized operations with optimized memory access patterns"
  - "Design transparent backend systems for automatic optimization selection"
  - "Measure and quantify real performance improvements scientifically"
  - "Apply systems thinking to optimization decisions in ML workflows"
prerequisites:
  - "Module 2: Tensor operations and NumPy fundamentals"
  - "Module 4: Linear layers and matrix multiplication"
  - "Understanding of basic algorithmic complexity (O notation)"
estimated_time: "3-4 hours"
difficulty: "Advanced"
tags:
  - "performance"
  - "optimization" 
  - "systems"
  - "hardware"
  - "acceleration"
  - "cache"
  - "vectorization"
  - "backends"
exports:
  - "blocked_matmul"
  - "vectorized_add"
  - "optimized_relu"
  - "ComputeBackend"
  - "OptimizedBackend"
  - "AccelerationCompetition"
assessment:
  - "Implement blocked matrix multiplication with measurable speedups"
  - "Build vectorized operations avoiding Python loops"
  - "Create backend system for transparent optimization"
  - "Design competition framework for kernel comparisons"
  - "Analyze optimization principles and real-world applications"