GPU Computing

Real GPU vs CPU performance comparison using WebGPU and optimized Zig implementations. Understanding when and how to leverage parallel computing for performance-critical applications.

GPU Capability Detection

Detecting GPU capabilities...

Real Performance Benchmarks

Comparing our optimized CPU implementations (Zig) with WebGPU shaders (WGSL) on real algorithms. Click each benchmark to see actual performance measurements.

🧮 Matrix Multiplication

A classic GPU-friendly problem with massive parallelism potential. Each output element can be computed independently, making it ideal for GPU acceleration with thousands of parallel threads.

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🖼️ Image Convolution

Real-world GPU application for image processing. Each pixel's new value depends only on its neighborhood, creating perfect 2D parallelism for GPU computation.

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🔢 Fibonacci Sequence

Demonstrates when CPU optimization wins. While each Fibonacci calculation is independent, the GPU overhead and memory transfer costs make this a CPU-favorable algorithm.

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Algorithm Categories

🚀 GPU Winners

Classic parallel algorithms that excel on GPU

  • • Matrix Multiplication
  • • Image Convolution
  • • Array Reduction
  • • Radix Sort

⚡ CPU Winners

Sequential algorithms that favor CPU

  • • Recursive Algorithms
  • • Tree Traversal
  • • Quicksort
  • • Small Datasets

🎯 Edge Cases

Depends on implementation approach

  • • Graph Algorithms
  • • String Matching
  • • Sparse Operations
  • • Dynamic Programming

Performance Analysis

Why Some Algorithms Favor CPU

  • • Long dependency chains - Each step depends on previous results
  • • Irregular memory access - Poor cache locality
  • • Branch divergence - Different execution paths per thread
  • • Transfer overhead - Small datasets don't benefit from GPU
  • • Low latency requirements - CPU branch prediction excels

Why Some Algorithms Favor GPU

  • • Massive parallelism - Thousands of independent threads
  • • Regular memory access - Coalesced memory patterns
  • • High arithmetic intensity - Many operations per memory access
  • • Large datasets - Transfer overhead amortized
  • • SIMD efficiency - Vectorized operations

Technical Implementation

CPU Implementation (Zig)

  • • Language: Zig (modern, low-level, zero-cost abstractions)
  • • Optimizations: SIMD, cache-friendly patterns, algorithm-specific tuning
  • • Memory Management: Arena allocator for efficient allocation
  • • Benchmarking: High-precision timing with detailed metrics
  • • Portability: Easy deployment to Fly.io servers

GPU Implementation (WebGPU)

  • • Shader Language: WGSL (WebGPU Shading Language)
  • • Workgroup Sizes: Optimized for different algorithms
  • • Memory Management: Efficient buffer usage patterns
  • • Error Handling: Graceful fallbacks for unsupported features
  • • Browser Support: Progressive enhancement approach