Week 20: SIMD and Parallel Computing (Advanced)

Overview

Week 20 explores advanced performance optimization techniques using Single Instruction Multiple Data (SIMD) and parallel computing. You’ll learn how to leverage SIMD instructions for data parallelism and develop sophisticated parallel algorithms to fully utilize modern multi-core processors.

Day 1-3: SIMD Programming

Topics

• SIMD fundamentals:
  • Vector processing concepts
  • CPU architectures and SIMD extensions
  • Data parallelism vs task parallelism
  • SIMD registers and operations
  • Limitations and considerations
• Using SIMD in Rust:
  • The std::simd module (unstable)
  • Portable SIMD with packed_simd
  • Platform-specific intrinsics
  • Cross-platform abstractions
  • Feature detection and runtime dispatch
• SIMD programming patterns:
  • Data layout optimization
  • Vectorizable algorithms
  • Loop vectorization
  • Gather/scatter operations
  • Masking and conditional execution
• Auto-vectorization:
  • Compiler optimizations
  • Assisting the compiler
  • Verifying vectorization
  • Common obstacles
  • LLVM vectorization passes

Resources

• Portable SIMD (packed_simd)
• SIMD for Mere Mortals
• Rust Performance Book: SIMD
• AVX Intrinsics Guide
• SIMD at Insomniac Games
• Crunching Numbers with AVX and AVX2

Use Cases

• Image and video processing
• Scientific computing
• Cryptography
• 3D graphics and physics
• Audio processing
• Machine learning
• Data compression
• Text processing

Day 4-7: Advanced Parallel Algorithms

Topics

• Thread pools and work stealing:
  • The Rayon library in depth
  • Work stealing scheduler internals
  • Fork-join parallelism
  • Task granularity optimization
  • Custom schedulers
• Lock-free data structures:
  • Atomic operations
  • Memory ordering
  • ABA problem
  • Lock-free queues, stacks, and hash maps
  • Wait-free algorithms
• Parallel iterators:
  • Rayon parallel iterators
  • Custom parallel iterators
  • Execution policies
  • Adaptive parallelism
  • Parallel reduction algorithms
• Parallel patterns and algorithms:
  • Map-reduce
  • Parallel scan and prefix sum
  • Divide and conquer
  • Pipeline parallelism
  • Parallel sorting algorithms
• Synchronization and coordination:
  • Barriers
  • Latches
  • Phasers
  • Thread coordination patterns
  • Avoiding false sharing

Resources

• Rayon Documentation
• Rust Atomics and Locks by Mara Bos
• Crossbeam Documentation
• Lock-Free Programming
• Papers We Love: Parallelism
• The Art of Multiprocessor Programming
• Parallel Algorithms

Use Cases

• Big data processing
• Scientific simulations
• Parallel compilation
• Search algorithms
• Database operations
• Graph algorithms
• Real-time data processing
• Web servers and services

Exercises

1. Implement a vectorized image processing algorithm using SIMD
2. Create a portable SIMD implementation of a mathematical function
3. Build a parallel sorting algorithm with Rayon
4. Implement a lock-free concurrent data structure
5. Optimize a data processing pipeline using parallel iterators
6. Create a work-stealing thread pool from scratch

Advanced Challenges

1. Implement an optimized SIMD-accelerated string search algorithm
2. Create a parallel graph processing framework
3. Build a high-performance parallel hash map implementation
4. Develop a vectorized compression algorithm
5. Implement a parallel machine learning algorithm

Next Steps

After completing Week 20, you’ll be able to optimize computationally intensive code using both data and task parallelism. Week 21 will build on this by exploring advanced compiler and memory optimizations to squeeze the most performance out of your Rust code.