mirror of
https://github.com/MLSysBook/TinyTorch.git
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45a9cef548
- Removed 01_setup module (archived to archive/setup_module) - Renumbered all modules: tensor is now 01, activations is 02, etc. - Added tito setup command for environment setup and package installation - Added numeric shortcuts: tito 01, tito 02, etc. for quick module access - Fixed view command to find dev files correctly - Updated module dependencies and references - Improved user experience: immediate ML learning instead of boring setup
41 lines
1.5 KiB
YAML
41 lines
1.5 KiB
YAML
assessment:
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- Understand why naive loops have poor cache performance
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- Implement cache-friendly blocked matrix multiplication showing 10-50x speedups
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- Recognize why NumPy provides 100x+ speedups over custom implementations
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- Build backend system that automatically chooses optimal implementations
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- 'Apply the ''free speedup'' principle: use better tools, don''t write faster code'
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description: 'Master the easiest optimization: using better backends! Learn why naive
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loops are slow, how cache-friendly blocking helps, and why NumPy provides 100x+
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speedups.'
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difficulty: Advanced
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estimated_time: 3-4 hours
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exports:
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- matmul_naive
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- matmul_blocked
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- matmul_numpy
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- OptimizedBackend
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- matmul
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- set_backend
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learning_objectives:
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- Understand CPU cache hierarchy and memory access performance bottlenecks
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- Implement cache-friendly blocked matrix multiplication algorithms
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- Build vectorized operations with optimized memory access patterns
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- Design transparent backend systems for automatic optimization selection
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- Measure and quantify real performance improvements scientifically
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- Apply systems thinking to optimization decisions in ML workflows
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name: acceleration
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prerequisites:
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- 'Module 2: Tensor operations and NumPy fundamentals'
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- 'Module 4: Linear layers and matrix multiplication'
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- Understanding of basic algorithmic complexity (O notation)
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tags:
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- performance
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- optimization
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- systems
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- hardware
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- acceleration
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- cache
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- vectorization
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- backends
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title: Hardware Acceleration - The Simplest Optimization
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