Introduction to ML Compiler Optimization
Chapter 1: The ML Compilation Stack
The Framework-Hardware Gap
Anatomy of an ML Compiler
AOT versus JIT Compilation
Tracing and Graph Capture
Environment Setup Practice
Chapter 2: Intermediate Representations
Role of Intermediate Representation
Dataflow Graphs and Dependencies
Tensor Shapes and Dtypes
Static versus Dynamic Shapes
Inspecting IR Structure
Chapter 3: Graph-Level Optimizations
Operator Fusion Strategies
Constant Folding and Propagation
Dead Code Elimination
Memory Layout Transformation
Common Subexpression Elimination
Implementing a Graph Pass
Chapter 4: Kernel and Loop Optimization
Loop Tiling and Cache Locality
Vectorization for SIMD
Loop Unrolling and Reordering
Parallelization Strategies
Memory Latency Hiding
Matrix Multiplication Practice
Chapter 5: Auto-Tuning and Code Generation
Defining the Search Space
Cost Models in Auto-Tuning
Automated Schedule Search
Code Generation Backends
Running an Auto-Tuning Session
Tensor Shapes and Dtypes
Was this section helpful?
© 2025 ApX Machine LearningEngineered with