趋近智
Introduction to ML Compiler Optimization
章节 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
章节 2: Intermediate Representations
Role of Intermediate Representation
Dataflow Graphs and Dependencies
Tensor Shapes and Dtypes
Static versus Dynamic Shapes
Inspecting IR Structure
章节 3: Graph-Level Optimizations
Operator Fusion Strategies
Constant Folding and Propagation
Dead Code Elimination
Memory Layout Transformation
Common Subexpression Elimination
Implementing a Graph Pass
章节 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
章节 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
Introduction to ML Compiler Optimization
先修课程 Basic ML & programming
级别:
中级
Compiler Architecture
Understand the components of an ML compiler stack, including frontend, intermediate representation, and backend code generation.
Graph Optimization
Apply high-level transformations such as operator fusion, constant folding, and dead code elimination to computation graphs.
Loop Scheduling
Implement low-level optimizations including tiling, vectorization, and loop reordering to maximize hardware utilization.
Auto-Tuning
Configure and run automated search processes to identify optimal parameters for specific hardware targets.
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