common subexpression elimination

**Common subexpression elimination** is the **optimization pass that reuses identical computation results instead of recomputing them** - it removes redundant graph branches and lowers both compute and memory overhead. **What Is Common subexpression elimination?** - **Definition**: Detect duplicate expression trees and replace repeated instances with shared computed values. - **Target Patterns**: Repeated arithmetic, repeated transform chains, and structurally equivalent subgraphs. - **Runtime Benefit**: Fewer arithmetic ops and reduced intermediate tensor creation. - **Applicability**: Requires expression equivalence under same inputs and side-effect-free semantics. **Why Common subexpression elimination Matters** - **Compute Reduction**: Eliminates duplicated expensive operations in complex model graphs. - **Memory Savings**: Shared intermediate use can reduce allocation pressure and cache churn. - **Compiler Efficiency**: Simpler graphs are easier to further optimize and schedule. - **Inference Latency**: Redundant-op removal often improves tail latency in serving paths. - **Energy Efficiency**: Less duplicated work lowers power consumed per inference or step. **How It Is Used in Practice** - **IR Equivalence Analysis**: Run CSE pass with robust hashing and structural comparison of nodes. - **Safety Checks**: Confirm no mutation or side effects invalidate shared-expression reuse. - **Performance Validation**: Benchmark before and after to ensure elimination produces measurable gains. Common subexpression elimination is **a high-value redundancy-removal optimization** - reusing equivalent computations improves efficiency without changing model semantics.