Having represented machine learning computations using intermediate representations, we now focus on optimizing the structure of the computation graph itself. These high-level transformations aim to reduce redundant operations, improve data locality, minimize kernel launch overhead, and restructure the graph for better hardware utilization before we proceed to detailed tensor-level optimizations.

This chapter examines techniques operating directly on the graph representation of ML models. We will cover:

• Graph rewriting systems: The underlying mechanisms for finding and applying graph transformations.
• Operator fusion: Combining multiple operations into single, more efficient kernels to reduce memory traffic and overhead. We'll look at strategies that go beyond simple adjacent node fusion.
• Data layout transformations: Optimizing the physical arrangement of tensor data (e.g., NCHW vs. NHWC) based on target hardware and operator sequences.
• Algebraic simplification: Applying mathematical identities to simplify or eliminate operations within the graph.
• Static memory planning: Techniques to minimize peak memory usage by analyzing the graph's dependencies.
• Control flow handling: Strategies for optimizing graphs containing conditional execution or loops.

We will also implement a basic fusion pass to solidify these concepts. By the end of this chapter, you will understand how to apply sophisticated graph-level optimizations to prepare ML models for efficient execution.