Lecture

Optimization: Register Allocation

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In course

CS-420: Advanced compiler construction

Students learn several implementation techniques for modern functional and object-oriented programming languages. They put some of them into practice by developing key parts of a compiler and run time system for a simple functional programming language. ...

Description

This lecture covers advanced compiler construction topics related to optimization techniques such as dead code elimination, common subexpression elimination, constant folding, and register allocation. The instructor explains how register allocation transforms a program using virtual registers into one using physical machine registers, with some registers possibly being spilled to memory. Techniques like graph coloring and linear scan register allocation are discussed, along with inlining heuristics and contification. The lecture also delves into interference graphs, contifiable subsets, and the process of shrinking optimizations. Various examples and exercises are provided to illustrate the concepts.

Instructor

Michel Schinz

Teaching & PhD Courses Advanced compiler construction CS-420 Students learn several implementation techniques for modern functional and object-oriented programming languages. They put some of them into practice by developing key parts of a compiler and run time system for a simple functional programming language. Practice of object-oriented programming CS-108 Students improve their knowledge of Java and put it into practice by implementing a sizeable project. They learn how t

Official source

Ontological neighbourhood

Computer engineering

Computer programming: Compilers

Related concepts (30)

Optimizing compiler

In computing, an optimizing compiler is a compiler that tries to minimize or maximize some attributes of an executable computer program. Common requirements are to minimize a program's execution time, memory footprint, storage size, and power consumption (the last three being popular for portable computers). Compiler optimization is generally implemented using a sequence of optimizing transformations, algorithms which take a program and transform it to produce a semantically equivalent output program that uses fewer resources or executes faster.

Dead-code elimination

In compiler theory, dead-code elimination (DCE, dead-code removal, dead-code stripping, or dead-code strip) is a compiler optimization to remove dead code (code that does not affect the program results). Removing such code has several benefits: it shrinks program size, an important consideration in some contexts, and it allows the running program to avoid executing irrelevant operations, which reduces its running time. It can also enable further optimizations by simplifying program structure.

Fractional coloring

Fractional coloring is a topic in a young branch of graph theory known as fractional graph theory. It is a generalization of ordinary graph coloring. In a traditional graph coloring, each vertex in a graph is assigned some color, and adjacent vertices — those connected by edges — must be assigned different colors. In a fractional coloring however, a set of colors is assigned to each vertex of a graph. The requirement about adjacent vertices still holds, so if two vertices are joined by an edge, they must have no colors in common.

Inline expansion

In computing, inline expansion, or inlining, is a manual or compiler optimization that replaces a function call site with the body of the called function. Inline expansion is similar to macro expansion, but occurs during compilation, without changing the source code (the text), while macro expansion occurs prior to compilation, and results in different text that is then processed by the compiler. Inlining is an important optimization, but has complicated effects on performance.

Intermediate representation

An intermediate representation (IR) is the data structure or code used internally by a compiler or virtual machine to represent source code. An IR is designed to be conducive to further processing, such as optimization and translation. A "good" IR must be accurate – capable of representing the source code without loss of information – and independent of any particular source or target language. An IR may take one of several forms: an in-memory data structure, or a special tuple- or stack-based code readable by the program.

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