Lecture

Code Optimization: Speeding-up Analyses

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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 techniques to speed up dataflow analyses, such as work-list algorithms, equation ordering, smaller CFGs, and bit-vectors. It illustrates these techniques using a live variables example and discusses the importance of node ordering and post-order traversal. The lecture also delves into the work-list algorithm in Scala, basic blocks, and the use of bit vectors to represent sets. Additionally, it explores the significance of intermediate representations (IRs) in optimizations, focusing on machine-independent rewriting optimizations in CPS/L3. The session concludes with discussions on dead code elimination, common subexpression elimination, inlining, and constant folding, emphasizing the impact of optimization contexts and heuristics.

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 (32)

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.

Program optimization

In computer science, program optimization, code optimization, or software optimization, is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources. In general, a computer program may be optimized so that it executes more rapidly, or to make it capable of operating with less memory storage or other resources, or draw less power. Although the word "optimization" shares the same root as "optimal", it is rare for the process of optimization to produce a truly optimal system.

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

Peephole optimization

Peephole optimization is an optimization technique performed on a small set of compiler-generated instructions; the small set is known as the peephole or window. Peephole optimization involves changing the small set of instructions to an equivalent set that has better performance.

Common subexpression elimination

In compiler theory, common subexpression elimination (CSE) is a compiler optimization that searches for instances of identical expressions (i.e., they all evaluate to the same value), and analyzes whether it is worthwhile replacing them with a single variable holding the computed value. In the following code: a = b * c + g; d = b * c * e; it may be worth transforming the code to: tmp = b * c; a = tmp + g; d = tmp * e; if the cost of storing and retrieving tmp is less than the cost of calculating b * c an extra time.

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