Topic
Loop-invariant code motion
About: Loop-invariant code motion is a research topic. Over the lifetime, 402 publications have been published within this topic receiving 18338 citations.
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Book•
1 Jan 1986TL;DR: This book discusses the design of a Code Generator, the role of the Lexical Analyzer, and other topics related to code generation and optimization.
Abstract: 1 Introduction 1.1 Language Processors 1.2 The Structure of a Compiler 1.3 The Evolution of Programming Languages 1.4 The Science of Building a Compiler 1.5 Applications of Compiler Technology 1.6 Programming Language Basics 1.7 Summary of Chapter 1 1.8 References for Chapter 1 2 A Simple Syntax-Directed Translator 2.1 Introduction 2.2 Syntax Definition 2.3 Syntax-Directed Translation 2.4 Parsing 2.5 A Translator for Simple Expressions 2.6 Lexical Analysis 2.7 Symbol Tables 2.8 Intermediate Code Generation 2.9 Summary of Chapter 2 3 Lexical Analysis 3.1 The Role of the Lexical Analyzer 3.2 Input Buffering 3.3 Specification of Tokens 3.4 Recognition of Tokens 3.5 The Lexical-Analyzer Generator Lex 3.6 Finite Automata 3.7 From Regular Expressions to Automata 3.8 Design of a Lexical-Analyzer Generator 3.9 Optimization of DFA-Based Pattern Matchers 3.10 Summary of Chapter 3 3.11 References for Chapter 3 4 Syntax Analysis 4.1 Introduction 4.2 Context-Free Grammars 4.3 Writing a Grammar 4.4 Top-Down Parsing 4.5 Bottom-Up Parsing 4.6 Introduction to LR Parsing: Simple LR 4.7 More Powerful LR Parsers 4.8 Using Ambiguous Grammars 4.9 Parser Generators 4.10 Summary of Chapter 4 4.11 References for Chapter 4 5 Syntax-Directed Translation 5.1 Syntax-Directed Definitions 5.2 Evaluation Orders for SDD's 5.3 Applications of Syntax-Directed Translation 5.4 Syntax-Directed Translation Schemes 5.5 Implementing L-Attributed SDD's 5.6 Summary of Chapter 5 5.7 References for Chapter 5 6 Intermediate-Code Generation 6.1 Variants of Syntax Trees 6.2 Three-Address Code 6.3 Types and Declarations 6.4 Translation of Expressions 6.5 Type Checking 6.6 Control Flow 6.7 Backpatching 6.8 Switch-Statements 6.9 Intermediate Code for Procedures 6.10 Summary of Chapter 6 6.11 References for Chapter 6 7 Run-Time Environments 7.1 Storage Organization 7.2 Stack Allocation of Space 7.3 Access to Nonlocal Data on the Stack 7.4 Heap Management 7.5 Introduction to Garbage Collection 7.6 Introduction to Trace-Based Collection 7.7 Short-Pause Garbage Collection 7.8 Advanced Topics in Garbage Collection 7.9 Summary of Chapter 7 7.10 References for Chapter 7 8 Code Generation 8.1 Issues in the Design of a Code Generator 8.2 The Target Language 8.3 Addresses in the Target Code 8.4 Basic Blocks and Flow Graphs 8.5 Optimization of Basic Blocks 8.6 A Simple Code Generator 8.7 Peephole Optimization 8.8 Register Allocation and Assignment 8.9 Instruction Selection by Tree Rewriting 8.10 Optimal Code Generation for Expressions 8.11 Dynamic Programming Code-Generation 8.12 Summary of Chapter 8 8.13 References for Chapter 8 9 Machine-Independent Optimizations 9.1 The Principal Sources of Optimization 9.2 Introduction to Data-Flow Analysis 9.3 Foundations of Data-Flow Analysis 9.4 Constant Propagation 9.5 Partial-Redundancy Elimination 9.6 Loops in Flow Graphs 9.7 Region-Based Analysis 9.8 Symbolic Analysis 9.9 Summary of Chapter 9 9.10 References for Chapter 9 10 Instruction-Level Parallelism 10.1 Processor Architectures 10.2 Code-Scheduling Constraints 10.3 Basic-Block Scheduling 10.4 Global Code Scheduling 10.5 Software Pipelining 10.6 Summary of Chapter 10 10.7 References for Chapter 10 11 Optimizing for Parallelism and Locality 11.1 Basic Concepts 11.2 Matrix Multiply: An In-Depth Example 11.3 Iteration Spaces 11.4 Affine Array Indexes 11.5 Data Reuse 11.6 Array Data-Dependence Analysis 11.7 Finding Synchronization-Free Parallelism 11.8 Synchronization Between Parallel Loops 11.9 Pipelining 11.10 Locality Optimizations 11.11 Other Uses of Affine Transforms 11.12 Summary of Chapter 11 11.13 References for Chapter 11 12 Interprocedural Analysis 12.1 Basic Concepts 12.2 Why Interprocedural Analysis? 12.3 A Logical Representation of Data Flow 12.4 A Simple Pointer-Analysis Algorithm 12.5 Context-Insensitive Interprocedural Analysis 12.6 Context-Sensitive Pointer Analysis 12.7 Datalog Implementation by BDD's 12.8 Summary of Chapter 12 12.9 References for Chapter 12 A A Complete Front End A.1 The Source Language A.2 Main A.3 Lexical Analyzer A.4 Symbol Tables and Types A.5 Intermediate Code for Expressions A.6 Jumping Code for Boolean Expressions A.7 Intermediate Code for Statements A.8 Parser A.9 Creating the Front End B Finding Linearly Independent Solutions Index
9,735 citations
TL;DR: The paper presents an efficient 88 line MATLAB code for topology optimization using the 99 line code presented by Sigmund as a starting point, and a considerable improvement in efficiency has been achieved, mainly by preallocating arrays and vectorizing loops.
Abstract: The paper presents an efficient 88 line MATLAB code for topology optimization. It has been developed using the 99 line code presented by Sigmund (Struct Multidisc Optim 21(2):120---127, 2001) as a starting point. The original code has been extended by a density filter, and a considerable improvement in efficiency has been achieved, mainly by preallocating arrays and vectorizing loops. A speed improvement with a factor of 100 is obtained for a benchmark example with 7,500 elements. Moreover, the length of the code has been reduced to a mere 88 lines. These improvements have been accomplished without sacrificing the readability of the code. The 88 line code can therefore be considered as a valuable successor to the 99 line code, providing a practical instrument that may help to ease the learning curve for those entering the field of topology optimization. The paper also discusses simple extensions of the basic code to include recent PDE-based and black-and-white projection filtering methods. The complete 88 line code is included as an appendix and can be downloaded from the web site www.topopt.dtu.dk .
1,423 citations
TL;DR: This article explores the use of compiler techniques to accomplish code compaction to yield smaller executables and shows that careful, aggressive, interprocedural optimization, together with procedural abstraction of repeated code fragments, can yield significantly better reductions in code size than previous approaches.
Abstract: In recent years there has been an increasing trend toward the incorpor ation of computers into a variety of devices where the amount of memory available is limited. This makes it desirable to try to reduce the size of applications where possible. This article explores the use of compiler techniques to accomplish code compaction to yield smaller executables. The main contribution of this article is to show that careful, aggressive, interprocedural optimization, together with procedural abstraction of repeated code fragments, can yield significantly better reductions in code size than previous approaches, which have generally focused on abstraction of repeated instruction sequences. We also show how “equivalent” code fragments can be detected and factored out using conventional compiler techniques, and without having to resort to purely linear treatments of code sequences as in suffix-tree-based approaches, thereby setting up a framework for code compaction that can be more flexible in its treatment of what code fragments are considered equivalent. Our ideas have been implemented in the form of a binary-rewriting tool that reduces the size of executables by about 30% on the average.
343 citations
1 Jan 1998
TL;DR: This dissertation shows how standard decision procedures can be adapted so that they can produce detailed proofs of the proved predicates and also how these proofs can be encoded compactly and checked efficiently.
Abstract: One of the major challenges of building software systems is to ensure that the various components fit together in a well-defined manner. This problem is exacerbated by the recent advent of software components whose origin is unknown or inherently untrusted, such as mobile code or user extensions for operating-system kernels or database servers. Such extensions are useful for implementing an efficient interaction model between a client and a server because several data exchanges between them can be saved at the cost of a single code exchange.
In this dissertation, I propose to tackle such system integrity and security problems with techniques from mathematical logic and programming-language semantics. I propose a framework, called proof-carrying code, in which the extension provider sends along with the extension code a representation of a formal proof that the code meets certain safety and correctness requirements. Then, the code receiver can ensure the safety of executing the extension by validating the attached proof. The major advantages of proof-carrying code are that it requires a simple trusted infrastructure and that it does not impose run-time penalties for the purpose of ensuring safety.
In addition to the concept of proof-carrying code, this dissertation contributes the idea of certifying compilation. A certifying compiler emits, in addition to optimized target code, function specifications and loop invariants that enable a theorem-proving agent to prove non-trivial properties of the target code, such as type safety. Such a certifying compiler, along with a proof-generating theorem prover, is not only a convenient producer of proof-carrying code but also a powerful software-engineering tool. The certifier also acts as an effective referee for the correctness of each compilation, thus simplifying considerably compiler testing and maintenance.
A complete system for proof-carrying code must also contain a proof-generating theorem prover for the purpose of producing the attached proofs of safety. This dissertation shows how standard decision procedures can be adapted so that they can produce detailed proofs of the proved predicates and also how these proofs can be encoded compactly and checked efficiently. Just like for the certifying compiler, a proof-generating theorem prover has significant software-engineering advantages over a traditional prover. In this case, a simple proof checker can ensure the soundness of each successful proving task and indirectly assist in testing and maintenance of the theorem prover.
227 citations
1 May 1999
TL;DR: This work focuses on reducing the size of a program's code segment, using pattern-matching techniques to identify and coalesce together repeated instruction sequences, and develops a new form of profile-driven code compression that reduces the speed penalty arising from compression.
Abstract: This paper explores compiler techniques for reducing the memory needed to load and run program executables. In embedded systems, where economic incentives to reduce both RAM and ROM are strong, the size of compiled code is increasingly important. Similarly, in mobile and network computing, the need to transmit an executable before running it places a premium on code size. Our work focuses on reducing the size of a program's code segment, using pattern-matching techniques to identify and coalesce together repeated instruction sequences. In contrast to other methods, our framework preserves the ability to run program executables directly, without an intervening decompression stage. Our compression framework is integrated into an industrial-strength optimizing compiler, which allows us to explore the interaction between code compression and classical code optimization techniques, and requires that we contend with the difficulties of compressing previously optimized code. The specific contributions in this paper include a comprehensive experimental evaluation of code compression for a RISC-like architecture, a more powerful pattern-matching scheme for improved identification of repeated code fragments, and a new form of profile-driven code compression that reduces the speed penalty arising from compression.
167 citations
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Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 3 |
| 2020 | 1 |
| 2019 | 2 |
| 2018 | 1 |
| 2017 | 6 |
| 2016 | 2 |