Approximate Reachability for Dead Code Elimination in Esterel
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A Methodology for Assessing Javascript Software Protections
A methodology for Assessing JavaScript Software Protections Pedro Fortuna A methodology for Assessing JavaScript Software Protections Pedro Fortuna About me Pedro Fortuna Co-Founder & CTO @ JSCRAMBLER OWASP Member SECURITY, JAVASCRIPT @pedrofortuna 2 A methodology for Assessing JavaScript Software Protections Pedro Fortuna Agenda 1 4 7 What is Code Protection? Testing Resilience 2 5 Code Obfuscation Metrics Conclusions 3 6 JS Software Protections Q & A Checklist 3 What is Code Protection Part 1 A methodology for Assessing JavaScript Software Protections Pedro Fortuna Intellectual Property Protection Legal or Technical Protection? Alice Bob Software Developer Reverse Engineer Sells her software over the Internet Wants algorithms and data structures Does not need to revert back to original source code 5 A methodology for Assessing JavaScript Software Protections Pedro Fortuna Intellectual Property IP Protection Protection Legal Technical Encryption ? Trusted Computing Server-Side Execution Obfuscation 6 A methodology for Assessing JavaScript Software Protections Pedro Fortuna Code Obfuscation Obfuscation “transforms a program into a form that is more difficult for an adversary to understand or change than the original code” [1] More Difficult “requires more human time, more money, or more computing power to analyze than the original program.” [1] in Collberg, C., and Nagra, J., “Surreptitious software: obfuscation, watermarking, and tamperproofing for software protection.”, Addison- Wesley Professional, 2010. 7 A methodology for Assessing -
The LLVM Instruction Set and Compilation Strategy
The LLVM Instruction Set and Compilation Strategy Chris Lattner Vikram Adve University of Illinois at Urbana-Champaign lattner,vadve ¡ @cs.uiuc.edu Abstract This document introduces the LLVM compiler infrastructure and instruction set, a simple approach that enables sophisticated code transformations at link time, runtime, and in the field. It is a pragmatic approach to compilation, interfering with programmers and tools as little as possible, while still retaining extensive high-level information from source-level compilers for later stages of an application’s lifetime. We describe the LLVM instruction set, the design of the LLVM system, and some of its key components. 1 Introduction Modern programming languages and software practices aim to support more reliable, flexible, and powerful software applications, increase programmer productivity, and provide higher level semantic information to the compiler. Un- fortunately, traditional approaches to compilation either fail to extract sufficient performance from the program (by not using interprocedural analysis or profile information) or interfere with the build process substantially (by requiring build scripts to be modified for either profiling or interprocedural optimization). Furthermore, they do not support optimization either at runtime or after an application has been installed at an end-user’s site, when the most relevant information about actual usage patterns would be available. The LLVM Compilation Strategy is designed to enable effective multi-stage optimization (at compile-time, link-time, runtime, and offline) and more effective profile-driven optimization, and to do so without changes to the traditional build process or programmer intervention. LLVM (Low Level Virtual Machine) is a compilation strategy that uses a low-level virtual instruction set with rich type information as a common code representation for all phases of compilation. -
Attacking Client-Side JIT Compilers.Key
Attacking Client-Side JIT Compilers Samuel Groß (@5aelo) !1 A JavaScript Engine Parser JIT Compiler Interpreter Runtime Garbage Collector !2 A JavaScript Engine • Parser: entrypoint for script execution, usually emits custom Parser bytecode JIT Compiler • Bytecode then consumed by interpreter or JIT compiler • Executing code interacts with the Interpreter runtime which defines the Runtime representation of various data structures, provides builtin functions and objects, etc. Garbage • Garbage collector required to Collector deallocate memory !3 A JavaScript Engine • Parser: entrypoint for script execution, usually emits custom Parser bytecode JIT Compiler • Bytecode then consumed by interpreter or JIT compiler • Executing code interacts with the Interpreter runtime which defines the Runtime representation of various data structures, provides builtin functions and objects, etc. Garbage • Garbage collector required to Collector deallocate memory !4 A JavaScript Engine • Parser: entrypoint for script execution, usually emits custom Parser bytecode JIT Compiler • Bytecode then consumed by interpreter or JIT compiler • Executing code interacts with the Interpreter runtime which defines the Runtime representation of various data structures, provides builtin functions and objects, etc. Garbage • Garbage collector required to Collector deallocate memory !5 A JavaScript Engine • Parser: entrypoint for script execution, usually emits custom Parser bytecode JIT Compiler • Bytecode then consumed by interpreter or JIT compiler • Executing code interacts with the Interpreter runtime which defines the Runtime representation of various data structures, provides builtin functions and objects, etc. Garbage • Garbage collector required to Collector deallocate memory !6 Agenda 1. Background: Runtime Parser • Object representation and Builtins JIT Compiler 2. JIT Compiler Internals • Problem: missing type information • Solution: "speculative" JIT Interpreter 3. -
CS153: Compilers Lecture 19: Optimization
CS153: Compilers Lecture 19: Optimization Stephen Chong https://www.seas.harvard.edu/courses/cs153 Contains content from lecture notes by Steve Zdancewic and Greg Morrisett Announcements •HW5: Oat v.2 out •Due in 2 weeks •HW6 will be released next week •Implementing optimizations! (and more) Stephen Chong, Harvard University 2 Today •Optimizations •Safety •Constant folding •Algebraic simplification • Strength reduction •Constant propagation •Copy propagation •Dead code elimination •Inlining and specialization • Recursive function inlining •Tail call elimination •Common subexpression elimination Stephen Chong, Harvard University 3 Optimizations •The code generated by our OAT compiler so far is pretty inefficient. •Lots of redundant moves. •Lots of unnecessary arithmetic instructions. •Consider this OAT program: int foo(int w) { var x = 3 + 5; var y = x * w; var z = y - 0; return z * 4; } Stephen Chong, Harvard University 4 Unoptimized vs. Optimized Output .globl _foo _foo: •Hand optimized code: pushl %ebp movl %esp, %ebp _foo: subl $64, %esp shlq $5, %rdi __fresh2: movq %rdi, %rax leal -64(%ebp), %eax ret movl %eax, -48(%ebp) movl 8(%ebp), %eax •Function foo may be movl %eax, %ecx movl -48(%ebp), %eax inlined by the compiler, movl %ecx, (%eax) movl $3, %eax so it can be implemented movl %eax, -44(%ebp) movl $5, %eax by just one instruction! movl %eax, %ecx addl %ecx, -44(%ebp) leal -60(%ebp), %eax movl %eax, -40(%ebp) movl -44(%ebp), %eax Stephen Chong,movl Harvard %eax,University %ecx 5 Why do we need optimizations? •To help programmers… •They write modular, clean, high-level programs •Compiler generates efficient, high-performance assembly •Programmers don’t write optimal code •High-level languages make avoiding redundant computation inconvenient or impossible •e.g. -
Quaxe, Infinity and Beyond
Quaxe, infinity and beyond Daniel Glazman — WWX 2015 /usr/bin/whoami Primary architect and developer of the leading Web and Ebook editors Nvu and BlueGriffon Former member of the Netscape CSS and Editor engineering teams Involved in Internet and Web Standards since 1990 Currently co-chair of CSS Working Group at W3C New-comer in the Haxe ecosystem Desktop Frameworks Visual Studio (Windows only) Xcode (OS X only) Qt wxWidgets XUL Adobe Air Mobile Frameworks Adobe PhoneGap/Air Xcode (iOS only) Qt Mobile AppCelerator Visual Studio Two solutions but many issues Fragmentation desktop/mobile Heavy runtimes Can’t easily reuse existing c++ libraries Complex to have native-like UI Qt/QtMobile still require c++ Qt’s QML is a weak and convoluted UI language Haxe 9 years success of Multiplatform OSS language Strong affinity to gaming Wide and vibrant community Some press recognition Dead code elimination Compiles to native on all But no native GUI… platforms through c++ and java Best of all worlds Haxe + Qt/QtMobile Multiplatform Native apps, native performance through c++/Java C++/Java lib reusability Introducing Quaxe Native apps w/o c++ complexity Highly dynamic applications on desktop and mobile Native-like UI through Qt HTML5-based UI, CSS-based styling Benefits from Haxe and Qt communities Going from HTML5 to native GUI completeness DOM dynamism in native UI var b: Element = document.getElementById("thirdButton"); var t: Element = document.createElement("input"); t.setAttribute("type", "text"); t.setAttribute("value", "a text field"); b.parentNode.insertBefore(t, -
Analysis of Program Optimization Possibilities and Further Development
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Theoretical Computer Science 90 (1991) 17-36 17 Elsevier Analysis of program optimization possibilities and further development I.V. Pottosin Institute of Informatics Systems, Siberian Division of the USSR Acad. Sci., 630090 Novosibirsk, USSR 1. Introduction Program optimization has appeared in the framework of program compilation and includes special techniques and methods used in compiler construction to obtain a rather efficient object code. These techniques and methods constituted in the past and constitute now an essential part of the so called optimizing compilers whose goal is to produce an object code in run time, saving such computer resources as CPU time and memory. For contemporary supercomputers, the requirement of the proper use of hardware peculiarities is added. In our opinion, the existence of a sufficiently large number of optimizing compilers with real possibilities of producing “good” object code has evidently proved to be practically significant for program optimization. The methods and approaches that have accumulated in program optimization research seem to be no lesser valuable, since they may be successfully used in the general techniques of program construc- tion, i.e. in program synthesis, program transformation and at different steps of program development. At the same time, there has been criticism on program optimization and even doubts about its necessity. On the one hand, this viewpoint is based on blind faith in the new possibilities bf computers which will be so powerful that any necessity to worry about program efficiency will disappear. -
Dead Code Elimination for Web Applications Written in Dynamic Languages
Dead code elimination for web applications written in dynamic languages Master’s Thesis Hidde Boomsma Dead code elimination for web applications written in dynamic languages THESIS submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in COMPUTER ENGINEERING by Hidde Boomsma born in Naarden 1984, the Netherlands Software Engineering Research Group Department of Software Technology Department of Software Engineering Faculty EEMCS, Delft University of Technology Hostnet B.V. Delft, the Netherlands Amsterdam, the Netherlands www.ewi.tudelft.nl www.hostnet.nl © 2012 Hidde Boomsma. All rights reserved Cover picture: Hoster the Hostnet mascot Dead code elimination for web applications written in dynamic languages Author: Hidde Boomsma Student id: 1174371 Email: [email protected] Abstract Dead code is source code that is not necessary for the correct execution of an application. Dead code is a result of software ageing. It is a threat for maintainability and should therefore be removed. Many organizations in the web domain have the problem that their software grows and de- mands increasingly more effort to maintain, test, check out and deploy. Old features often remain in the software, because their dependencies are not obvious from the software documentation. Dead code can be found by collecting the set of code that is used and subtract this set from the set of all code. Collecting the set can be done statically or dynamically. Web applications are often written in dynamic languages. For dynamic languages dynamic analysis suits best. From the maintainability perspective a dynamic analysis is preferred over static analysis because it is able to detect reachable but unused code. -
Lecture Notes on Peephole Optimizations and Common Subexpression Elimination
Lecture Notes on Peephole Optimizations and Common Subexpression Elimination 15-411: Compiler Design Frank Pfenning and Jan Hoffmann Lecture 18 October 31, 2017 1 Introduction In this lecture, we discuss common subexpression elimination and a class of optimiza- tions that is called peephole optimizations. The idea of common subexpression elimination is to avoid to perform the same operation twice by replacing (binary) operations with variables. To ensure that these substitutions are sound we intro- duce dominance, which ensures that substituted variables are always defined. Peephole optimizations are optimizations that are performed locally on a small number of instructions. The name is inspired from the picture that we look at the code through a peephole and make optimization that only involve the small amount code we can see and that are indented of the rest of the program. There is a large number of possible peephole optimizations. The LLVM com- piler implements for example more than 1000 peephole optimizations [LMNR15]. In this lecture, we discuss three important and representative peephole optimiza- tions: constant folding, strength reduction, and null sequences. 2 Constant Folding Optimizations have two components: (1) a condition under which they can be ap- plied and the (2) code transformation itself. The optimization of constant folding is a straightforward example of this. The code transformation itself replaces a binary operation with a single constant, and applies whenever c1 c2 is defined. l : x c1 c2 −! l : x c (where c = -
Register Allocation and Method Inlining
Combining Offline and Online Optimizations: Register Allocation and Method Inlining Hiroshi Yamauchi and Jan Vitek Department of Computer Sciences, Purdue University, {yamauchi,jv}@cs.purdue.edu Abstract. Fast dynamic compilers trade code quality for short com- pilation time in order to balance application performance and startup time. This paper investigates the interplay of two of the most effective optimizations, register allocation and method inlining for such compil- ers. We present a bytecode representation which supports offline global register allocation, is suitable for fast code generation and verification, and yet is backward compatible with standard Java bytecode. 1 Introduction Programming environments which support dynamic loading of platform-indep- endent code must provide supports for efficient execution and find a good balance between responsiveness (shorter delays due to compilation) and performance (optimized compilation). Thus, most commercial Java virtual machines (JVM) include several execution engines. Typically, there is an interpreter or a fast com- piler for initial executions of all code, and a profile-guided optimizing compiler for performance-critical code. Improving the quality of the code of a fast compiler has the following bene- fits. It raises the performance of short-running and medium length applications which exit before the expensive optimizing compiler fully kicks in. It also ben- efits long-running applications with improved startup performance and respon- siveness (due to less eager optimizing compilation). One way to achieve this is to shift some of the burden to an offline compiler. The main question is what optimizations are profitable when performed offline and are either guaranteed to be safe or can be easily validated by a fast compiler. -
Generalizing Loop-Invariant Code Motion in a Real-World Compiler
Imperial College London Department of Computing Generalizing loop-invariant code motion in a real-world compiler Author: Supervisor: Paul Colea Fabio Luporini Co-supervisor: Prof. Paul H. J. Kelly MEng Computing Individual Project June 2015 Abstract Motivated by the perpetual goal of automatically generating efficient code from high-level programming abstractions, compiler optimization has developed into an area of intense research. Apart from general-purpose transformations which are applicable to all or most programs, many highly domain-specific optimizations have also been developed. In this project, we extend such a domain-specific compiler optimization, initially described and implemented in the context of finite element analysis, to one that is suitable for arbitrary applications. Our optimization is a generalization of loop-invariant code motion, a technique which moves invariant statements out of program loops. The novelty of the transformation is due to its ability to avoid more redundant recomputation than normal code motion, at the cost of additional storage space. This project provides a theoretical description of the above technique which is fit for general programs, together with an implementation in LLVM, one of the most successful open-source compiler frameworks. We introduce a simple heuristic-driven profitability model which manages to successfully safeguard against potential performance regressions, at the cost of missing some speedup opportunities. We evaluate the functional correctness of our implementation using the comprehensive LLVM test suite, passing all of its 497 whole program tests. The results of our performance evaluation using the same set of tests reveal that generalized code motion is applicable to many programs, but that consistent performance gains depend on an accurate cost model. -
Efficient Symbolic Analysis for Optimizing Compilers*
Efficient Symbolic Analysis for Optimizing Compilers? Robert A. van Engelen Dept. of Computer Science, Florida State University, Tallahassee, FL 32306-4530 [email protected] Abstract. Because most of the execution time of a program is typically spend in loops, loop optimization is the main target of optimizing and re- structuring compilers. An accurate determination of induction variables and dependencies in loops is of paramount importance to many loop opti- mization and parallelization techniques, such as generalized loop strength reduction, loop parallelization by induction variable substitution, and loop-invariant expression elimination. In this paper we present a new method for induction variable recognition. Existing methods are either ad-hoc and not powerful enough to recognize some types of induction variables, or existing methods are powerful but not safe. The most pow- erful method known is the symbolic differencing method as demonstrated by the Parafrase-2 compiler on parallelizing the Perfect Benchmarks(R). However, symbolic differencing is inherently unsafe and a compiler that uses this method may produce incorrectly transformed programs without issuing a warning. In contrast, our method is safe, simpler to implement in a compiler, better adaptable for controlling loop transformations, and recognizes a larger class of induction variables. 1 Introduction It is well known that the optimization and parallelization of scientific applica- tions by restructuring compilers requires extensive analysis of induction vari- ables and dependencies -
The Effect of Code Expanding Optimizations on Instruction Cache Design
May 1991 UILU-EN G-91-2227 CRHC-91-17 Center for Reliable and High-Performance Computing THE EFFECT OF CODE EXPANDING OPTIMIZATIONS ON INSTRUCTION CACHE DESIGN William Y. Chen Pohua P. Chang Thomas M. Conte Wen-mei W. Hwu Coordinated Science Laboratory College of Engineering UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Approved for Public Release. Distribution Unlimited. UNCLASSIFIED____________ SECURITY ¿LASSIPlOvriON OF t h is PAGE REPORT DOCUMENTATION PAGE 1a. REPORT SECURITY CLASSIFICATION 1b. RESTRICTIVE MARKINGS Unclassified None 2a. SECURITY CLASSIFICATION AUTHORITY 3 DISTRIBUTION/AVAILABILITY OF REPORT 2b. DECLASSIFICATION/DOWNGRADING SCHEDULE Approved for public release; distribution unlimited 4. PERFORMING ORGANIZATION REPORT NUMBER(S) 5. MONITORING ORGANIZATION REPORT NUMBER(S) UILU-ENG-91-2227 CRHC-91-17 6a. NAME OF PERFORMING ORGANIZATION 6b. OFFICE SYMBOL 7a. NAME OF MONITORING ORGANIZATION Coordinated Science Lab (If applicable) NCR, NSF, AMD, NASA University of Illinois N/A 6c ADDRESS (G'ty, Staff, and ZIP Code) 7b. ADDRESS (Oty, Staff, and ZIP Code) 1101 W. Springfield Avenue Dayton, OH 45420 Urbana, IL 61801 Washington DC 20550 Langley VA 20200 8a. NAME OF FUNDING/SPONSORING 8b. OFFICE SYMBOL ORGANIZATION 9. PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER 7a (If applicable) N00014-91-J-1283 NASA NAG 1-613 8c. ADDRESS (City, State, and ZIP Cod*) 10. SOURCE OF FUNDING NUMBERS PROGRAM PROJECT t a sk WORK UNIT 7b ELEMENT NO. NO. NO. ACCESSION NO. The Effect of Code Expanding Optimizations on Instruction Cache Design 12. PERSONAL AUTHOR(S) Chen, William, Pohua Chang, Thomas Conte and Wen-Mei Hwu 13a. TYPE OF REPORT 13b. TIME COVERED 14. OATE OF REPORT (Year, Month, Day) Jl5.