The University of Chicago a Compiler-Based Online
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
US 2002/0066086 A1 Linden (43) Pub
US 2002O066086A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2002/0066086 A1 Linden (43) Pub. Date: May 30, 2002 (54) DYNAMIC RECOMPLER (52) U.S. Cl. ............................................ 717/145; 717/151 (76) Inventor: Randal N. Linden, Los Angeles, CA (57) ABSTRACT (US) A method for dynamic recompilation of Source Software Correspondence Address: instructions for execution by a target processor, which LU & LIU LLP considers not only the Specific Source instructions, but also 811 WEST SEVENTH STREET, SUITE 1100 the intent and purpose of the instructions, to translate and LOS ANGELES, CA 90017 (US) optimize a set of equivalent code for the target processor. The dynamic recompiler determines what the Source opera (21) Appl. No.: 09/755,542 tion code is trying to accomplish and the optimum way of Filed: Jan. 5, 2001 doing it at the target processor, in an “interpolative' and (22) context Sensitive fashion. The Source instructions are pro Related U.S. Application Data cessed in blocks of varying Sizes by the dynamic recompiler, which considers the instructions that come before and after (63) Non-provisional of provisional application No. a current instruction to determine the most efficient approach 60/175,008, filed on Jan. 7, 2000. out of Several available approaches for encoding the opera tion code for the target processor to perform the equivalent Publication Classification tasks Specified by the Source instructions. The dynamic compiler comprises a decoding Stage, an optimization Stage (51) Int. Cl. ................................................. G06F 9/45 and an encoding Stage. Patent Application Publication May 30, 2002 Sheet 1 of 3 US 2002/0066086 A1 Patent Application Publication May 30, 2002 Sheet 2 of 3 US 2002/0066086 A1 - \O, 2 Patent Application Publication May 30, 2002 Sheet 3 of 3 US 2002/0066086 A1 fo, 3 US 2002/0066086 A1 May 30, 2002 DYNAMIC RECOMPLER more target instructions to be executed in response to a given Source instruction. -
Generalized Jump Threading in Libfirm
Institut für Programmstrukturen und Datenorganisation (IPD) Lehrstuhl Prof. Dr.-Ing. Snelting Generalized Jump Threading in libFIRM Masterarbeit von Joachim Priesner an der Fakultät für Informatik Erstgutachter: Prof. Dr.-Ing. Gregor Snelting Zweitgutachter: Prof. Dr.-Ing. Jörg Henkel Betreuender Mitarbeiter: Dipl.-Inform. Andreas Zwinkau Bearbeitungszeit: 5. Oktober 2016 – 23. Januar 2017 KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft www.kit.edu Zusammenfassung/Abstract Jump Threading (dt. „Sprünge fädeln“) ist eine Compileroptimierung, die statisch vorhersagbare bedingte Sprünge in unbedingte Sprünge umwandelt. Bei der Ausfüh- rung kann ein Prozessor bedingte Sprünge zunächst nur heuristisch mit Hilfe der Sprungvorhersage auswerten. Sie stellen daher generell ein Performancehindernis dar. Die Umwandlung ist insbesondere auch dann möglich, wenn das Sprungziel nur auf einer Teilmenge der zu dem Sprung führenden Ausführungspfade statisch be- stimmbar ist. In diesem Fall, der den überwiegenden Teil der durch Jump Threading optimierten Sprünge betrifft, muss die Optimierung Grundblöcke duplizieren, um jene Ausführungspfade zu isolieren. Verschiedene aktuelle Compiler enthalten sehr unterschiedliche Implementierungen von Jump Threading. In dieser Masterarbeit wird zunächst ein theoretischer Rahmen für Jump Threading vorgestellt. Sodann wird eine allgemeine Fassung eines Jump- Threading-Algorithmus entwickelt, implementiert und in diverser Hinsicht untersucht, insbesondere auf Wechselwirkungen mit anderen Optimierungen wie If -
A Dynamically Recompiling ARM Emulator POWERED
POWERED TACARM A Dynamically Recompiling ARM Emulator 3rd year project report 2000-2001 University of Warwick Written by David Sharp Supervised by Graham Martin 2 Abstract Dynamically recompiling from one machine code to another can be used to emulate modern microprocessors at realistic speeds. This report is a discussion of the techniques used in implementing a dynamically recompiling emulator of an ARM processor for use in an emulation of a complete computer system. Keywords Emulation, Dynamic Recompilation, Binary Translation, Just-In-Time compilation, Virtual Machine, Microprocessor Simulation, Intermediate Code, ARM. 3 Contents 1. Introduction 7 1.1. What is emulation? 7 1.2. Applications of emulation 7 1.3. Processor emulation techniques 9 1.4. This Project 13 2. Analysis 16 2.1. Introduction to the ARM 16 2.2. Identifying the problem 18 2.3. Getting started 20 2.4. The System Design 21 3. Disassembler 23 3.1. Purpose 23 3.2. ARM decoding 23 3.3. Design 24 4. Interpreter 26 4.1. Purpose 26 4.2. The problem with JIT 26 4.3. The HotSpotTM alternative 26 4.4. Quantifying the JIT problem 27 4.5. Faster decoding 28 4.6. Interfaces 29 4.7. The emulation loop 30 4.8. Implementation 31 4.9. Debugging 32 4.10. Compatibility 33 5. Recompilation 35 5.1. Overview 35 5.2. Methods of generating native code 35 5.3. The use of intermediate code 36 6. Armlets – An Intermediate Code 38 6.1. Purpose 38 6.2. The ‘explicit-implicit problem’ 38 6.3. Options 39 6.4. -
A Brief History of Just-In-Time Compilation
A Brief History of Just-In-Time JOHN AYCOCK University of Calgary Software systems have been using “just-in-time” compilation (JIT) techniques since the 1960s. Broadly, JIT compilation includes any translation performed dynamically, after a program has started execution. We examine the motivation behind JIT compilation and constraints imposed on JIT compilation systems, and present a classification scheme for such systems. This classification emerges as we survey forty years of JIT work, from 1960–2000. Categories and Subject Descriptors: D.3.4 [Programming Languages]: Processors; K.2 [History of Computing]: Software General Terms: Languages, Performance Additional Key Words and Phrases: Just-in-time compilation, dynamic compilation 1. INTRODUCTION into a form that is executable on a target platform. Those who cannot remember the past are con- What is translated? The scope and na- demned to repeat it. ture of programming languages that re- George Santayana, 1863–1952 [Bartlett 1992] quire translation into executable form covers a wide spectrum. Traditional pro- This oft-quoted line is all too applicable gramming languages like Ada, C, and in computer science. Ideas are generated, Java are included, as well as little lan- explored, set aside—only to be reinvented guages [Bentley 1988] such as regular years later. Such is the case with what expressions. is now called “just-in-time” (JIT) or dy- Traditionally, there are two approaches namic compilation, which refers to trans- to translation: compilation and interpreta- lation that occurs after a program begins tion. Compilation translates one language execution. into another—C to assembly language, for Strictly speaking, JIT compilation sys- example—with the implication that the tems (“JIT systems” for short) are com- translated form will be more amenable pletely unnecessary. -
Program Dynamic Analysis Overview
4/14/16 Program Dynamic Analysis Overview • Dynamic Analysis • JVM & Java Bytecode [2] • A Java bytecode engineering library: ASM [1] 2 1 4/14/16 What is dynamic analysis? [3] • The investigation of the properties of a running software system over one or more executions 3 Has anyone done dynamic analysis? [3] • Loggers • Debuggers • Profilers • … 4 2 4/14/16 Why dynamic analysis? [3] • Gap between run-time structure and code structure in OO programs Trying to understand one [structure] from the other is like trying to understand the dynamism of living ecosystems from the static taxonomy of plants and animals, and vice-versa. -- Erich Gamma et al., Design Patterns 5 Why dynamic analysis? • Collect runtime execution information – Resource usage, execution profiles • Program comprehension – Find bugs in applications, identify hotspots • Program transformation – Optimize or obfuscate programs – Insert debugging or monitoring code – Modify program behaviors on the fly 6 3 4/14/16 How to do dynamic analysis? • Instrumentation – Modify code or runtime to monitor specific components in a system and collect data – Instrumentation approaches • Source code modification • Byte code modification • VM modification • Data analysis 7 A Running Example • Method call instrumentation – Given a program’s source code, how do you modify the code to record which method is called by main() in what order? public class Test { public static void main(String[] args) { if (args.length == 0) return; if (args.length % 2 == 0) printEven(); else printOdd(); } public -
Transparent Dynamic Optimization: the Design and Implementation of Dynamo
Transparent Dynamic Optimization: The Design and Implementation of Dynamo Vasanth Bala, Evelyn Duesterwald, Sanjeev Banerjia HP Laboratories Cambridge HPL-1999-78 June, 1999 E-mail: [email protected] dynamic Dynamic optimization refers to the runtime optimization optimization, of a native program binary. This report describes the compiler, design and implementation of Dynamo, a prototype trace selection, dynamic optimizer that is capable of optimizing a native binary translation program binary at runtime. Dynamo is a realistic implementation, not a simulation, that is written entirely in user-level software, and runs on a PA-RISC machine under the HPUX operating system. Dynamo does not depend on any special programming language, compiler, operating system or hardware support. Contrary to intuition, we demonstrate that it is possible to use a piece of software to improve the performance of a native, statically optimized program binary, while it is executing. Dynamo not only speeds up real application programs, its performance improvement is often quite significant. For example, the performance of many +O2 optimized SPECint95 binaries running under Dynamo is comparable to the performance of their +O4 optimized version running without Dynamo. Internal Accession Date Only Ó Copyright Hewlett-Packard Company 1999 Contents 1 INTRODUCTION ........................................................................................... 7 2 RELATED WORK ......................................................................................... 9 3 OVERVIEW -
Infrastructures and Compilation Strategies for the Performance of Computing Systems Erven Rohou
Infrastructures and Compilation Strategies for the Performance of Computing Systems Erven Rohou To cite this version: Erven Rohou. Infrastructures and Compilation Strategies for the Performance of Computing Systems. Other [cs.OH]. Universit´ede Rennes 1, 2015. <tel-01237164> HAL Id: tel-01237164 https://hal.inria.fr/tel-01237164 Submitted on 2 Dec 2015 HAL is a multi-disciplinary open access L'archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destin´eeau d´ep^otet `ala diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publi´esou non, lished or not. The documents may come from ´emanant des ´etablissements d'enseignement et de teaching and research institutions in France or recherche fran¸caisou ´etrangers,des laboratoires abroad, or from public or private research centers. publics ou priv´es. Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License HABILITATION À DIRIGER DES RECHERCHES présentée devant L’Université de Rennes 1 Spécialité : informatique par Erven Rohou Infrastructures and Compilation Strategies for the Performance of Computing Systems soutenue le 2 novembre 2015 devant le jury composé de : Mme Corinne Ancourt Rapporteur Prof. Stefano Crespi Reghizzi Rapporteur Prof. Frédéric Pétrot Rapporteur Prof. Cédric Bastoul Examinateur Prof. Olivier Sentieys Président M. André Seznec Examinateur Contents 1 Introduction 3 1.1 Evolution of Hardware . .3 1.1.1 Traditional Evolution . .4 1.1.2 Multicore Era . .4 1.1.3 Amdahl’s Law . .5 1.1.4 Foreseeable Future Architectures . .6 1.1.5 Extremely Complex Micro-architectures . .6 1.2 Evolution of Software Ecosystem . -
Costing JIT Traces
Costing JIT Traces John Magnus Morton, Patrick Maier, and Philip Trinder University of Glasgow [email protected], {Patrick.Maier, Phil.Trinder}@glasgow.ac.uk Abstract. Tracing JIT compilation generates units of compilation that are easy to analyse and are known to execute frequently. The AJITPar project aims to investigate whether the information in JIT traces can be used to make better scheduling decisions or perform code transformations to adapt the code for a specific parallel architecture. To achieve this goal, a cost model must be developed to estimate the execution time of an individual trace. This paper presents the design and implementation of a system for ex- tracting JIT trace information from the Pycket JIT compiler. We define three increasingly parametric cost models for Pycket traces. We perform a search of the cost model parameter space using genetic algorithms to identify the best weightings for those parameters. We test the accuracy of these cost models for predicting the cost of individual traces on a set of loop-based micro-benchmarks. We also compare the accuracy of the cost models for predicting whole program execution time over the Py- cket benchmark suite. Our results show that the weighted cost model using the weightings found from the genetic algorithm search has the best accuracy. 1 Introduction Modern hardware is increasingly multicore, and increasingly, software is required to exhibit decent parallel performance in order to match the hardware’s poten- tial. Writing performant parallel code is non-trivial for a fixed architecture, yet it is much harder if the target architecture is not known in advance, or if the code is meant to be portable across a range of architectures. -
Improving the Compilation Process Using Program Annotations
POLITECNICO DI MILANO Corso di Laurea Magistrale in Ingegneria Informatica Dipartimento di Elettronica, Informazione e Bioingegneria Improving the Compilation process using Program Annotations Relatore: Prof. Giovanni Agosta Correlatore: Prof. Lenore Zuck Tesi di Laurea di: Niko Zarzani, matricola 783452 Anno Accademico 2012-2013 Alla mia famiglia Alla mia ragazza Ai miei amici Ringraziamenti Ringrazio in primis i miei relatori, Prof. Giovanni Agosta e Prof. Lenore Zuck, per la loro disponibilità, i loro preziosi consigli e il loro sostegno. Grazie per avermi seguito sia nel corso della tesi che della mia carriera universitaria. Ringrazio poi tutti coloro con cui ho avuto modo di confrontarmi durante la mia ricerca, Dr. Venkat N. Venkatakrishnan, Dr. Rigel Gjomemo, Dr. Phu H. H. Phung e Giacomo Tagliabure, che mi sono stati accanto sin dall’inizio del mio percorso di tesi. Voglio ringraziare con tutto il cuore la mia famiglia per il prezioso sup- porto in questi anni di studi e Camilla per tutto l’amore che mi ha dato anche nei momenti più critici di questo percorso. Non avrei potuto su- perare questa avventura senza voi al mio fianco. Ringrazio le mie amiche e i miei amici più cari Ilaria, Carolina, Elisa, Riccardo e Marco per la nostra speciale amicizia a distanza e tutte le risate fatte assieme. Infine tutti i miei conquilini, dai più ai meno nerd, per i bei momenti pas- sati assieme. Ricorderò per sempre questi ultimi anni come un’esperienza stupenda che avete reso memorabile. Mi mancherete tutti. Contents 1 Introduction 1 2 Background 3 2.1 Annotated Code . .3 2.2 Sources of annotated code . -
Arxiv:2106.08833V1 [Cs.NI] 16 Jun 2021 Mented on Commodity Servers, Are Widely Adopted in Real Tion Deployments
Dynamic Recompilation of Software Network Services with Morpheus Sebastiano Miano1, Alireza Sanaee1, Fulvio Risso2, Gábor Rétvári3, Gianni Antichi1, 1Queen Mary University of London, UK 2Politecnico di Torino, IT 3MTA-BME Information Systems Research Group & Ericsson Research, HU Abstract that are only known at run time, and take difficult-to-predict branches conditioned on variable data. State-of-the-art approaches to design, develop and optimize Dynamic compilation, in contrast, enables program opti- software packet-processing programs are based on static com- mization based on invariant data computed at run time and pilation: the compiler’s input is a description of the forward- produces code that is specialized to the input the program ing plane semantics and the output is a binary that can accom- is processing [7, 24, 34]. The idea is to continuously collect modate any control plane configuration or input traffic. run-time data about program execution and then re-compile In this paper, we demonstrate that tracking control plane it to improve performance. This is a well-known practice actions and packet-level traffic dynamics at run time opens up adopted by generic programming languages (e.g., Java [24], new opportunities for code specialization. We present Mor- JavaScript [34], and C/C++ [7]) and often produces orders pheus, a system working alongside static compilers that con- of magnitude more efficient code in the context of, e.g., data- tinuously optimizes the targeted networking code. We intro- caching services [67], data mining [21] and databases [51,90]. duce a number of new techniques, from static code analysis to adaptive code instrumentation, and we implement a tool- To our surprise, we found that state-of-the-art dynamic box of domain specific optimizations that are not restricted optimization tools for generic software, including Google’s to a specific data plane framework or programming language. -
Control Flow Graphs
CONTROL FLOW GRAPHS PROGRAM ANALYSIS AND OPTIMIZATION – DCC888 Fernando Magno Quintão Pereira [email protected] The material in these slides have been taken from the "Dragon Book", Secs 8.5 – 8.7, by Aho et al. Intermediate Program Representations • Optimizing compilers and human beings do not see the program in the same way. – We are more interested in source code. – But, source code is too different from machine code. – Besides, from an engineering point of view, it is better to have a common way to represent programs in different languages, and target different architectures. Fortran PowerPC COBOL x86 Front Back Optimizer Lisp End End ARM … … Basic Blocks and Flow Graphs • Usually compilers represent programs as control flow graphs (CFG). • A control flow graph is a directed graph. – Nodes are basic blocks. – There is an edge from basic block B1 to basic block B2 if program execution can flow from B1 to B2. • Before defining basic void identity(int** a, int N) { int i, j; block, we will illustrate for (i = 0; i < N; i++) { this notion by showing the for (j = 0; j < N; j++) { a[i][j] = 0; CFG of the function on the } right. } for (i = 0; i < N; i++) { What does a[i][i] = 1; this program } do? } The Low Level Virtual Machine • We will be working with a compilation framework called The Low Level Virtual Machine, or LLVM, for short. • LLVM is today the most used compiler in research. • Additionally, this compiler is used in many important companies: Apple, Cray, Google, etc. The front-end Machine independent Machine dependent that parses C optimizations, such as optimizations, such into bytecodes constant propagation as register allocation ../0 %"&'( *+, ""% !"#$% !"#$)% !"#$)% !"#$- Using LLVM to visualize a CFG • We can use the opt tool, the LLVM machine independent optimizer, to visualize the control flow graph of a given function $> clang -c -emit-llvm identity.c -o identity.bc $> opt –view-cfg identity.bc • We will be able to see the CFG of our target program, as long as we have the tool DOT installed in our system. -
A Dynamic Optimization Framework for a Java Just-In-Time Compiler
A Dynamic Optimization Framework for a Java Just-in-Time Compiler Toshio $uganuma, Toshiaki Yasue, Motohiro Kawahito, Hideaki Kornatsu, Toshio Nakatani IBM Tokyo Research Laboratory 1623-14 Shimotururna, Yamato-shi, Kanagawa 242-8502, Japan Phone: +81-46-215-4658 Email: {suganuma, yasue, jl25131, komatsu, nakatani}@jp.ibm.com ABSTRACT from the compiled code. Once program hot regions are detected, the The high performance implementation of Java Virtual Machines dynamic compiler must be very aggressive in identifying good op- (JVM) and Just-In-Time (JIT) compilers is directed toward adaptive portunities for optimizations that can achieve higher total perform- compilation optimizations on the basis of online runtime profile in- ance. This tradeoff between compilation overhead and its perform- formation. This paper describes the design and implementation of a ance benefit is a crucial issue for dynamic compilers. dynamic optimization framework in a production-level Java JIT In the above context, the high performance implementation of Java compiler. Our approach is to employ a mixed mode interpreter and Virtual Machines (JVM) and Just-In-Time (JIT) compilers is mov- a three level optimizing compiler, supporting quick, full, and spe- ing toward exploitation of adaptive compilation optimizations on cial optimization, each of which has a different set of tradeoffs be- the basis of runtime profile information. Although there is a long tween compilation overhead and execution speed. A lightweight history of research on mntime feedback-directed optimizations sampling profiler operates continuously during the entire program's (FDO), many of these techniques are not directly applicable for use execution. When necessary, detailed information on runtime behav- in JVMs because of the requirements for programmer intervention.