19770010812.Pdf
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Efficient Computation of LALR(1) Look-Ahead Sets
Efficient Computation of LALR(1) Look-Ahead Sets FRANK DeREMER and THOMAS PENNELLO University of California, Santa Cruz, and MetaWare TM Incorporated Two relations that capture the essential structure of the problem of computing LALR(1) look-ahead sets are defined, and an efficient algorithm is presented to compute the sets in time linear in the size of the relations. In particular, for a PASCAL grammar, the algorithm performs fewer than 15 percent of the set unions performed by the popular compiler-compiler YACC. When a grammar is not LALR(1), the relations, represented explicitly, provide for printing user- oriented error messages that specifically indicate how the look-ahead problem arose. In addition, certain loops in the digraphs induced by these relations indicate that the grammar is not LR(k) for any k. Finally, an oft-discovered and used but incorrect look-ahead set algorithm is similarly based on two other relations defined for the fwst time here. The formal presentation of this algorithm should help prevent its rediscovery. Categories and Subject Descriptors: D.3.1 [Programming Languages]: Formal Definitions and Theory--syntax; D.3.4 [Programming Languages]: Processors--translator writing systems and compiler generators; F.4.2 [Mathematical Logic and Formal Languages]: Grammars and Other Rewriting Systems--parsing General Terms: Algorithms, Languages, Theory Additional Key Words and Phrases: Context-free grammar, Backus-Naur form, strongly connected component, LALR(1), LR(k), grammar debugging, 1. INTRODUCTION Since the invention of LALR(1) grammars by DeRemer [9], LALR grammar analysis and parsing techniques have been popular as a component of translator writing systems and compiler-compilers. -
9. Optimization
9. Optimization Marcus Denker Optimization Roadmap > Introduction > Optimizations in the Back-end > The Optimizer > SSA Optimizations > Advanced Optimizations © Marcus Denker 2 Optimization Roadmap > Introduction > Optimizations in the Back-end > The Optimizer > SSA Optimizations > Advanced Optimizations © Marcus Denker 3 Optimization Optimization: The Idea > Transform the program to improve efficiency > Performance: faster execution > Size: smaller executable, smaller memory footprint Tradeoffs: 1) Performance vs. Size 2) Compilation speed and memory © Marcus Denker 4 Optimization No Magic Bullet! > There is no perfect optimizer > Example: optimize for simplicity Opt(P): Smallest Program Q: Program with no output, does not stop Opt(Q)? © Marcus Denker 5 Optimization No Magic Bullet! > There is no perfect optimizer > Example: optimize for simplicity Opt(P): Smallest Program Q: Program with no output, does not stop Opt(Q)? L1 goto L1 © Marcus Denker 6 Optimization No Magic Bullet! > There is no perfect optimizer > Example: optimize for simplicity Opt(P): Smallest ProgramQ: Program with no output, does not stop Opt(Q)? L1 goto L1 Halting problem! © Marcus Denker 7 Optimization Another way to look at it... > Rice (1953): For every compiler there is a modified compiler that generates shorter code. > Proof: Assume there is a compiler U that generates the shortest optimized program Opt(P) for all P. — Assume P to be a program that does not stop and has no output — Opt(P) will be L1 goto L1 — Halting problem. Thus: U does not exist. > There will -
SAKI: a Fortran Program for Generalized Linear Inversion of Gravity and Magnetic Profiles
UNITED STATES DEPARTMENT OF THE INTERIOR GEOLOGICAL SURVEY SAKI: A Fortran program for generalized linear Inversion of gravity and magnetic profiles by Michael Webring Open File Report 85-122 1985 This report is preliminary and has not been reviewed for conformity with U.S. Geological Survey editorial standards. Table of Contents Abstract ------------------------------ 1 Introduction ---------------------------- 1 Theory ------------------------------- 2 Users Guide ---------------------------- 7 Model File -------------------------- 7 Observed Data Files ---------------------- 9 Command File ------------------------- 10 Data File Parameters ------------------- 10 Magnetic Parameters ------------------- 10 General Parameters -------------------- H Plotting Parameters ------------------- n Annotation Parameters ------------------ 12 Program Interactive Commands ----------------- 13 Inversion Function ---------------------- 16 Examples ------------------------------ 20 References ----------------------------- 26 Appendix A - plot system description ---------------- 27 Appendix B - program listing -------------------- 30 Abstract A gravity and magnetic inversion program is presented which models a structural cross-section as an ensemble of 2-d prisms formed by linking vertices into a network that may be deformed by a modified Marquardt algorithm. The nonuniqueness of this general model is constrained by the user in an interactive mode. Introduction This program fits in a least-squares sense the theoretical gravity and magnetic response of -
Value Numbering
SOFTWARE—PRACTICE AND EXPERIENCE, VOL. 0(0), 1–18 (MONTH 1900) Value Numbering PRESTON BRIGGS Tera Computer Company, 2815 Eastlake Avenue East, Seattle, WA 98102 AND KEITH D. COOPER L. TAYLOR SIMPSON Rice University, 6100 Main Street, Mail Stop 41, Houston, TX 77005 SUMMARY Value numbering is a compiler-based program analysis method that allows redundant computations to be removed. This paper compares hash-based approaches derived from the classic local algorithm1 with partitioning approaches based on the work of Alpern, Wegman, and Zadeck2. Historically, the hash-based algorithm has been applied to single basic blocks or extended basic blocks. We have improved the technique to operate over the routine’s dominator tree. The partitioning approach partitions the values in the routine into congruence classes and removes computations when one congruent value dominates another. We have extended this technique to remove computations that define a value in the set of available expressions (AVA IL )3. Also, we are able to apply a version of Morel and Renvoise’s partial redundancy elimination4 to remove even more redundancies. The paper presents a series of hash-based algorithms and a series of refinements to the partitioning technique. Within each series, it can be proved that each method discovers at least as many redundancies as its predecessors. Unfortunately, no such relationship exists between the hash-based and global techniques. On some programs, the hash-based techniques eliminate more redundancies than the partitioning techniques, while on others, partitioning wins. We experimentally compare the improvements made by these techniques when applied to real programs. These results will be useful for commercial compiler writers who wish to assess the potential impact of each technique before implementation. -
Language and Compiler Support for Dynamic Code Generation by Massimiliano A
Language and Compiler Support for Dynamic Code Generation by Massimiliano A. Poletto S.B., Massachusetts Institute of Technology (1995) M.Eng., Massachusetts Institute of Technology (1995) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 1999 © Massachusetts Institute of Technology 1999. All rights reserved. A u th or ............................................................................ Department of Electrical Engineering and Computer Science June 23, 1999 Certified by...............,. ...*V .,., . .* N . .. .*. *.* . -. *.... M. Frans Kaashoek Associate Pro essor of Electrical Engineering and Computer Science Thesis Supervisor A ccepted by ................ ..... ............ ............................. Arthur C. Smith Chairman, Departmental CommitteA on Graduate Students me 2 Language and Compiler Support for Dynamic Code Generation by Massimiliano A. Poletto Submitted to the Department of Electrical Engineering and Computer Science on June 23, 1999, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Abstract Dynamic code generation, also called run-time code generation or dynamic compilation, is the cre- ation of executable code for an application while that application is running. Dynamic compilation can significantly improve the performance of software by giving the compiler access to run-time infor- mation that is not available to a traditional static compiler. A well-designed programming interface to dynamic compilation can also simplify the creation of important classes of computer programs. Until recently, however, no system combined efficient dynamic generation of high-performance code with a powerful and portable language interface. This thesis describes a system that meets these requirements, and discusses several applications of dynamic compilation. -
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 -
Optimizing for Reduced Code Space Using Genetic Algorithms
Optimizing for Reduced Code Space using Genetic Algorithms Keith D. Cooper, Philip J. Schielke, and Devika Subramanian Department of Computer Science Rice University Houston, Texas, USA {keith | phisch | devika}@cs.rice.edu Abstract is virtually impossible to select the best set of optimizations to run on a particular piece of code. Historically, compiler Code space is a critical issue facing designers of software for embedded systems. Many traditional compiler optimiza- writers have made one of two assumptions. Either a fixed tions are designed to reduce the execution time of compiled optimization order is “good enough” for all programs, or code, but not necessarily the size of the compiled code. Fur- giving the user a large set of flags that control optimization ther, different results can be achieved by running some opti- is sufficient, because it shifts the burden onto the user. mizations more than once and changing the order in which optimizations are applied. Register allocation only com- Interplay between optimizations occurs frequently. A plicates matters, as the interactions between different op- transformation can create opportunities for other transfor- timizations can cause more spill code to be generated. The mations. Similarly, a transformation can eliminate oppor- compiler for embedded systems, then, must take care to use tunities for other transformations. These interactions also the best sequence of optimizations to minimize code space. Since much of the code for embedded systems is compiled depend on the program being compiled, and they are of- once and then burned into ROM, the software designer will ten difficult to predict. Multiple applications of the same often tolerate much longer compile times in the hope of re- transformation at different points in the optimization se- ducing the size of the compiled code. -
Michigan Terminal System (MTS) Distribution 3.0 Documentation
D3.NOTES 1 M T S D I S T R I B U T I O N 3.0 August 1973 General Notes Distribution 3.0 consists of 6 tapes for the 1600 bpi copy or 9 tapes for the 800 bpi copy. All tapes are unlabeled to save space. The last tape of each set is a dump/restore tape, designed to be used to get started (for new installations) or for conversion (for old installations). Instructions for these procedures are given in items 1001 and 1002 of the documentation list (later in this writeup). The remaining tapes are "Distribution Utility" tapes containing source, object, command, data, and print files, designed to be read by *FS or by the Distribution program, which is a superset of *FS. A writeup for this distribution version of *FS is enclosed (item 1003). Throughout the documentation for this distribution, reference is made to the components of the distribution. Generally these references are made by the 3-digit component number, sometimes followed by a slash and a 1- or 2-digit sub- component number. For example, the G assembler (*ASMG) has been assigned component number 066. However, the assembler actually has many "pieces" and so the assembler consists of components 066/1 through 066/60. Component numbers 001 through 481 are compatible with the numbers used in distribution 2.0; numbers above 481 are new components, or in some cases, old components which have been grouped under a new number. Two versions of the distribution driver file are provided: 461/11 for the 1600 bpi tapes, and 461/16 for the 800 bpi tapes. -
Pearl: Diagrams for Composing Compilers
Pearl: Diagrams for Composing Compilers John Wickerson Imperial College London, UK [email protected] Paul Brunet University College London, UK [email protected] Abstract T-diagrams (or ‘tombstone diagrams’) are widely used in teaching for explaining how compilers and interpreters can be composed together to build and execute software. In this Pearl, we revisit these diagrams, and show how they can be redesigned for better readability. We demonstrate how they can be applied to explain compiler concepts including bootstrapping and cross-compilation. We provide a formal semantics for our redesigned diagrams, based on binary trees. Finally, we suggest how our diagrams could be used to analyse the performance of a compilation system. 2012 ACM Subject Classification Software and its engineering → Compilers Keywords and phrases compiler networks, graphical languages, pedagogy Digital Object Identifier 10.4230/LIPIcs.ECOOP.2020.1 1 Introduction In introductory courses on compilers across the globe, students are taught about the inter- actions between compilers using ‘tombstone diagrams’ or ‘T-diagrams’. In this formalism, a compiler is represented as a ‘T-piece’. A T-piece, as shown in Fig. 1, is characterised by three labels: the source language of the compiler, the target language of the compiler, and the language in which the compiler is implemented. Complex networks of compilers can be represented by composing these basic pieces in two ways. Horizontal composition means that the output of the first compiler is fed in as the input to the second. Diagonal composition means that the first compiler is itself compiled using the second compiler. -
Peephole Optimization from Wikipedia, the Free Encyclopedia
Peephole optimization From Wikipedia, the free encyclopedia In compiler theory, peephole optimization is a kind of optimization performed over a very small set of instructions in a segment of generated code. The set is called a "peephole" or a "window". It works by recognising sets of instructions that can be replaced by shorter or faster sets of instructions. Contents 1 Replacement rules 2 Examples 2.1 Replacing slow instructions with faster ones 2.2 Removing redundant code 2.3 Removing redundant stack instructions 3 Implementation 4 See also 5 References 6 External links Replacement rules Common techniques applied in peephole optimization:[1] Constant folding – Evaluate constant subexpressions in advance. Strength reduction – Replace slow operations with faster equivalents. Null sequences – Delete useless operations. Combine operations – Replace several operations with one equivalent. Algebraic laws – Use algebraic laws to simplify or reorder instructions. Special case instructions – Use instructions designed for special operand cases. Address mode operations – Use address modes to simplify code. There can, of course, be other types of peephole optimizations involving simplifying the target machine instructions, assuming that the target machine is known in advance. Advantages of a given architecture and instruction sets can be exploited, and disadvantages avoided in this case. Examples Replacing slow instructions with faster ones The following Java bytecode ... aload 1 aload 1 mul ... can be replaced by ... aload 1 dup mul ... This kind of optimization, like most peephole optimizations, makes certain assumptions about the efficiency of instructions. For instance, in this case, it is assumed that the dup operation (which duplicates and pushes the top of the stack) is more efficient than the aload X operation (which loads a local variable identified as X and pushes it on the stack). -
MTS on Wikipedia Snapshot Taken 9 January 2011
MTS on Wikipedia Snapshot taken 9 January 2011 PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sun, 09 Jan 2011 13:08:01 UTC Contents Articles Michigan Terminal System 1 MTS system architecture 17 IBM System/360 Model 67 40 MAD programming language 46 UBC PLUS 55 Micro DBMS 57 Bruce Arden 58 Bernard Galler 59 TSS/360 60 References Article Sources and Contributors 64 Image Sources, Licenses and Contributors 65 Article Licenses License 66 Michigan Terminal System 1 Michigan Terminal System The MTS welcome screen as seen through a 3270 terminal emulator. Company / developer University of Michigan and 7 other universities in the U.S., Canada, and the UK Programmed in various languages, mostly 360/370 Assembler Working state Historic Initial release 1967 Latest stable release 6.0 / 1988 (final) Available language(s) English Available programming Assembler, FORTRAN, PL/I, PLUS, ALGOL W, Pascal, C, LISP, SNOBOL4, COBOL, PL360, languages(s) MAD/I, GOM (Good Old Mad), APL, and many more Supported platforms IBM S/360-67, IBM S/370 and successors History of IBM mainframe operating systems On early mainframe computers: • GM OS & GM-NAA I/O 1955 • BESYS 1957 • UMES 1958 • SOS 1959 • IBSYS 1960 • CTSS 1961 On S/360 and successors: • BOS/360 1965 • TOS/360 1965 • TSS/360 1967 • MTS 1967 • ORVYL 1967 • MUSIC 1972 • MUSIC/SP 1985 • DOS/360 and successors 1966 • DOS/VS 1972 • DOS/VSE 1980s • VSE/SP late 1980s • VSE/ESA 1991 • z/VSE 2005 Michigan Terminal System 2 • OS/360 and successors -
Die Gruncllehren Cler Mathematischen Wissenschaften
Die Gruncllehren cler mathematischen Wissenschaften in Einzeldarstellungen mit besonderer Beriicksichtigung der Anwendungsgebiete Band 135 Herausgegeben von J. L. Doob . E. Heinz· F. Hirzebruch . E. Hopf . H. Hopf W. Maak . S. Mac Lane • W. Magnus. D. Mumford M. M. Postnikov . F. K. Schmidt· D. S. Scott· K. Stein Geschiiftsfiihrende Herausgeber B. Eckmann und B. L. van der Waerden Handbook for Automatic Computation Edited by F. L. Bauer· A. S. Householder· F. W. J. Olver H. Rutishauser . K. Samelson· E. Stiefel Volume I . Part a Heinz Rutishauser Description of ALGOL 60 Springer-Verlag New York Inc. 1967 Prof. Dr. H. Rutishauser Eidgenossische Technische Hochschule Zurich Geschaftsfuhrende Herausgeber: Prof. Dr. B. Eckmann Eidgenossische Tecbnische Hocbscbule Zurich Prof. Dr. B. L. van der Waerden Matbematisches Institut der Universitat ZUrich Aile Rechte, insbesondere das der Obersetzung in fremde Spracben, vorbebalten Ohne ausdriickliche Genehmigung des Verlages ist es auch nicht gestattet, dieses Buch oder Teile daraus auf photomechaniscbem Wege (Photokopie, Mikrokopie) oder auf andere Art zu vervielfaltigen ISBN-13: 978-3-642-86936-5 e-ISBN-13: 978-3-642-86934-1 DOl: 10.1007/978-3-642-86934-1 © by Springer-Verlag Berlin· Heidelberg 1967 Softcover reprint of the hardcover 1st edition 1%7 Library of Congress Catalog Card Number 67-13537 Titel-Nr. 5l1B Preface Automatic computing has undergone drastic changes since the pioneering days of the early Fifties, one of the most obvious being that today the majority of computer programs are no longer written in machine code but in some programming language like FORTRAN or ALGOL. However, as desirable as the time-saving achieved in this way may be, still a high proportion of the preparatory work must be attributed to activities such as error estimates, stability investigations and the like, and for these no programming aid whatsoever can be of help.