Design and Implementation of Compiler
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Expression Rematerialization for VLIW DSP Processors with Distributed Register Files ?
Expression Rematerialization for VLIW DSP Processors with Distributed Register Files ? Chung-Ju Wu, Chia-Han Lu, and Jenq-Kuen Lee Department of Computer Science, National Tsing-Hua University, Hsinchu 30013, Taiwan {jasonwu,chlu}@pllab.cs.nthu.edu.tw,[email protected] Abstract. Spill code is the overhead of memory load/store behavior if the available registers are not sufficient to map live ranges during the process of register allocation. Previously, works have been proposed to reduce spill code for the unified register file. For reducing power and cost in design of VLIW DSP processors, distributed register files and multi- bank register architectures are being adopted to eliminate the amount of read/write ports between functional units and registers. This presents new challenges for devising compiler optimization schemes for such ar- chitectures. This paper aims at addressing the issues of reducing spill code via rematerialization for a VLIW DSP processor with distributed register files. Rematerialization is a strategy for register allocator to de- termine if it is cheaper to recompute the value than to use memory load/store. In the paper, we propose a solution to exploit the character- istics of distributed register files where there is the chance to balance or split live ranges. By heuristically estimating register pressure for each register file, we are going to treat them as optional spilled locations rather than spilling to memory. The choice of spilled location might pre- serve an expression result and keep the value alive in different register file. It increases the possibility to do expression rematerialization which is effectively able to reduce spill code. -
User-Directed Loop-Transformations in Clang
User-Directed Loop-Transformations in Clang Michael Kruse Hal Finkel Argonne Leadership Computing Facility Argonne Leadership Computing Facility Argonne National Laboratory Argonne National Laboratory Argonne, USA Argonne, USA [email protected] hfi[email protected] Abstract—Directives for the compiler such as pragmas can Only #pragma unroll has broad support. #pragma ivdep made help programmers to separate an algorithm’s semantics from popular by icc and Cray to help vectorization is mimicked by its optimization. This keeps the code understandable and easier other compilers as well, but with different interpretations of to optimize for different platforms. Simple transformations such as loop unrolling are already implemented in most mainstream its meaning. However, no compiler allows applying multiple compilers. We recently submitted a proposal to add generalized transformations on a single loop systematically. loop transformations to the OpenMP standard. We are also In addition to straightforward trial-and-error execution time working on an implementation in LLVM/Clang/Polly to show its optimization, code transformation pragmas can be useful for feasibility and usefulness. The current prototype allows applying machine-learning assisted autotuning. The OpenMP approach patterns common to matrix-matrix multiplication optimizations. is to make the programmer responsible for the semantic Index Terms—OpenMP, Pragma, Loop Transformation, correctness of the transformation. This unfortunately makes it C/C++, Clang, LLVM, Polly hard for an autotuner which only measures the timing difference without understanding the code. Such an autotuner would I. MOTIVATION therefore likely suggest transformations that make the program Almost all processor time is spent in some kind of loop, and return wrong results or crash. -
Elimination of Memory-Based Dependences For
Elimination of Memory-Based Dependences for Loop-Nest Optimization and Parallelization: Evaluation of a Revised Violated Dependence Analysis Method on a Three-Address Code Polyhedral Compiler Konrad Trifunovic1, Albert Cohen1, Razya Ladelsky2, and Feng Li1 1 INRIA Saclay { ^Ile-de-France and LRI, Paris-Sud 11 University, Orsay, France falbert.cohen, konrad.trifunovic, [email protected] 2 IBM Haifa Research, Haifa, Israel [email protected] Abstract In order to preserve the legality of loop nest transformations and parallelization, data- dependences need to be analyzed. Memory dependences come in two varieties: they are either data-flow dependences or memory-based dependences. While data-flow de- pendences must be satisfied in order to preserve the correct order of computations, memory-based dependences are induced by the reuse of a single memory location to store multiple values. Memory-based dependences reduce the degrees of freedom for loop transformations and parallelization. While systematic array expansion techniques exist to remove all memory-based dependences, the overhead on memory footprint and the detrimental impact on register-level reuse can be catastrophic. Much care is needed when eliminating memory-based dependences, and this is particularly essential for polyhedral compilation on three-address code representation like the GRAPHITE pass of GCC. We propose and evaluate a technique allowing a compiler to ignore some memory- based dependences while checking for the legality of a given affine transformation. This technique does not involve any array expansion. When this technique is not sufficient to expose data parallelism, it can be used to compute the minimal set of scalars and arrays that should be privatized. -
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. -
A General Compilation Algorithm to Parallelize and Optimize Counted Loops with Dynamic Data-Dependent Bounds Jie Zhao, Albert Cohen
A general compilation algorithm to parallelize and optimize counted loops with dynamic data-dependent bounds Jie Zhao, Albert Cohen To cite this version: Jie Zhao, Albert Cohen. A general compilation algorithm to parallelize and optimize counted loops with dynamic data-dependent bounds. IMPACT 2017 - 7th International Workshop on Polyhedral Compilation Techniques, Jan 2017, Stockholm, Sweden. pp.1-10. hal-01657608 HAL Id: hal-01657608 https://hal.inria.fr/hal-01657608 Submitted on 7 Dec 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. A general compilation algorithm to parallelize and optimize counted loops with dynamic data-dependent bounds Jie Zhao Albert Cohen INRIA & DI, École Normale Supérieure 45 rue d’Ulm, 75005 Paris fi[email protected] ABSTRACT iterating until a dynamically computed, data-dependent up- We study the parallelizing compilation and loop nest opti- per bound. Such bounds are loop invariants, but often re- mization of an important class of programs where counted computed in the immediate vicinity of the loop they con- loops have a dynamically computed, data-dependent upper trol; for example, their definition may take place in the im- bound. Such loops are amenable to a wider set of trans- mediately enclosing loop. -
Loop Transformations and Parallelization
Loop transformations and parallelization Claude Tadonki LAL/CNRS/IN2P3 University of Paris-Sud [email protected] December 2010 Claude Tadonki Loop transformations and parallelization C. Tadonki – Loop transformations Introduction Most of the time, the most time consuming part of a program is on loops. Thus, loops optimization is critical in high performance computing. Depending on the target architecture, the goal of loops transformations are: improve data reuse and data locality efficient use of memory hierarchy reducing overheads associated with executing loops instructions pipeline maximize parallelism Loop transformations can be performed at different levels by the programmer, the compiler, or specialized tools. At high level, some well known transformations are commonly considered: loop interchange loop (node) splitting loop unswitching loop reversal loop fusion loop inversion loop skewing loop fission loop vectorization loop blocking loop unrolling loop parallelization Claude Tadonki Loop transformations and parallelization C. Tadonki – Loop transformations Dependence analysis Extract and analyze the dependencies of a computation from its polyhedral model is a fundamental step toward loop optimization or scheduling. Definition For a given variable V and given indexes I1, I2, if the computation of X(I1) requires the value of X(I2), then I1 ¡ I2 is called a dependence vector for variable V . Drawing all the dependence vectors within the computation polytope yields the so-called dependencies diagram. Example The dependence vectors are (1; 0); (0; 1); (¡1; 1). Claude Tadonki Loop transformations and parallelization C. Tadonki – Loop transformations Scheduling Definition The computation on the entire domain of a given loop can be performed following any valid schedule.A timing function tV for variable V yields a valid schedule if and only if t(x) > t(x ¡ d); 8d 2 DV ; (1) where DV is the set of all dependence vectors for variable V . -
Foundations of Scientific Research
2012 FOUNDATIONS OF SCIENTIFIC RESEARCH N. M. Glazunov National Aviation University 25.11.2012 CONTENTS Preface………………………………………………….…………………….….…3 Introduction……………………………………………….…..........................……4 1. General notions about scientific research (SR)……………….……….....……..6 1.1. Scientific method……………………………….………..……..……9 1.2. Basic research…………………………………………...……….…10 1.3. Information supply of scientific research……………..….………..12 2. Ontologies and upper ontologies……………………………….…..…….…….16 2.1. Concepts of Foundations of Research Activities 2.2. Ontology components 2.3. Ontology for the visualization of a lecture 3. Ontologies of object domains………………………………..………………..19 3.1. Elements of the ontology of spaces and symmetries 3.1.1. Concepts of electrodynamics and classical gauge theory 4. Examples of Research Activity………………….……………………………….21 4.1. Scientific activity in arithmetics, informatics and discrete mathematics 4.2. Algebra of logic and functions of the algebra of logic 4.3. Function of the algebra of logic 5. Some Notions of the Theory of Finite and Discrete Sets…………………………25 6. Algebraic Operations and Algebraic Structures……………………….………….26 7. Elements of the Theory of Graphs and Nets…………………………… 42 8. Scientific activity on the example “Information and its investigation”……….55 9. Scientific research in Artificial Intelligence……………………………………..59 10. Compilers and compilation…………………….......................................……69 11. Objective, Concepts and History of Computer security…….………………..93 12. Methodological and categorical apparatus of scientific research……………114 13. Methodology and methods of scientific research…………………………….116 13.1. Methods of theoretical level of research 13.1.1. Induction 13.1.2. Deduction 13.2. Methods of empirical level of research 14. Scientific idea and significance of scientific research………………………..119 15. Forms of scientific knowledge organization and principles of SR………….121 1 15.1. Forms of scientific knowledge 15.2. -
Stack Applications
Applications of Stacks Based on the notes from David Fernandez-Baca and Steve Kautz Bryn Mawr College CS206 Intro to Data Structures App1: Verifying Matched Parentheses • Verifying whether an string of parentheses is well- formed. o “{[(){[]}]()}” -- well-formed o “{[]}[]]()}” -- not well-formed • More precisely, a string γ of parentheses is well- formed if either γ is empty or γ has the form (α)β [α]β or {α}β where α and β are themselves well-formed strings of parentheses. This kind of recursive definition lends itself naturally to a stack-based algorithm. 1 Verifying Matched Parentheses (cont.) • Idea: to check a String like "{[(){[]}]()}", scan it character by character. o When you encounter a lefty— '{', '[', or '(' — push it onto the stack. o When you encounter a righty, pop its counterpart from atop the stack, and check that they match. o If there is a mismatch or exception, or if the stack is not empty when you reach the end of the string, the parentheses are not properly matched. o Detailed code is posted separately. App2: Arithmetic Expressions Infix notation: • Operators are written between the operands they act on; e.g., “2+2”. • Parentheses surrounding groups of operands and operators are used to indicate the intended order in which operations are to be performed. • In the absence of parentheses, precedence rules determine the order of operations. E.g., Because “-” has lower precedence than “*”, the infix expression “3-4*5” is evaluated as “3-(4*5)”, not as “(3-4)*5”. If you want it to evaluate the second way, you need to parenthesize. -
Compiler Optimizations
Compiler Optimizations CS 498: Compiler Optimizations Fall 2006 University of Illinois at Urbana-Champaign A note about the name of optimization • It is a misnomer since there is no guarantee of optimality. • We could call the operation code improvement, but this is not quite true either since compiler transformations are not guaranteed to improve the performance of the generated code. Outline Assignment Statement Optimizations Loop Body Optimizations Procedure Optimizations Register allocation Instruction Scheduling Control Flow Optimizations Cache Optimizations Vectorization and Parallelization Advanced Compiler Design Implementation. Steven S. Muchnick, Morgan and Kaufmann Publishers, 1997. Chapters 12 - 19 Historical origins of compiler optimization "It was our belief that if FORTRAN, during its first months, were to translate any reasonable "scientific" source program into an object program only half as fast as its hand coded counterpart, then acceptance of our system would be in serious danger. This belief caused us to regard the design of the translator as the real challenge, not the simple task of designing the language."... "To this day I believe that our emphasis on object program efficiency rather than on language design was basically correct. I believe that has we failed to produce efficient programs, the widespread use of language like FORTRAN would have been seriously delayed. John Backus FORTRAN I, II, and III Annals of the History of Computing Vol. 1, No 1, July 1979 Compilers are complex systems “Like most of the early hardware and software systems, Fortran was late in delivery, and didn’t really work when it was delivered. At first people thought it would never be done. -
Polyhedral-Model Guided Loop-Nest Auto-Vectorization Konrad Trifunović, Dorit Nuzman, Albert Cohen, Ayal Zaks, Ira Rosen
Polyhedral-Model Guided Loop-Nest Auto-Vectorization Konrad Trifunović, Dorit Nuzman, Albert Cohen, Ayal Zaks, Ira Rosen To cite this version: Konrad Trifunović, Dorit Nuzman, Albert Cohen, Ayal Zaks, Ira Rosen. Polyhedral-Model Guided Loop-Nest Auto-Vectorization. The 18th International Conference on Parallel Architectures and Com- pilation Techniques, Sep 2009, Raleigh, United States. hal-00645325 HAL Id: hal-00645325 https://hal.inria.fr/hal-00645325 Submitted on 27 Nov 2011 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Polyhedral-Model Guided Loop-Nest Auto-Vectorization Konrad Trifunovic †, Dorit Nuzman ∗, Albert Cohen †, Ayal Zaks ∗ and Ira Rosen ∗ ∗IBM Haifa Research Lab, {dorit, zaks, irar}@il.ibm.com †INRIA Saclay, {konrad.trifunovic, albert.cohen }@inria.fr Abstract—Optimizing compilers apply numerous inter- Modern architectures must exploit multiple forms of par- dependent optimizations, leading to the notoriously difficult allelism provided by platforms while using the memory phase-ordering problem — that of deciding which trans- hierarchy efficiently. Systematic solutions to harness the formations to apply and in which order. Fortunately, new infrastructures such as the polyhedral compilation framework interplay of multi-level parallelism and locality are emerg- host a variety of transformations, facilitating the efficient explo- ing, by advances in automatic parallelization and loop nest ration and configuration of multiple transformation sequences. -
Compiler Construction
Compiler construction PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 10 Dec 2011 02:23:02 UTC Contents Articles Introduction 1 Compiler construction 1 Compiler 2 Interpreter 10 History of compiler writing 14 Lexical analysis 22 Lexical analysis 22 Regular expression 26 Regular expression examples 37 Finite-state machine 41 Preprocessor 51 Syntactic analysis 54 Parsing 54 Lookahead 58 Symbol table 61 Abstract syntax 63 Abstract syntax tree 64 Context-free grammar 65 Terminal and nonterminal symbols 77 Left recursion 79 Backus–Naur Form 83 Extended Backus–Naur Form 86 TBNF 91 Top-down parsing 91 Recursive descent parser 93 Tail recursive parser 98 Parsing expression grammar 100 LL parser 106 LR parser 114 Parsing table 123 Simple LR parser 125 Canonical LR parser 127 GLR parser 129 LALR parser 130 Recursive ascent parser 133 Parser combinator 140 Bottom-up parsing 143 Chomsky normal form 148 CYK algorithm 150 Simple precedence grammar 153 Simple precedence parser 154 Operator-precedence grammar 156 Operator-precedence parser 159 Shunting-yard algorithm 163 Chart parser 173 Earley parser 174 The lexer hack 178 Scannerless parsing 180 Semantic analysis 182 Attribute grammar 182 L-attributed grammar 184 LR-attributed grammar 185 S-attributed grammar 185 ECLR-attributed grammar 186 Intermediate language 186 Control flow graph 188 Basic block 190 Call graph 192 Data-flow analysis 195 Use-define chain 201 Live variable analysis 204 Reaching definition 206 Three address -
Study Topics Test 3
Printed by David Whalley Apr 22, 20 9:44 studytopics_test3.txt Page 1/2 Apr 22, 20 9:44 studytopics_test3.txt Page 2/2 Topics to Study for Exam 3 register allocation array padding scalar replacement Chapter 6 loop interchange prefetching intermediate code representations control flow optimizations static versus dynamic type checking branch chaining equivalence of type expressions reversing branches coercions, overloading, and polymorphism code positioning translation of boolean expressions loop inversion backpatching and translation of flow−of−control constructs useless jump elimination translation of record declarations unreachable code elimination translation of switch statements data flow optimizations translation of array references common subexpression elimination partial redundancy elimination dead assignment elimination Chapter 5 evaluation order determination recurrence elimination syntax directed definitions machine specific optimizations synthesized and inherited attributes instruction scheduling dependency graphs filling delay slots L−attributed definitions exploiting instruction−level parallelism translation schemes peephole optimization syntax−directed construction of syntax trees and DAGs constructing a control flow graph syntax−directed translation with YACC optimizations before and after code generation instruction selection optimization Chapter 7 Chapter 10 activation records call graphs instruction pipelining typical actions during calls and returns data dependences storage allocation strategies true dependences static,