The Effects of the Precision of Pointer Analysis *
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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. -
Equality Saturation: a New Approach to Optimization
Logical Methods in Computer Science Vol. 7 (1:10) 2011, pp. 1–37 Submitted Oct. 12, 2009 www.lmcs-online.org Published Mar. 28, 2011 EQUALITY SATURATION: A NEW APPROACH TO OPTIMIZATION ROSS TATE, MICHAEL STEPP, ZACHARY TATLOCK, AND SORIN LERNER Department of Computer Science and Engineering, University of California, San Diego e-mail address: {rtate,mstepp,ztatlock,lerner}@cs.ucsd.edu Abstract. Optimizations in a traditional compiler are applied sequentially, with each optimization destructively modifying the program to produce a transformed program that is then passed to the next optimization. We present a new approach for structuring the optimization phase of a compiler. In our approach, optimizations take the form of equality analyses that add equality information to a common intermediate representation. The op- timizer works by repeatedly applying these analyses to infer equivalences between program fragments, thus saturating the intermediate representation with equalities. Once saturated, the intermediate representation encodes multiple optimized versions of the input program. At this point, a profitability heuristic picks the final optimized program from the various programs represented in the saturated representation. Our proposed way of structuring optimizers has a variety of benefits over previous approaches: our approach obviates the need to worry about optimization ordering, enables the use of a global optimization heuris- tic that selects among fully optimized programs, and can be used to perform translation validation, even on compilers other than our own. We present our approach, formalize it, and describe our choice of intermediate representation. We also present experimental results showing that our approach is practical in terms of time and space overhead, is effective at discovering intricate optimization opportunities, and is effective at performing translation validation for a realistic optimizer. -
A Formally-Verified Alias Analysis
A Formally-Verified Alias Analysis Valentin Robert1;2 and Xavier Leroy1 1 INRIA Paris-Rocquencourt 2 University of California, San Diego [email protected], [email protected] Abstract. This paper reports on the formalization and proof of sound- ness, using the Coq proof assistant, of an alias analysis: a static analysis that approximates the flow of pointer values. The alias analysis con- sidered is of the points-to kind and is intraprocedural, flow-sensitive, field-sensitive, and untyped. Its soundness proof follows the general style of abstract interpretation. The analysis is designed to fit in the Comp- Cert C verified compiler, supporting future aggressive optimizations over memory accesses. 1 Introduction Alias analysis. Most imperative programming languages feature pointers, or object references, as first-class values. With pointers and object references comes the possibility of aliasing: two syntactically-distinct program variables, or two semantically-distinct object fields can contain identical pointers referencing the same shared piece of data. The possibility of aliasing increases the expressiveness of the language, en- abling programmers to implement mutable data structures with sharing; how- ever, it also complicates tremendously formal reasoning about programs, as well as optimizing compilation. In this paper, we focus on optimizing compilation in the presence of pointers and aliasing. Consider, for example, the following C program fragment: ... *p = 1; *q = 2; x = *p + 3; ... Performance would be increased if the compiler propagates the constant 1 stored in p to its use in *p + 3, obtaining ... *p = 1; *q = 2; x = 4; ... This optimization, however, is unsound if p and q can alias. -
Control Flow Analysis in Scheme
Control Flow Analysis in Scheme Olin Shivers Carnegie Mellon University [email protected] Abstract Fortran). Representative texts describing these techniques are [Dragon], and in more detail, [Hecht]. Flow analysis is perhaps Traditional ¯ow analysis techniques, such as the ones typically the chief tool in the optimising compiler writer's bag of tricks; employed by optimising Fortran compilers, do not work for an incomplete list of the problems that can be addressed with Scheme-like languages. This paper presents a ¯ow analysis ¯ow analysis includes global constant subexpression elimina- technique Ð control ¯ow analysis Ð which is applicable to tion, loop invariant detection, redundant assignment detection, Scheme-like languages. As a demonstration application, the dead code elimination, constant propagation, range analysis, information gathered by control ¯ow analysis is used to per- code hoisting, induction variable elimination, copy propaga- form a traditional ¯ow analysis problem, induction variable tion, live variable analysis, loop unrolling, and loop jamming. elimination. Extensions and limitations are discussed. However, these traditional ¯ow analysis techniques have The techniques presented in this paper are backed up by never successfully been applied to the Lisp family of computer working code. They are applicable not only to Scheme, but languages. This is a curious omission. The Lisp community also to related languages, such as Common Lisp and ML. has had suf®cient time to consider the problem. Flow analysis dates back at least to 1960, ([Dragon], pp. 516), and Lisp is 1 The Task one of the oldest computer programming languages currently in use, rivalled only by Fortran and COBOL. Flow analysis is a traditional optimising compiler technique Indeed, the Lisp community has long been concerned with for determining useful information about a program at compile the execution speed of their programs. -
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. -
Practical and Accurate Low-Level Pointer Analysis
Practical and Accurate Low-Level Pointer Analysis Bolei Guo Matthew J. Bridges Spyridon Triantafyllis Guilherme Ottoni Easwaran Raman David I. August Department of Computer Science, Princeton University {bguo, mbridges, strianta, ottoni, eraman, august}@princeton.edu Abstract High−Level IR Low−Level IR Pointer SuperBlock .... Inlining Opti Scheduling .... Analysis Formation Pointer analysis is traditionally performed once, early Source Machine Code Code in the compilation process, upon an intermediate repre- Lowering sentation (IR) with source-code semantics. However, per- forming pointer analysis only once at this level imposes a Figure 1. Traditional compiler organization phase-ordering constraint, causing alias information to be- char A[10],B[10],C[10]; . come stale after subsequent code transformations. More- foo() { int i; over, high-level pointer analysis cannot be used at link time char *p; or run time, where the source code is unavailable. for (i=0;i<10;i++) { if (...) 1: p = A 2: p = B 1: p = A 2: p = B This paper advocates performing pointer analysis on a 1: p = A; 3': C[i] = p[i] 3: C[i] = p[i] else low-level intermediate representation. We present the first 2: p = B; 4': A[i] = ... 4: A[i] = ... 3: C[i] = p[i]; context-sensitive and partially flow-sensitive points-to anal- 4: A[i] = ...; 3: C[i] = p[i] } 4: A[i] = ... ysis designed to operate at the assembly level. As we will } demonstrate, low-level pointer analysis can be as accurate (a) Source code (b) Source code CFG (c) Transformed code CFG as high-level analysis. Additionally, our low-level pointer analysis also enables a quantitative comparison of prop- agating high-level pointer analysis results through subse- Figure 2. -
Strength Reduction of Induction Variables and Pointer Analysis – Induction Variable Elimination
Loop optimizations • Optimize loops – Loop invariant code motion [last time] Loop Optimizations – Strength reduction of induction variables and Pointer Analysis – Induction variable elimination CS 412/413 Spring 2008 Introduction to Compilers 1 CS 412/413 Spring 2008 Introduction to Compilers 2 Strength Reduction Induction Variables • Basic idea: replace expensive operations (multiplications) with • An induction variable is a variable in a loop, cheaper ones (additions) in definitions of induction variables whose value is a function of the loop iteration s = 3*i+1; number v = f(i) while (i<10) { while (i<10) { j = 3*i+1; //<i,3,1> j = s; • In compilers, this a linear function: a[j] = a[j] –2; a[j] = a[j] –2; i = i+2; i = i+2; f(i) = c*i + d } s= s+6; } •Observation:linear combinations of linear • Benefit: cheaper to compute s = s+6 than j = 3*i functions are linear functions – s = s+6 requires an addition – Consequence: linear combinations of induction – j = 3*i requires a multiplication variables are induction variables CS 412/413 Spring 2008 Introduction to Compilers 3 CS 412/413 Spring 2008 Introduction to Compilers 4 1 Families of Induction Variables Representation • Basic induction variable: a variable whose only definition in the • Representation of induction variables in family i by triples: loop body is of the form – Denote basic induction variable i by <i, 1, 0> i = i + c – Denote induction variable k=i*a+b by triple <i, a, b> where c is a loop-invariant value • Derived induction variables: Each basic induction variable i defines -
Dead Code Elimination Based Pointer Analysis for Multithreaded Programs
Journal of the Egyptian Mathematical Society (2012) 20, 28–37 Egyptian Mathematical Society Journal of the Egyptian Mathematical Society www.etms-eg.org www.elsevier.com/locate/joems ORIGINAL ARTICLE Dead code elimination based pointer analysis for multithreaded programs Mohamed A. El-Zawawy Department of Mathematics, Faculty of Science, Cairo University, Giza 12316, Egypt Available online 2 February 2012 Abstract This paper presents a new approach for optimizing multitheaded programs with pointer constructs. The approach has applications in the area of certified code (proof-carrying code) where a justification or a proof for the correctness of each optimization is required. The optimization meant here is that of dead code elimination. Towards optimizing multithreaded programs the paper presents a new operational semantics for parallel constructs like join-fork constructs, parallel loops, and conditionally spawned threads. The paper also presents a novel type system for flow-sensitive pointer analysis of multithreaded pro- grams. This type system is extended to obtain a new type system for live-variables analysis of mul- tithreaded programs. The live-variables type system is extended to build the third novel type system, proposed in this paper, which carries the optimization of dead code elimination. The justification mentioned above takes the form of type derivation in our approach. ª 2011 Egyptian Mathematical Society. Production and hosting by Elsevier B.V. All rights reserved. 1. Introduction (a) concealing suspension caused by some commands, (b) mak- ing it easier to build huge software systems, (c) improving exe- One of the mainstream programming approaches today is mul- cution of programs specially those that are executed on tithreading. -
CSE 401/M501 – Compilers
CSE 401/M501 – Compilers Dataflow Analysis Hal Perkins Autumn 2018 UW CSE 401/M501 Autumn 2018 O-1 Agenda • Dataflow analysis: a framework and algorithm for many common compiler analyses • Initial example: dataflow analysis for common subexpression elimination • Other analysis problems that work in the same framework • Some of these are the same optimizations we’ve seen, but more formally and with details UW CSE 401/M501 Autumn 2018 O-2 Common Subexpression Elimination • Goal: use dataflow analysis to A find common subexpressions m = a + b n = a + b • Idea: calculate available expressions at beginning of B C p = c + d q = a + b each basic block r = c + d r = c + d • Avoid re-evaluation of an D E available expression – use a e = b + 18 e = a + 17 copy operation s = a + b t = c + d u = e + f u = e + f – Simple inside a single block; more complex dataflow analysis used F across bocks v = a + b w = c + d x = e + f G y = a + b z = c + d UW CSE 401/M501 Autumn 2018 O-3 “Available” and Other Terms • An expression e is defined at point p in the CFG if its value a+b is computed at p defined t1 = a + b – Sometimes called definition site ... • An expression e is killed at point p if one of its operands a+b is defined at p available t10 = a + b – Sometimes called kill site … • An expression e is available at point p if every path a+b leading to p contains a prior killed b = 7 definition of e and e is not … killed between that definition and p UW CSE 401/M501 Autumn 2018 O-4 Available Expression Sets • To compute available expressions, for each block -
Fast Online Pointer Analysis
Fast Online Pointer Analysis MARTIN HIRZEL IBM Research DANIEL VON DINCKLAGE and AMER DIWAN University of Colorado and MICHAEL HIND IBM Research Pointer analysis benefits many useful clients, such as compiler optimizations and bug finding tools. Unfortunately, common programming language features such as dynamic loading, reflection, and foreign language interfaces, make pointer analysis difficult. This article describes how to deal with these features by performing pointer analysis online during program execution. For example, dynamic loading may load code that is not available for analysis before the program starts. Only an online analysis can analyze such code, and thus support clients that optimize or find bugs in it. This article identifies all problems in performing Andersen’s pointer analysis for the full Java language, presents solutions to these problems, and uses a full implementation of the solutions in a Java virtual machine for validation and performance evaluation. Our analysis is fast: On average over our benchmark suite, if the analysis recomputes points-to results upon each program change, most analysis pauses take under 0.1 seconds, and add up to 64.5 seconds. Categories and Subject Descriptors: D.3.4 [Programming Languages]: Processors—Compilers General Terms: Algorithms, Languages Additional Key Words and Phrases: Pointer analysis, class loading, reflection, native interface ACM Reference Format: Hirzel, M., von Dincklage, D., Diwan, A., and Hind, M. 2007. Fast online pointer analysis. ACM Trans. Program. Lang. Syst. 29, 2, Article 11 (April 2007), 55 pages. DOI = 10.1145/1216374. 1216379 http://doi.acm.org/10.1145/1216374.1216379. A preliminary version of parts of this article appeared in the European Conference on Object- Oriented Programming 2004. -
Hybrid Analysis of Memory References And
HYBRID ANALYSIS OF MEMORY REFERENCES AND ITS APPLICATION TO AUTOMATIC PARALLELIZATION A Dissertation by SILVIUS VASILE RUS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY December 2006 Major Subject: Computer Science HYBRID ANALYSIS OF MEMORY REFERENCES AND ITS APPLICATION TO AUTOMATIC PARALLELIZATION A Dissertation by SILVIUS VASILE RUS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Approved by: Chair of Committee, Lawrence Rauchwerger Committee Members, Nancy Amato Narasimha Reddy Vivek Sarin Head of Department, Valerie Taylor December 2006 Major Subject: Computer Science iii ABSTRACT Hybrid Analysis of Memory References and Its Application to Automatic Parallelization. (December 2006) Silvius Vasile Rus, B.S., Babes-Bolyai University Chair of Advisory Committee: Dr. Lawrence Rauchwerger Executing sequential code in parallel on a multithreaded machine has been an elusive goal of the academic and industrial research communities for many years. It has recently become more important due to the widespread introduction of multi- cores in PCs. Automatic multithreading has not been achieved because classic, static compiler analysis was not powerful enough and program behavior was found to be, in many cases, input dependent. Speculative thread level parallelization was a welcome avenue for advancing parallelization coverage but its performance was not always op- timal due to the sometimes unnecessary overhead of checking every dynamic memory reference. In this dissertation we introduce a novel analysis technique, Hybrid Analysis, which unifies static and dynamic memory reference techniques into a seamless com- piler framework which extracts almost maximum available parallelism from scientific codes and incurs close to the minimum necessary run time overhead. -
Optimization Compiler Passes Optimizations the Role of The
Analysis Synthesis of input program of output program Compiler (front -end) (back -end) character Passes stream Intermediate Lexical Analysis Code Generation token intermediate Optimization stream form Syntactic Analysis Optimization abstract intermediate Before and after generating machine syntax tree form code, devote one or more passes over the program to “improve” code quality Semantic Analysis Code Generation annotated target AST language 2 The Role of the Optimizer Optimizations • The compiler can implement a procedure in many ways • The optimizer tries to find an implementation that is “better” – Speed, code size, data space, … Identify inefficiencies in intermediate or target code Replace with equivalent but better sequences To accomplish this, it • Analyzes the code to derive knowledge about run-time • equivalent = "has the same externally visible behavior – Data-flow analysis, pointer disambiguation, … behavior" – General term is “static analysis” Target-independent optimizations best done on IL • Uses that knowledge in an attempt to improve the code – Literally hundreds of transformations have been proposed code – Large amount of overlap between them Target-dependent optimizations best done on Nothing “optimal” about optimization target code • Proofs of optimality assume restrictive & unrealistic conditions • Better goal is to “usually improve” 3 4 Traditional Three-pass Compiler The Optimizer (or Middle End) IR Source Front Middle IR Back Machine IROpt IROpt IROpt ... IROpt IR Code End End End code 1 2 3 n Errors Errors Modern