A Case for an SC-Preserving Compiler
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CSE 582 – Compilers
CSE P 501 – Compilers Optimizing Transformations Hal Perkins Autumn 2011 11/8/2011 © 2002-11 Hal Perkins & UW CSE S-1 Agenda A sampler of typical optimizing transformations Mostly a teaser for later, particularly once we’ve looked at analyzing loops 11/8/2011 © 2002-11 Hal Perkins & UW CSE S-2 Role of Transformations Data-flow analysis discovers opportunities for code improvement Compiler must rewrite the code (IR) to realize these improvements A transformation may reveal additional opportunities for further analysis & transformation May also block opportunities by obscuring information 11/8/2011 © 2002-11 Hal Perkins & UW CSE S-3 Organizing Transformations in a Compiler Typically middle end consists of many individual transformations that filter the IR and produce rewritten IR No formal theory for order to apply them Some rules of thumb and best practices Some transformations can be profitably applied repeatedly, particularly if others transformations expose more opportunities 11/8/2011 © 2002-11 Hal Perkins & UW CSE S-4 A Taxonomy Machine Independent Transformations Realized profitability may actually depend on machine architecture, but are typically implemented without considering this Machine Dependent Transformations Most of the machine dependent code is in instruction selection & scheduling and register allocation Some machine dependent code belongs in the optimizer 11/8/2011 © 2002-11 Hal Perkins & UW CSE S-5 Machine Independent Transformations Dead code elimination Code motion Specialization Strength reduction -
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 -
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. -
ICS803 Elective – III Multicore Architecture Teacher Name: Ms
ICS803 Elective – III Multicore Architecture Teacher Name: Ms. Raksha Pandey Course Structure L T P 3 1 0 4 Prerequisite: Course Content: Unit-I: Multi-core Architectures Introduction to multi-core architectures, issues involved into writing code for multi-core architectures, Virtual Memory, VM addressing, VA to PA translation, Page fault, TLB- Parallel computers, Instruction level parallelism (ILP) vs. thread level parallelism (TLP), Performance issues, OpenMP and other message passing libraries, threads, mutex etc. Unit-II: Multi-threaded Architectures Brief introduction to cache hierarchy - Caches: Addressing a Cache, Cache Hierarchy, States of Cache line, Inclusion policy, TLB access, Memory Op latency, MLP, Memory Wall, communication latency, Shared memory multiprocessors, General architectures and the problem of cache coherence, Synchronization primitives: Atomic primitives; locks: TTS, ticket, array; barriers: central and tree; performance implications in shared memory programs; Chip multiprocessors: Why CMP (Moore's law, wire delay); shared L2 vs. tiled CMP; core complexity; power/performance; Snoopy coherence: invalidate vs. update, MSI, MESI, MOESI, MOSI; performance trade-offs; pipelined snoopy bus design; Memory consistency models: SC, PC, TSO, PSO, WO/WC, RC; Chip multiprocessor case studies: Intel Montecito and dual-core, Pentium4, IBM Power4, Sun Niagara Unit-III: Compiler Optimization Issues Code optimizations: Copy Propagation, dead Code elimination , Loop Optimizations-Loop Unrolling, Induction variable Simplification, Loop Jamming, Loop Unswitching, Techniques to improve detection of parallelism: Scalar Processors, Special locality, Temporal locality, Vector machine, Strip mining, Shared memory model, SIMD architecture, Dopar loop, Dosingle loop. Unit-IV: Control Flow analysis Control flow analysis, Flow graph, Loops in Flow graphs, Loop Detection, Approaches to Control Flow Analysis, Reducible Flow Graphs, Node Splitting. -
Scalable Conditional Induction Variables (CIV) Analysis
Scalable Conditional Induction Variables (CIV) Analysis ifact Cosmin E. Oancea Lawrence Rauchwerger rt * * Comple A t te n * A te s W i E * s e n l C l o D C O Department of Computer Science Department of Computer Science and Engineering o * * c u e G m s E u e C e n R t v e o d t * y * s E University of Copenhagen Texas A & M University a a l d u e a [email protected] [email protected] t Abstract k = k0 Ind. k = k0 DO i = 1, N DO i =1,N Var. DO i = 1, N IF(cond(b(i)))THEN Subscripts using induction variables that cannot be ex- k = k+2 ) a(k0+2*i)=.. civ = civ+1 )? pressed as a formula in terms of the enclosing-loop indices a(k)=.. Sub. ENDDO a(civ) = ... appear in the low-level implementation of common pro- ENDDO k=k0+MAX(2N,0) ENDIF ENDDO gramming abstractions such as filter, or stack operations and (a) (b) (c) pose significant challenges to automatic parallelization. Be- Figure 1. Loops with affine and CIV array accesses. cause the complexity of such induction variables is often due to their conditional evaluation across the iteration space of its closed-form equivalent k0+2*i, which enables its in- loops we name them Conditional Induction Variables (CIV). dependent evaluation by all iterations. More importantly, This paper presents a flow-sensitive technique that sum- the resulted code, shown in Figure 1(b), allows the com- marizes both such CIV-based and affine subscripts to pro- piler to verify that the set of points written by any dis- gram level, using the same representation. -
Induction Variable Analysis with Delayed Abstractions1
Induction Variable Analysis with Delayed Abstractions1 SEBASTIAN POP, and GEORGES-ANDRE´ SILBER CRI, Mines Paris, France and ALBERT COHEN ALCHEMY group, INRIA Futurs, Orsay, France We present the design of an induction variable analyzer suitable for the analysis of typed, low-level, three address representations in SSA form. At the heart of our analyzer stands a new algorithm that recognizes scalar evolutions. We define a representation called trees of recurrences that is able to capture different levels of abstractions: from the finer level that is a subset of the SSA representation restricted to arithmetic operations on scalar variables, to the coarser levels such as the evolution envelopes that abstract sets of possible evolutions in loops. Unlike previous work, our algorithm tracks induction variables without prior classification of a few evolution patterns: different levels of abstraction can be obtained on demand. The low complexity of the algorithm fits the constraints of a production compiler as illustrated by the evaluation of our implementation on standard benchmark programs. Categories and Subject Descriptors: D.3.4 [Programming Languages]: Processors—compilers, interpreters, optimization, retargetable compilers; F.3.2 [Logics and Meanings of Programs]: Semantics of Programming Languages—partial evaluation, program analysis General Terms: Compilers Additional Key Words and Phrases: Scalar evolutions, static analysis, static single assignment representation, assessing compilers heuristics regressions. 1Extension of Conference Paper: -
Fast-Path Loop Unrolling of Non-Counted Loops to Enable Subsequent Compiler Optimizations∗
Fast-Path Loop Unrolling of Non-Counted Loops to Enable Subsequent Compiler Optimizations∗ David Leopoldseder Roland Schatz Lukas Stadler Johannes Kepler University Linz Oracle Labs Oracle Labs Austria Linz, Austria Linz, Austria [email protected] [email protected] [email protected] Manuel Rigger Thomas Würthinger Hanspeter Mössenböck Johannes Kepler University Linz Oracle Labs Johannes Kepler University Linz Austria Zurich, Switzerland Austria [email protected] [email protected] [email protected] ABSTRACT Non-Counted Loops to Enable Subsequent Compiler Optimizations. In 15th Java programs can contain non-counted loops, that is, loops for International Conference on Managed Languages & Runtimes (ManLang’18), September 12–14, 2018, Linz, Austria. ACM, New York, NY, USA, 13 pages. which the iteration count can neither be determined at compile https://doi.org/10.1145/3237009.3237013 time nor at run time. State-of-the-art compilers do not aggressively optimize them, since unrolling non-counted loops often involves 1 INTRODUCTION duplicating also a loop’s exit condition, which thus only improves run-time performance if subsequent compiler optimizations can Generating fast machine code for loops depends on a selective appli- optimize the unrolled code. cation of different loop optimizations on the main optimizable parts This paper presents an unrolling approach for non-counted loops of a loop: the loop’s exit condition(s), the back edges and the loop that uses simulation at run time to determine whether unrolling body. All these parts must be optimized to generate optimal code such loops enables subsequent compiler optimizations. Simulat- for a loop. -
Compiler Optimization for Configurable Accelerators Betul Buyukkurt Zhi Guo Walid A
Compiler Optimization for Configurable Accelerators Betul Buyukkurt Zhi Guo Walid A. Najjar University of California Riverside University of California Riverside University of California Riverside Computer Science Department Electrical Engineering Department Computer Science Department Riverside, CA 92521 Riverside, CA 92521 Riverside, CA 92521 [email protected] [email protected] [email protected] ABSTRACT such as constant propagation, constant folding, dead code ROCCC (Riverside Optimizing Configurable Computing eliminations that enables other optimizations. Then, loop Compiler) is an optimizing C to HDL compiler targeting level optimizations such as loop unrolling, loop invariant FPGA and CSOC (Configurable System On a Chip) code motion, loop fusion and other such transforms are architectures. ROCCC system is built on the SUIF- applied. MACHSUIF compiler infrastructure. Our system first This paper is organized as follows. Next section discusses identifies frequently executed kernel loops inside programs on the related work. Section 3 gives an in depth description and then compiles them to VHDL after optimizing the of the ROCCC system. SUIF2 level optimizations are kernels to make best use of FPGA resources. This paper described in Section 4 followed by the compilation done at presents an overview of the ROCCC project as well as the MACHSUIF level in Section 5. Section 6 mentions our optimizations performed inside the ROCCC compiler. early results. Finally Section 7 concludes the paper. 1. INTRODUCTION 2. RELATED WORK FPGAs (Field Programmable Gate Array) can boost There are several projects and publications on translating C software performance due to the large amount of or other high level languages to different HDLs. Streams- parallelism they offer. -
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 -
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. -
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 . -
Topic 1D Slides
Topic I (d): Static Single Assignment Form (SSA) 621-10F/Topic-1d-SSA 1 Reading List Slides: Topic Ix Other readings as assigned in class 621-10F/Topic-1d-SSA 2 ABET Outcome Ability to apply knowledge of SSA technique in compiler optimization An ability to formulate and solve the basic SSA construction problem based on the techniques introduced in class. Ability to analyze the basic algorithms using SSA form to express and formulate dataflow analysis problem A Knowledge on contemporary issues on this topic. 621-10F/Topic-1d-SSA 3 Roadmap Motivation Introduction: SSA form Construction Method Application of SSA to Dataflow Analysis Problems PRE (Partial Redundancy Elimination) and SSAPRE Summary 621-10F/Topic-1d-SSA 4 Prelude SSA: A program is said to be in SSA form iff Each variable is statically defined exactly only once, and each use of a variable is dominated by that variable’s definition. So, is straight line code in SSA form ? 621-10F/Topic-1d-SSA 5 Example 푿ퟏ In general, how to transform an arbitrary program into SSA form? Does the definition of X 푿ퟐ 2 dominate its use in the example? 푿ퟑ 흓 (푿ퟏ, 푿ퟐ) 푿 ퟒ 621-10F/Topic-1d-SSA 6 SSA: Motivation Provide a uniform basis of an IR to solve a wide range of classical dataflow problems Encode both dataflow and control flow information A SSA form can be constructed and maintained efficiently Many SSA dataflow analysis algorithms are more efficient (have lower complexity) than their CFG counterparts. 621-10F/Topic-1d-SSA 7 Algorithm Complexity Assume a 1 GHz machine, and an algorithm that takes f(n) steps (1 step = 1 nanosecond).