The Effect of Code Expanding Optimizations on Instruction Cache Design
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Loop Pipelining with Resource and Timing Constraints
UNIVERSITAT POLITÈCNICA DE CATALUNYA LOOP PIPELINING WITH RESOURCE AND TIMING CONSTRAINTS Autor: Fermín Sánchez October, 1995 1 1 1 1 1 1 1 1 2 1• SOFTWARE PIPELINING 1 1 1 2.1 INTRODUCTION 1 Software pipelining [ChaSl] comprises a family of techniques aimed at finding a pipelined schedule of the execution of loop iterations. The pipelined schedule represents a new loop body which may contain instructions belonging to different iterations. The sequential execution of the schedule takes less time than the sequential execution of the iterations of the loop as they were initially written. 1 In general, a pipelined loop schedule has the following characteristics1: • All the iterations (of the new loop) are executed in the same fashion. • The initiation interval (ÍÍ) between the issuing of two consecutive iterations is always the same. 1 Figure 2.1 shows an example of software pipelining a loop. The DDG representing the loop body to pipeline is presented in Figure 2.1(a). The loop must be executed ten times (with an iteration index i G [0,9]). Let us assume that all instructions in the loop are executed in a single cycle 1 and a new iteration may start every cycle (otherwise the dependence between instruction A from 1 We will assume here that the schedule contains a unique iteration of the loop. 1 15 1 1 I I 16 CHAPTER 2 I I time B,, Prol AO Q, BO A, I Q, B, A2 3<i<9 I Steady State D4 B7 I Epilogue Cy I D9 (a) (b) (c) I Figure 2.1 Software pipelining a loop (a) DDG representing a loop body (b) Parallel execution of the loop (c) New parallel loop body I iterations i and i+ 1 will not be honored).- With this assumption,-the loop can be executed in a I more parallel fashion than the sequential one, as shown in Figure 2.1(b). -
Program Analysis and Optimization for Embedded Systems
Program Analysis and Optimization for Embedded Systems Mike Jochen and Amie Souter jochen, souter ¡ @cis.udel.edu Computer and Information Sciences University of Delaware Newark, DE 19716 (302) 831-6339 1 Introduction ¢ have low cost A proliferation in use of embedded systems is giving cause ¢ have low power consumption for a rethinking of traditional methods and effects of pro- ¢ require as little physical space as possible gram optimization and how these methods and optimizations can be adapted for these highly specialized systems. This pa- ¢ meet rigid time constraints for computation completion per attempts to raise some of the issues unique to program These requirements place constraints on the underlying analysis and optimization for embedded system design and architecture’s design, which together affect compiler design to address some of the solutions that have been proposed to and program optimization techniques. Each of these crite- address these specialized issues. ria must be balanced against the other, as each will have an This paper is organized as follows: section 2 provides effect on the other (e.g. making a system smaller and faster pertinent background information on embedded systems, de- will typically increase its cost and power consumption). A lineating the special needs and problems inherent with em- typical embedded system, as illustrated in figure 1, consists bedded system design; section 3 addresses one such inherent of a processor core, a program ROM (Read Only Memory), problem, namely limited space for program code; section 4 RAM (Random Access Memory), and an ASIC (Applica- discusses current approaches and open issues for code min- tion Specific Integrated Circuit). -
The LLVM Instruction Set and Compilation Strategy
The LLVM Instruction Set and Compilation Strategy Chris Lattner Vikram Adve University of Illinois at Urbana-Champaign lattner,vadve ¡ @cs.uiuc.edu Abstract This document introduces the LLVM compiler infrastructure and instruction set, a simple approach that enables sophisticated code transformations at link time, runtime, and in the field. It is a pragmatic approach to compilation, interfering with programmers and tools as little as possible, while still retaining extensive high-level information from source-level compilers for later stages of an application’s lifetime. We describe the LLVM instruction set, the design of the LLVM system, and some of its key components. 1 Introduction Modern programming languages and software practices aim to support more reliable, flexible, and powerful software applications, increase programmer productivity, and provide higher level semantic information to the compiler. Un- fortunately, traditional approaches to compilation either fail to extract sufficient performance from the program (by not using interprocedural analysis or profile information) or interfere with the build process substantially (by requiring build scripts to be modified for either profiling or interprocedural optimization). Furthermore, they do not support optimization either at runtime or after an application has been installed at an end-user’s site, when the most relevant information about actual usage patterns would be available. The LLVM Compilation Strategy is designed to enable effective multi-stage optimization (at compile-time, link-time, runtime, and offline) and more effective profile-driven optimization, and to do so without changes to the traditional build process or programmer intervention. LLVM (Low Level Virtual Machine) is a compilation strategy that uses a low-level virtual instruction set with rich type information as a common code representation for all phases of compilation. -
Research of Register Pressure Aware Loop Unrolling Optimizations for Compiler
MATEC Web of Conferences 228, 03008 (2018) https://doi.org/10.1051/matecconf/201822803008 CAS 2018 Research of Register Pressure Aware Loop Unrolling Optimizations for Compiler Xuehua Liu1,2, Liping Ding1,3 , Yanfeng Li1,2 , Guangxuan Chen1,4 , Jin Du5 1Laboratory of Parallel Software and Computational Science, Institute of Software, Chinese Academy of Sciences, Beijing, China 2University of Chinese Academy of Sciences, Beijing, China 3Digital Forensics Lab, Institute of Software Application Technology, Guangzhou & Chinese Academy of Sciences, Guangzhou, China 4Zhejiang Police College, Hangzhou, China 5Yunnan Police College, Kunming, China Abstract. Register pressure problem has been a known problem for compiler because of the mismatch between the infinite number of pseudo registers and the finite number of hard registers. Too heavy register pressure may results in register spilling and then leads to performance degradation. There are a lot of optimizations, especially loop optimizations suffer from register spilling in compiler. In order to fight register pressure and therefore improve the effectiveness of compiler, this research takes the register pressure into account to improve loop unrolling optimization during the transformation process. In addition, a register pressure aware transformation is able to reduce the performance overhead of some fine-grained randomization transformations which can be used to defend against ROP attacks. Experiments showed a peak improvement of about 3.6% and an average improvement of about 1% for SPEC CPU 2006 benchmarks and a peak improvement of about 3% and an average improvement of about 1% for the LINPACK benchmark. 1 Introduction fundamental transformations, because it is not only performed individually at least twice during the Register pressure is the number of hard registers needed compilation process, it is also an important part of to store values in the pseudo registers at given program optimizations like vectorization, module scheduling and point [1] during the compilation process. -
CS153: Compilers Lecture 19: Optimization
CS153: Compilers Lecture 19: Optimization Stephen Chong https://www.seas.harvard.edu/courses/cs153 Contains content from lecture notes by Steve Zdancewic and Greg Morrisett Announcements •HW5: Oat v.2 out •Due in 2 weeks •HW6 will be released next week •Implementing optimizations! (and more) Stephen Chong, Harvard University 2 Today •Optimizations •Safety •Constant folding •Algebraic simplification • Strength reduction •Constant propagation •Copy propagation •Dead code elimination •Inlining and specialization • Recursive function inlining •Tail call elimination •Common subexpression elimination Stephen Chong, Harvard University 3 Optimizations •The code generated by our OAT compiler so far is pretty inefficient. •Lots of redundant moves. •Lots of unnecessary arithmetic instructions. •Consider this OAT program: int foo(int w) { var x = 3 + 5; var y = x * w; var z = y - 0; return z * 4; } Stephen Chong, Harvard University 4 Unoptimized vs. Optimized Output .globl _foo _foo: •Hand optimized code: pushl %ebp movl %esp, %ebp _foo: subl $64, %esp shlq $5, %rdi __fresh2: movq %rdi, %rax leal -64(%ebp), %eax ret movl %eax, -48(%ebp) movl 8(%ebp), %eax •Function foo may be movl %eax, %ecx movl -48(%ebp), %eax inlined by the compiler, movl %ecx, (%eax) movl $3, %eax so it can be implemented movl %eax, -44(%ebp) movl $5, %eax by just one instruction! movl %eax, %ecx addl %ecx, -44(%ebp) leal -60(%ebp), %eax movl %eax, -40(%ebp) movl -44(%ebp), %eax Stephen Chong,movl Harvard %eax,University %ecx 5 Why do we need optimizations? •To help programmers… •They write modular, clean, high-level programs •Compiler generates efficient, high-performance assembly •Programmers don’t write optimal code •High-level languages make avoiding redundant computation inconvenient or impossible •e.g. -
Analysis of Program Optimization Possibilities and Further Development
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Theoretical Computer Science 90 (1991) 17-36 17 Elsevier Analysis of program optimization possibilities and further development I.V. Pottosin Institute of Informatics Systems, Siberian Division of the USSR Acad. Sci., 630090 Novosibirsk, USSR 1. Introduction Program optimization has appeared in the framework of program compilation and includes special techniques and methods used in compiler construction to obtain a rather efficient object code. These techniques and methods constituted in the past and constitute now an essential part of the so called optimizing compilers whose goal is to produce an object code in run time, saving such computer resources as CPU time and memory. For contemporary supercomputers, the requirement of the proper use of hardware peculiarities is added. In our opinion, the existence of a sufficiently large number of optimizing compilers with real possibilities of producing “good” object code has evidently proved to be practically significant for program optimization. The methods and approaches that have accumulated in program optimization research seem to be no lesser valuable, since they may be successfully used in the general techniques of program construc- tion, i.e. in program synthesis, program transformation and at different steps of program development. At the same time, there has been criticism on program optimization and even doubts about its necessity. On the one hand, this viewpoint is based on blind faith in the new possibilities bf computers which will be so powerful that any necessity to worry about program efficiency will disappear. -
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. -
Register Allocation and Method Inlining
Combining Offline and Online Optimizations: Register Allocation and Method Inlining Hiroshi Yamauchi and Jan Vitek Department of Computer Sciences, Purdue University, {yamauchi,jv}@cs.purdue.edu Abstract. Fast dynamic compilers trade code quality for short com- pilation time in order to balance application performance and startup time. This paper investigates the interplay of two of the most effective optimizations, register allocation and method inlining for such compil- ers. We present a bytecode representation which supports offline global register allocation, is suitable for fast code generation and verification, and yet is backward compatible with standard Java bytecode. 1 Introduction Programming environments which support dynamic loading of platform-indep- endent code must provide supports for efficient execution and find a good balance between responsiveness (shorter delays due to compilation) and performance (optimized compilation). Thus, most commercial Java virtual machines (JVM) include several execution engines. Typically, there is an interpreter or a fast com- piler for initial executions of all code, and a profile-guided optimizing compiler for performance-critical code. Improving the quality of the code of a fast compiler has the following bene- fits. It raises the performance of short-running and medium length applications which exit before the expensive optimizing compiler fully kicks in. It also ben- efits long-running applications with improved startup performance and respon- siveness (due to less eager optimizing compilation). One way to achieve this is to shift some of the burden to an offline compiler. The main question is what optimizations are profitable when performed offline and are either guaranteed to be safe or can be easily validated by a fast compiler. -
A Hierarchical Region-Based Static Single Assignment Form
A Hierarchical Region-Based Static Single Assignment Form Jisheng Zhao and Vivek Sarkar Department of Computer Science, Rice University fjisheng.zhao,[email protected] Abstract. Modern compilation systems face the challenge of incrementally re- analyzing a program’s intermediate representation each time a code transforma- tion is performed. Current approaches typically either re-analyze the entire pro- gram after an individual transformation or limit the analysis information that is available after a transformation. To address both efficiency and precision goals in an optimizing compiler, we introduce a hierarchical static single-assignment form called Region Static Single-Assignment (Region-SSA) form. Static single assign- ment (SSA) form is an efficient intermediate representation that is well suited for solving many data flow analysis and optimization problems. By partitioning the program into hierarchical regions, Region-SSA form maintains a local SSA form for each region. Region-SSA supports a demand-driven re-computation of SSA form after a transformation is performed, since only the updated region’s SSA form needs to be reconstructed along with a potential propagation of exposed defs and uses. In this paper, we introduce the Region-SSA data structure, and present algo- rithms for construction and incremental reconstruction of Region-SSA form. The Region-SSA data structure includes a tree based region hierarchy, a region based control flow graph, and region-based SSA forms. We have implemented in Region- SSA form in the Habanero-Java (HJ) research compiler. Our experimental re- sults show significant improvements in compile-time compared to traditional ap- proaches that recompute the entire procedure’s SSA form exhaustively after trans- formation. -
The Effect of C Ode Expanding Optimizations on Instruction Cache Design
May 1991 UILU-ENG-91-2227 CRHC-91-17 Center for Reliable and High-Performance Computing " /ff ,./IJ 5; F.__. =, _. I J THE EFFECT OF C ODE EXPANDING OPTIMIZATIONS ON INSTRUCTION CACHE DESIGN William Y. Chen Pohua P. Chang Thomas M. Conte Wen-mei W. Hwu Coordinated Science Laboratory College of Engineering UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Approved for Public Release. Distribution Unlimited. UNCLASSIFIED ;ECURIrY CLASSIFICATION OF THiS PAGE REPORT DOCUMENTATION PAGE ii la. REPORT SECURITY CLASSIFICATION lb. RESTRICTIVE MARKINGS Unclassified None 2a. SECURITY CLASSIFICATION AUTHORITY 3 DISTRIBUTION / AVAILABILITY OF REPORT Approved for public release; 2b. DECLASSIFICATION I DOWNGRADING SCHEDULE distribution unlimited 4, PERFORMING ORGANIZATION REPORT NUMBER(S) S. MONITORING ORGANIZATION REPORT NUMBER(S) UILU-ENG-91-2227 CRHC-91-17 _.NAME OFPERFORMING ORGANIZATION 7a. NAME OF MONITORING ORGANIZATION Coordinated Science Lab (If ap_lic,_ble) NCR, NSF, AI_, NASA University of Illinois 6b. OFFICEN/A SYMBOL 6cAOORESS(O_,S_,andZ;PCode) 7b. AOORESS(O_,$tate, andZIPCodc) Dayton, OH 45420 ii01 W. Springfield Avenue Washington DC 20550 Langley VA 20200 Urbana, IL 61801 NAME OF FUNDING/SPONSORING 18b.OFFICE SYMBOL 9. PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER NO0014-9 l-J- 1283 NASA NAG 1-613 ORGANIZATION 7a I (If aDplitable) 8c. ADDRESS (City, State, and ZlP Code) 10. SOURCE OF FUNDING NUMBERS WORK UNIT ELEMENT NO. NO. ACCESSION NO. 7b I 11. TITLE (Include Securf(y C/aclrification) The Effect of Code Expanding Optimizations on Instruction Cache Design I 12. PERSONAL AUTHOR(S) Chen, William, Pohua Chang, Thomas Conte and Wen-Mei Hwu 13a. TYPE OF REPORT i13b, TIME COVERED 114. -
Stochastic Program Optimization by Eric Schkufza, Rahul Sharma, and Alex Aiken
research highlights DOI:10.1145/2863701 Stochastic Program Optimization By Eric Schkufza, Rahul Sharma, and Alex Aiken Abstract Although the technique sacrifices completeness it pro- The optimization of short sequences of loop-free, fixed-point duces dramatic increases in the quality of the resulting code. assembly code sequences is an important problem in high- Figure 1 shows two versions of the Montgomery multiplica- performance computing. However, the competing constraints tion kernel used by the OpenSSL RSA encryption library. of transformation correctness and performance improve- Beginning from code compiled by llvm −O0 (116 lines, not ment often force even special purpose compilers to pro- shown), our prototype stochastic optimizer STOKE produces duce sub-optimal code. We show that by encoding these code (right) that is 16 lines shorter and 1.6 times faster than constraints as terms in a cost function, and using a Markov the code produced by gcc −O3 (left), and even slightly faster Chain Monte Carlo sampler to rapidly explore the space of than the expert handwritten assembly included in the all possible code sequences, we are able to generate aggres- OpenSSL repository. sively optimized versions of a given target code sequence. Beginning from binaries compiled by llvm −O0, we are able 2. RELATED WORK to produce provably correct code sequences that either Although techniques that preserve completeness are effec- match or outperform the code produced by gcc −O3, icc tive within certain domains, their general applicability −O3, and in some cases expert handwritten assembly. remains limited. The shortcomings are best highlighted in the context of the code sequence shown in Figure 1. -
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.