XL Fortran Optimization and Programming Guide

Total Page:16

File Type:pdf, Size:1020Kb

XL Fortran Optimization and Programming Guide IBM XL Fortran Advanced Edition V10.1 for Linux Optimization and Programming Guide SC09-8022-00 IBM XL Fortran Advanced Edition V10.1 for Linux Optimization and Programming Guide SC09-8022-00 Note! Before using this information and the product it supports, be sure to read the general information under “Notices” on page 253. First Edition (November 2005) This edition applies to Version 10.1 of IBM XL Fortran Advanced Edition V10.1 for Linux (Program 5724-M17) and to all subsequent releases and modifications until otherwise indicated in new editions. Make sure you are using the correct edition for the level of the product. IBM welcomes your comments. You can send your comments electronically to the network ID listed below. Be sure to include your entire network address if you wish a reply. v Internet: [email protected] When you send information to IBM, you grant IBM a nonexclusive right to use or distribute the information in any way it believes appropriate without incurring any obligation to you. © Copyright International Business Machines Corporation 1990, 2005. All rights reserved. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents About this document . vii Using -qtune . .20 Who should read this document . vii Using -qcache . .20 How to use this document . vii Before you finish tuning . .20 How this document is organized . vii Further option driven tuning . .21 Conventions and terminology used in this Options for providing application characteristics 21 document . viii Options to control optimization transformations 23 Typographical conventions . viii Options to assist with performance analysis . .24 How to read syntax diagrams . viii Options that can inhibit performance . .25 How to read syntax statements . .xi Examples . .xi Chapter 4. Advanced optimization Notes on the path names . .xi concepts . .27 Notes on the terminology used . .xi Aliasing . .27 Related information . xii Inlining . .28 IBM XL Fortran documentation . xii Finding the right level of inlining . .28 Additional documentation . xiii Related documentation . xiii Chapter 5. Managing code size . .31 Standards documents . xiii Steps for reducing code size . .32 Technical support . xiv Compiler option influences on code size . .32 How to send your comments . xiv The -qipa compiler option . .32 The -Q inlining option . .32 Chapter 1. Performance concepts . .1 The -qhot compiler option . .32 Optimization explained . .1 The -qcompact compiler option . .33 Tuning explained . .2 Other influences on code size . .33 Beyond optimization and tuning: effective High activity areas . .33 programming techniques . .3 Computed GOTOs and CASE constructs . .33 Linking and code size . .34 Chapter 2. Optimizing XL compiler applications . .5 Chapter 6. Debugging optimized code 37 Why optimization is essential. .5 Different results in optimized programs . .37 Basic command-line optimization . .5 Optimizing at level 0 . .6 Chapter 7. Compiler-friendly Optimizing at level 2 . .6 programming techniques . .39 Advanced command-line optimization . .7 General practices . .39 Optimizing at level 3 . .8 Variables and pointers . .40 An intermediate step: adding -qhot suboptions at Arrays . .40 level 3 . .8 Choosing appropriate variable sizes . .40 Optimization at level 4 . .9 Optimization at level 5 . .10 Chapter 8. High performance libraries 41 Benefits of high-order transformation (HOT) . .10 HOT short vectorization . .11 Using the Mathematical Acceleration Subsystem HOT long vectorization . .11 (MASS) . .41 HOT array size adjustment . .11 Using the scalar library . .41 Benefits of interprocedural analysis (IPA) . .12 Using the vector libraries . .43 Using IPA on the compile step only . .13 Compiling and linking a program with MASS . .46 IPA Levels and other IPA suboptions . .13 Using the Basic Linear Algebra Subprograms (BLAS) 47 Using IPA across the XL compiler family . .14 BLAS function syntax . .48 Benefits of profile-directed feedback (PDF) . .15 Linking the libxlopt library . .50 PDF walkthrough . .15 Getting more performance . .16 Chapter 9. Parallel programming with XL Fortran . .51 Chapter 3. Tuning XL compiler Compiling your SMP code . .51 applications . .17 Setting OMP and SMP run time options . .51 Tuning for your target architecture . .17 The XLSMPOPTS environment variable . .51 Using -qarch . .18 OpenMP environment variables . .56 Optimizing your SMP code . .58 © Copyright IBM Corp. 1990, 2005 iii Developing and running SMP applications . .58 omp_set_nest_lock(nvar) . 134 An introduction to SMP directives . .59 omp_set_num_threads(number_of_threads_expr) 135 Parallel region construct . .59 omp_test_lock(svar) . 136 Work-sharing constructs . .59 omp_test_nest_lock(nvar) . 136 Combined parallel work-sharing constructs . .60 omp_unset_lock(svar) . 137 Synchronization constructs . .60 omp_unset_nest_lock(nvar) . 138 Other OpenMP Directives . .60 Pthreads library module . 139 Non-OpenMP SMP directives . .60 Pthreads data structures, functions, and Detailed descriptions of SMP directives . .60 subroutines . 139 ATOMIC . .60 f_maketime(delay). 141 BARRIER . .63 f_pthread_attr_destroy(attr). 142 CRITICAL / END CRITICAL . .64 f_pthread_attr_getdetachstate(attr, detach) . 142 DO / END DO . .66 f_pthread_attr_getguardsize(attr, guardsize) . 143 DO SERIAL . .69 f_pthread_attr_getinheritsched(attr, inherit) . 143 FLUSH . .70 f_pthread_attr_getschedparam(attr, param) . 144 MASTER / END MASTER . .72 f_pthread_attr_getschedpolicy(attr, policy) . 144 ORDERED / END ORDERED . .74 f_pthread_attr_getscope(attr, scope) . 145 PARALLEL / END PARALLEL . .76 f_pthread_attr_getstack(attr, stackaddr, ssize) 145 PARALLEL DO / END PARALLEL DO . .78 f_pthread_attr_init(attr) . 146 PARALLEL SECTIONS / END PARALLEL f_pthread_attr_setdetachstate(attr, detach) . 146 SECTIONS . .82 f_pthread_attr_setguardsize(attr, guardsize) . 147 PARALLEL WORKSHARE / END PARALLEL f_pthread_attr_setinheritsched(attr, inherit) . 147 WORKSHARE . .85 f_pthread_attr_setschedparam(attr, param) . 148 SCHEDULE . .86 f_pthread_attr_setschedpolicy(attr, policy) . 149 SECTIONS / END SECTIONS . .89 f_pthread_attr_setscope(attr, scope) . 149 SINGLE / END SINGLE . .93 f_pthread_attr_setstack(attr, stackaddr, ssize) 150 THREADLOCAL . .96 f_pthread_attr_t . 150 THREADPRIVATE . .99 f_pthread_cancel(thread) . 151 WORKSHARE . 103 f_pthread_cleanup_pop(exec) . 151 OpenMP directive clauses . 106 f_pthread_cleanup_push(cleanup, flag, arg) . 152 Global rules for directive clauses . 106 f_pthread_cond_broadcast(cond) . 153 COPYIN . 107 f_pthread_cond_destroy(cond) . 153 COPYPRIVATE . 109 f_pthread_cond_init(cond, cattr) . 154 DEFAULT . .110 f_pthread_cond_signal(cond) . 154 IF . 111 f_pthread_cond_t . 155 FIRSTPRIVATE . .112 f_pthread_cond_timedwait(cond, mutex, LASTPRIVATE . .113 timeout) . 155 NUM_THREADS . .115 f_pthread_cond_wait(cond, mutex) . 156 ORDERED . .115 f_pthread_condattr_destroy(cattr) . 156 PRIVATE . .116 f_pthread_condattr_getpshared(cattr, pshared) 156 REDUCTION . .117 f_pthread_condattr_init(cattr) . 157 SCHEDULE . 120 f_pthread_condattr_setpshared(cattr, pshared) 158 SHARED . 122 f_pthread_condattr_t . 158 OpenMP execution environment, lock and timing f_pthread_create(thread, attr, flag, ent, arg) . 159 routines . 124 f_pthread_detach(thread) . 160 omp_destroy_lock(svar) . 125 f_pthread_equal(thread1, thread2) . 160 omp_destroy_nest_lock(nvar) . 125 f_pthread_exit(ret) . 161 omp_get_dynamic() . 126 f_pthread_getconcurrency() . 161 omp_get_max_threads() . 126 f_pthread_getschedparam(thread, policy, param) 162 omp_get_nested() . 127 f_pthread_getspecific(key, arg) . 162 omp_get_num_procs() . 127 f_pthread_join(thread, ret) . 163 omp_get_num_threads() . 127 f_pthread_key_create(key, dtr) . 163 omp_get_thread_num() . 128 f_pthread_key_delete(key) . 164 omp_get_wtick() . 129 f_pthread_key_t . 164 omp_get_wtime() . 130 f_pthread_kill(thread, sig) . 165 omp_in_parallel() . 130 f_pthread_mutex_destroy(mutex) . 165 omp_init_lock(svar) . 131 f_pthread_mutex_init(mutex, mattr) . 166 omp_init_nest_lock(nvar) . 132 f_pthread_mutex_lock(mutex) . 166 omp_set_dynamic(enable_expr) . 132 f_pthread_mutex_t . 167 omp_set_lock(svar) . 133 f_pthread_mutex_trylock(mutex) . 167 omp_set_nested(enable_expr) . 134 f_pthread_mutex_unlock(mutex) . 167 iv XL Fortran Optimization and Programming Guide f_pthread_mutexattr_destroy(mattr) . 168 Linkage convention for function calls . 205 f_pthread_mutexattr_getpshared(mattr, pshared) 168 Pointers to functions . 206 f_pthread_mutexattr_gettype(mattr, type) . 169 Function values . 206 f_pthread_mutexattr_init(mattr) . 170 The Stack floor . 207 f_pthread_mutexattr_setpshared(mattr, pshared) 170 Stack overflow . 207 f_pthread_mutexattr_settype(mattr, type) . 171 Prolog and epilog . 207 f_pthread_mutexattr_t . 172 Traceback . 207 f_pthread_once(once, initr) . 172 THREADLOCAL common blocks and f_pthread_once_t . 172 interlanguage calls with C . 208 f_pthread_rwlock_destroy(rwlock) . 172 Example . 209 f_pthread_rwlock_init(rwlock, rwattr) . 173 f_pthread_rwlock_rdlock(rwlock) . 173 Chapter 11. Implementation details of f_pthread_rwlock_t . 174 XL Fortran Input/Output (I/O) . .211 f_pthread_rwlock_tryrdlock(rwlock) . 174 Implementation details of file formats . .211 f_pthread_rwlock_trywrlock(rwlock) . 175 File names . 212 f_pthread_rwlock_unlock(rwlock) . 175 Preconnected and Implicitly Connected Files . 212 f_pthread_rwlock_wrlock(rwlock) . 176 File positioning . 213 f_pthread_rwlockattr_destroy(rwattr) . 176 I/O Redirection
Recommended publications
  • 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).
    [Show full text]
  • 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.
    [Show full text]
  • The Effect of Code Expanding Optimizations on Instruction Cache Design
    May 1991 UILU-EN G-91-2227 CRHC-91-17 Center for Reliable and High-Performance Computing THE EFFECT OF CODE 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____________ SECURITY ¿LASSIPlOvriON OF t h is PAGE REPORT DOCUMENTATION PAGE 1a. REPORT SECURITY CLASSIFICATION 1b. RESTRICTIVE MARKINGS Unclassified None 2a. SECURITY CLASSIFICATION AUTHORITY 3 DISTRIBUTION/AVAILABILITY OF REPORT 2b. DECLASSIFICATION/DOWNGRADING SCHEDULE Approved for public release; distribution unlimited 4. PERFORMING ORGANIZATION REPORT NUMBER(S) 5. MONITORING ORGANIZATION REPORT NUMBER(S) UILU-ENG-91-2227 CRHC-91-17 6a. NAME OF PERFORMING ORGANIZATION 6b. OFFICE SYMBOL 7a. NAME OF MONITORING ORGANIZATION Coordinated Science Lab (If applicable) NCR, NSF, AMD, NASA University of Illinois N/A 6c ADDRESS (G'ty, Staff, and ZIP Code) 7b. ADDRESS (Oty, Staff, and ZIP Code) 1101 W. Springfield Avenue Dayton, OH 45420 Urbana, IL 61801 Washington DC 20550 Langley VA 20200 8a. NAME OF FUNDING/SPONSORING 8b. OFFICE SYMBOL ORGANIZATION 9. PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER 7a (If applicable) N00014-91-J-1283 NASA NAG 1-613 8c. ADDRESS (City, State, and ZIP Cod*) 10. SOURCE OF FUNDING NUMBERS PROGRAM PROJECT t a sk WORK UNIT 7b ELEMENT NO. NO. NO. ACCESSION NO. The Effect of Code Expanding Optimizations on Instruction Cache Design 12. PERSONAL AUTHOR(S) Chen, William, Pohua Chang, Thomas Conte and Wen-Mei Hwu 13a. TYPE OF REPORT 13b. TIME COVERED 14. OATE OF REPORT (Year, Month, Day) Jl5.
    [Show full text]
  • ARM Optimizing C/C++ Compiler V20.2.0.LTS User's Guide (Rev. V)
    ARM Optimizing C/C++ Compiler v20.2.0.LTS User's Guide Literature Number: SPNU151V January 1998–Revised February 2020 Contents Preface ........................................................................................................................................ 9 1 Introduction to the Software Development Tools.................................................................... 12 1.1 Software Development Tools Overview ................................................................................. 13 1.2 Compiler Interface.......................................................................................................... 15 1.3 ANSI/ISO Standard ........................................................................................................ 15 1.4 Output Files ................................................................................................................. 15 1.5 Utilities ....................................................................................................................... 16 2 Using the C/C++ Compiler ................................................................................................... 17 2.1 About the Compiler......................................................................................................... 18 2.2 Invoking the C/C++ Compiler ............................................................................................. 18 2.3 Changing the Compiler's Behavior with Options ......................................................................
    [Show full text]
  • Compiler Optimization-Space Exploration
    Journal of Instruction-Level Parallelism 7 (2005) 1-25 Submitted 10/04; published 2/05 Compiler Optimization-Space Exploration Spyridon Triantafyllis [email protected] Department of Computer Science Princeton University Princeton, NJ 08540 USA Manish Vachharajani [email protected] Department of Electrical and Computer Engineering University of Colorado at Boulder Boulder, CO 80309 USA David I. August [email protected] Department of Computer Science Princeton University Princeton, NJ 08540 USA Abstract To meet the performance demands of modern architectures, compilers incorporate an ever- increasing number of aggressive code transformations. Since most of these transformations are not universally beneficial, compilers traditionally control their application through predictive heuris- tics, which attempt to judge an optimization’s effect on final code quality a priori. However, com- plex target architectures and unpredictable optimization interactions severely limit the accuracy of these judgments, leading to performance degradation because of poor optimization decisions. This performance loss can be avoided through the iterative compilation approach, which ad- vocates exploring many optimization options and selecting the best one a posteriori. However, existing iterative compilation systems suffer from excessive compile times and narrow application domains. By overcoming these limitations, Optimization-Space Exploration (OSE) becomes the first iterative compilation technique suitable for general-purpose production compilers. OSE nar- rows down the space of optimization options explored through limited use of heuristics. A compiler tuning phase further limits the exploration space. At compile time, OSE prunes the remaining opti- mization configurations in the search space by exploiting feedback from earlier configurations tried. Finally, rather than measuring actual runtimes, OSE compares optimization outcomes through static performance estimation, further enhancing compilation speed.
    [Show full text]
  • XL Fortran: Getting Started
    IBM XL Fortran for AIX, V12.1 Getting Started with XL Fortran Ve r s i o n 12.1 GC23-8898-00 IBM XL Fortran for AIX, V12.1 Getting Started with XL Fortran Ve r s i o n 12.1 GC23-8898-00 Note Before using this information and the product it supports, read the information in “Notices” on page 29. First edition This edition applies to IBM XL Fortran for AIX, V12.1 (Program number 5724-U82) and to all subsequent releases and modifications until otherwise indicated in new editions. Make sure you are using the correct edition for the level of the product. © Copyright International Business Machines Corporation 1996, 2008. All rights reserved. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents About this document . .v Architecture and processor support . .12 Conventions . .v Performance and optimization . .13 Related information . .ix Other new or changed compiler options . .15 IBM XL Fortran information . .ix Standards and specifications . .x Chapter 3. Setting up and customizing Other IBM information. .xi XL Fortran . .17 Technical support . .xi Using custom compiler configuration files . .17 How to send your comments . .xi Chapter 4. Developing applications Chapter 1. Introducing XL Fortran . .1 with XL Fortran . .19 Commonality with other IBM compilers . .1 The compiler phases . .19 Hardware and operating system support . .1 Editing Fortran source files . .19 A highly configurable compiler . .1 Compiling with XL Fortran . .20 Language standards compliance . .2 Invoking the compiler . .22 Source-code migration and conformance checking 3 Compiling parallelized XL Fortran applications 22 Tools and utilities .
    [Show full text]
  • Machine Learning in Compiler Optimisation Zheng Wang and Michael O’Boyle
    1 Machine Learning in Compiler Optimisation Zheng Wang and Michael O’Boyle Abstract—In the last decade, machine learning based com- hope of improving performance but could in some instances pilation has moved from an an obscure research niche to damage it. a mainstream activity. In this article, we describe the rela- Machine learning predicts an outcome for a new data point tionship between machine learning and compiler optimisation and introduce the main concepts of features, models, training based on prior data. In its simplest guise it can be considered and deployment. We then provide a comprehensive survey and a from of interpolation. This ability to predict based on prior provide a road map for the wide variety of different research information can be used to find the data point with the best areas. We conclude with a discussion on open issues in the outcome and is closely tied to the area of optimisation. It is at area and potential research directions. This paper provides both this overlap of looking at code improvement as an optimisation an accessible introduction to the fast moving area of machine learning based compilation and a detailed bibliography of its problem and machine learning as a predictor of the optima main achievements. where we find machine-learning compilation. Optimisation as an area, machine-learning based or other- Index Terms—Compiler, Machine Learning, Code Optimisa- tion, Program Tuning wise, has been studied since the 1800s [8], [9]. An interesting question is therefore why has has the convergence of these two areas taken so long? There are two fundamental reasons.
    [Show full text]
  • An Optimization-Driven Incremental Inline Substitution Algorithm for Just-In-Time Compilers
    An Optimization-Driven Incremental Inline Substitution Algorithm for Just-in-Time Compilers Aleksandar Prokopec Gilles Duboscq David Leopoldseder Thomas Würthinger Oracle Labs, Switzerland Oracle Labs, Switzerland JKU Linz, Austria Oracle Labs, Switzerland [email protected] [email protected] [email protected] [email protected] Abstract—Inlining is one of the most important compiler mization prediction by incrementally exploring and specializing optimizations. It reduces call overheads and widens the scope of the program’s call tree, and clusters the callsites before inlining. other optimizations. But, inlining is somewhat of a black art of an We report the following new observations: optimizing compiler, and was characterized as a computationally intractable problem. Intricate heuristics, tuned during countless • In many programs, there is an impedance between the hours of compiler engineering, are often at the core of an logical units of code (i.e. subroutines) and the optimizable inliner implementation. And despite decades of research, well- units of code (groups of subroutines). It is therefore established inlining heuristics are still missing. beneficial to inline specific clusters of callsites, instead In this paper, we describe a novel inlining algorithm for of a single callsite at a time. We describe how to identify JIT compilers that incrementally explores a program’s call graph, and alternates between inlining and optimizations. We such callsite clusters. devise three novel heuristics that guide our inliner: adaptive • When deciding when to stop inlining, using an adaptive decision thresholds, callsite clustering, and deep inlining trials. threshold function (rather than a fixed threshold value) We implement the algorithm inside Graal, a dynamic JIT produces more efficient programs.
    [Show full text]
  • A Unified Approach to Global Program Optimization
    A UNIFIED APPROACH TO GLOBAL PROGRAM OPTIMIZATION Gary A. Kildall Computer Science Group Naval Postgraduate School Monterey, California Abstract A technique is presented for global analysie of program structure in order to perform compile time optimization of object code generated for expressions. The global expression optimization presented includes constant propagation, common subexpression elimination, elimination of redundant register load operations, and live expression analysis. A general purpose program flow analysis algorithm is developed which depends upon the existence of an “optimizing function.” The algorithm is defined formally using a directed graph model of program flow structure, and is shown to be correct, Several optimizing functions are defined which, when used in conjunction with the flow analysis algorithm, provide the various forms of code optimization. The flow analysis algorithm is sufficiently general that additional functions can easily be defined for other forms of globa~ cod: optimization. 1. INTRODUCTION of the graph represent program control flow Dossi- bilities between the nodes at execution–time. A number of techniques have evolved for the compile-time analysis of program structure in order to locate redundant computations, perform constant computations, and reduce the number of store–load sequences between memory and high-speed registers. Some of these techniques provide analysis of only entry’ ~:=j straight-line sequences of instructions [5,6,9,14, o 17,18,19,20,27,29,34,36,38,39,43,45 ,46], while B others take the program branching structure into &() account [2,3,4,10,11,12,13,15,23,30, 32,33,35]. The purpose here is to describe a single program c flow analysis algorithm which extends all of b=2 these straight-line optimizing techniques to in- D clude branching structure.
    [Show full text]
  • XL C/C++: Optimization and Programming Guide About This Document
    IBM XL C/C++ for Linux, V16.1.1 IBM Optimization and Programming Guide Version 16.1.1 SC27-8046-01 IBM XL C/C++ for Linux, V16.1.1 IBM Optimization and Programming Guide Version 16.1.1 SC27-8046-01 Note Before using this information and the product it supports, read the information in “Notices” on page 121. First edition This edition applies to IBM XL C/C++ for Linux, V16.1.1 (Program 5765-J13, 5725-C73) and to all subsequent releases and modifications until otherwise indicated in new editions. Make sure you are using the correct edition for the level of the product. © Copyright IBM Corporation 1996, 2018. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents About this document ......... v Optimizing at level 4 .......... 26 Who should read this document........ v Optimizing at level 5 .......... 27 How to use this document.......... v Tuning for your system architecture ...... 28 How this document is organized ....... v Getting the most out of target machine options 28 Conventions .............. vi Using high-order loop analysis and transformations 29 Related information ............ ix Getting the most out of -qhot ....... 30 Available help information ........ ix Using shared-memory parallelism (SMP) .... 31 Standards and specifications ........ xi Getting the most out of -qsmp ....... 32 Technical support ............ xi Using interprocedural analysis ........ 33 How to send your comments ........ xii Getting the most from -qipa ........ 35 Using profile-directed feedback ........ 36 Chapter 1. Using XL C/C++ with Fortran 1 Compiling with -qpdf1 ......... 37 Training with typical data sets ......
    [Show full text]
  • Demand-Driven Inlining Heuristics in a Region-Based Optimizing Compiler for ILP Architectures
    Demand-driven Inlining Heuristics in a Region-based Optimizing Compiler for ILP Architectures Tom Way, Ben Breech, Wei Du and Lori Pollock Department of Computer and Information Sciences University of Delaware, Newark, DE 19716, USA ¡ email: way,breech,wei,pollock ¢ @cis.udel.edu ABSTRACT compilation unit size and content. In addition to expos- While scientists and engineers have been taking on the ing opportunities for interprocedural instruction schedul- challenge of converting their applications into parallel ing and optimization, the size of the resulting regions (i.e., programs, compilers are charged with identifying op- compilation units) has been shown to be smaller than func- portunities and transforming codes to exploit the avail- tions, and can be limited to any desired size, in order to able instruction-level parallelism (ILP) offered by today’s reduce the impact of the quadratic algorithmic complexity uniprocessors, even within a parallel computing environ- of the applied optimization transformations [14]. ment. Region-based compilation repartitions a program The key component of a region-based compiler is the into more desirable compilation units for optimization and region formation phase which partitions the program into ILP scheduling. With region-based compilation, the com- regions using heuristics with the intent that the optimizer piler can control problem size and complexity by control- will be invoked with a scope that is limited to a single re- ling region size and contents, and expose interprocedu- gion at a time. Thus, the quality of the generated code ral scheduling and optimization opportunities without in- depends greatly upon the ability of the region formation terprocedural analysis or large function bodies.
    [Show full text]
  • Analysis and Optimization of Scientific Applications Through Set and Relation Abstractions M
    Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 2017 Analysis and Optimization of Scientific Applications through Set and Relation Abstractions M. Tohid (Rastegar Tohid, Mohammed) Louisiana State University and Agricultural and Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Electrical and Computer Engineering Commons Recommended Citation Tohid (Rastegar Tohid, Mohammed), M., "Analysis and Optimization of Scientific Applications through Set and Relation Abstractions" (2017). LSU Doctoral Dissertations. 4404. https://digitalcommons.lsu.edu/gradschool_dissertations/4404 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. ANALYSIS AND OPTIMIZATION OF SCIENTIFIC APPLICATIONS THROUGH SET AND RELATION ABSTRACTIONS A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The School of Electrical Engineering and Computer Science by Mohammad Rastegar Tohid M.Sc., Newcastle University, 2008 B.Sc., Azad University (S. Tehran Campus), 2006 May 2017 Dedication Dedicated to my parents who have dedicated their lives to their children. ii Acknowledgments I thank my advisor Dr. Ramanujam for valueing independent research and allowing me to work on topics that geniunely intrigued me while never lettig me go off the rail. His optimism and words of encouragement always aspired me. It has been a great pleasure to have known and worked with him.
    [Show full text]