The Need for Large Register Files in Integer Codes
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Risc I: a Reduced Instruction Set Vlsi Computer
RISC I: A REDUCED INSTRUCTION SET VLSI COMPUTER DAVID A. PATTERSON and CARLO H. SEQUIN Computer Science Division University of California Berkeley, California ABSTRACT to implement CISC is the best way to use this “scarce” resource. The Reduced Instruction Set Computer (RISC) Project investigates an alternatrve to the general trend toward computers wrth increasingly complex instruction sets: With a The above findings led to the Reduced Instruction Set proper set of instructions and a corresponding architectural Computer (RISC) Project. The purpose of the project is design, a machine wrth a high effective throughput can be to explore alternatives to the general trend toward achieved. The simplicity of the instruction set and addressing architectural complexity. The hypothesis is that by modes allows most Instructions to execute in a single machine cycle, and the srmplicity of each instruction guarantees a short reducing the instruction set, VLSI architecture can be cycle time. In addition, such a machine should have a much designed that uses the scarce resources more effectively shorter design trme. than CISC. We also expect this approach to reduce design time, the number of design errors, and the This paper presents the architecture of RISC I and its novel execution time of individual instructions. hardware support scheme for procedure call/return. Overlapprng sets of regrster banks that can pass parameters directly to subrouttnes are largely responsible for the excellent Our initial version of such a computer is called RISC I. performance of RISC I. Static and dynamtc comparisons To meet our goals of simplicity and effective single-chip between this new architecture and more traditional machines implementation, we placed the following “constraints” are given. -
Computer Architecture and Assembly Language
Computer Architecture and Assembly Language Gabriel Laskar EPITA 2015 License I Copyright c 2004-2005, ACU, Benoit Perrot I Copyright c 2004-2008, Alexandre Becoulet I Copyright c 2009-2013, Nicolas Pouillon I Copyright c 2014, Joël Porquet I Copyright c 2015, Gabriel Laskar Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with the Invariant Sections being just ‘‘Copying this document’’, no Front-Cover Texts, and no Back-Cover Texts. Introduction Part I Introduction Gabriel Laskar (EPITA) CAAL 2015 3 / 378 Introduction Problem definition 1: Introduction Problem definition Outline Gabriel Laskar (EPITA) CAAL 2015 4 / 378 Introduction Problem definition What are we trying to learn? Computer Architecture What is in the hardware? I A bit of history of computers, current machines I Concepts and conventions: processing, memory, communication, optimization How does a machine run code? I Program execution model I Memory mapping, OS support Gabriel Laskar (EPITA) CAAL 2015 5 / 378 Introduction Problem definition What are we trying to learn? Assembly Language How to “talk” with the machine directly? I Mechanisms involved I Assembly language structure and usage I Low-level assembly language features I C inline assembly Gabriel Laskar (EPITA) CAAL 2015 6 / 378 I Programmers I Wise managers Introduction Problem definition Who do I talk to? I System gurus I Low-level enthusiasts Gabriel Laskar (EPITA) CAAL -
VME for Experiments Chairman: Junsei Chiba (KEK)
KEK Report 89-26 March 1990 D PROCEEDINGS of SYMPOSIUM on Data Acquisition and Processing for Next Generation Experiments 9 -10 March 1989 KEK, Tsukuba Edited by H. FUJII, J. CHIBA and Y. WATASE NATIONAL LABORATORY FOR HIGH ENERGY PHYSICS PROCEEDINGS of SYMPOSIUM on Data Acquisition and Processing for Next Generation Experiments 9 - 10 March 1989 KEK, Tsukuba Edited H. Fiflii, J. Chiba andY. Watase i National Laboratory for High Energy Physics, 1990 KEK Reports are available from: Technical Infonnation&Libraiy National Laboratory for High Energy Physics 1-1 Oho, Tsukuba-shi Ibaraki-ken, 305 JAPAN Phone: 0298-64-1171 Telex: 3652-534 (Domestic) (0)3652-534 (International) Fax: 0298-64-4604 Cable: KEKOHO Foreword This symposium has been organized to foresee the next generation of data acquisition and processing system in high energy physics and nuclear physics experiments. The recent revolutionary progress in the semiconductor and computer technologies is giving us an oppotunity to extend our idea on the experiments. The high density electronics of LSI technology provides an ideal front-end electronics such as readout circuits for silicon strip detector and multi-anode phototubes as well as wire chambers. The VLSI technology has advantages over the obsolite discrete one in the various aspects ; reduction of noise, small propagation delay, lower power dissipation, small space for the installation, improvement of the system reliability and maintenability. The small sized front-end electronics will be mounted just on the detector and the digital data might be transfered off the detector to the computer room with optical fiber data transmission lines. Then, a monster of bandies of signal cables might disappear from the experimental area. -
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. -
MIPS Assembly Language Programming Using Qtspim
MIPS Assembly Language Programming using QtSpim Ed Jorgensen, Ph.D. Version 1.1.50 July 2019 Cover image: MIPS R3000 Custom Chip http://commons.wikimedia.org/wiki/File:RCP-NUS_01.jpg Spim is copyrighted by James Larus and distributed under a BSD license. Copyright (c) 1990-2011, James R. Larus. All rights reserved. Copyright © 2013, 2014, 2015, 2016, 2017 by Ed Jorgensen You are free: To Share — to copy, distribute and transmit the work To Remix — to adapt the work Under the following conditions: Attribution — you must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). Noncommercial — you may not use this work for commercial purposes. Share Alike — if you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one. Table of Contents 1.0 Introduction...........................................................................................................1 1.1 Additional References.........................................................................................1 2.0 MIPS Architecture Overview..............................................................................3 2.1 Architecture Overview........................................................................................3 2.2 Data Types/Sizes.................................................................................................4 2.3 Memory...............................................................................................................4 -
Loop Transformations and Parallelization
Loop transformations and parallelization Claude Tadonki LAL/CNRS/IN2P3 University of Paris-Sud [email protected] December 2010 Claude Tadonki Loop transformations and parallelization C. Tadonki – Loop transformations Introduction Most of the time, the most time consuming part of a program is on loops. Thus, loops optimization is critical in high performance computing. Depending on the target architecture, the goal of loops transformations are: improve data reuse and data locality efficient use of memory hierarchy reducing overheads associated with executing loops instructions pipeline maximize parallelism Loop transformations can be performed at different levels by the programmer, the compiler, or specialized tools. At high level, some well known transformations are commonly considered: loop interchange loop (node) splitting loop unswitching loop reversal loop fusion loop inversion loop skewing loop fission loop vectorization loop blocking loop unrolling loop parallelization Claude Tadonki Loop transformations and parallelization C. Tadonki – Loop transformations Dependence analysis Extract and analyze the dependencies of a computation from its polyhedral model is a fundamental step toward loop optimization or scheduling. Definition For a given variable V and given indexes I1, I2, if the computation of X(I1) requires the value of X(I2), then I1 ¡ I2 is called a dependence vector for variable V . Drawing all the dependence vectors within the computation polytope yields the so-called dependencies diagram. Example The dependence vectors are (1; 0); (0; 1); (¡1; 1). Claude Tadonki Loop transformations and parallelization C. Tadonki – Loop transformations Scheduling Definition The computation on the entire domain of a given loop can be performed following any valid schedule.A timing function tV for variable V yields a valid schedule if and only if t(x) > t(x ¡ d); 8d 2 DV ; (1) where DV is the set of all dependence vectors for variable V . -
Design of the RISC-V Instruction Set Architecture
Design of the RISC-V Instruction Set Architecture Andrew Waterman Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2016-1 http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-1.html January 3, 2016 Copyright © 2016, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Design of the RISC-V Instruction Set Architecture by Andrew Shell Waterman A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor David Patterson, Chair Professor Krste Asanovi´c Associate Professor Per-Olof Persson Spring 2016 Design of the RISC-V Instruction Set Architecture Copyright 2016 by Andrew Shell Waterman 1 Abstract Design of the RISC-V Instruction Set Architecture by Andrew Shell Waterman Doctor of Philosophy in Computer Science University of California, Berkeley Professor David Patterson, Chair The hardware-software interface, embodied in the instruction set architecture (ISA), is arguably the most important interface in a computer system. Yet, in contrast to nearly all other interfaces in a modern computer system, all commercially popular ISAs are proprietary. -
Computer Architectures an Overview
Computer Architectures An Overview PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 25 Feb 2012 22:35:32 UTC Contents Articles Microarchitecture 1 x86 7 PowerPC 23 IBM POWER 33 MIPS architecture 39 SPARC 57 ARM architecture 65 DEC Alpha 80 AlphaStation 92 AlphaServer 95 Very long instruction word 103 Instruction-level parallelism 107 Explicitly parallel instruction computing 108 References Article Sources and Contributors 111 Image Sources, Licenses and Contributors 113 Article Licenses License 114 Microarchitecture 1 Microarchitecture In computer engineering, microarchitecture (sometimes abbreviated to µarch or uarch), also called computer organization, is the way a given instruction set architecture (ISA) is implemented on a processor. A given ISA may be implemented with different microarchitectures.[1] Implementations might vary due to different goals of a given design or due to shifts in technology.[2] Computer architecture is the combination of microarchitecture and instruction set design. Relation to instruction set architecture The ISA is roughly the same as the programming model of a processor as seen by an assembly language programmer or compiler writer. The ISA includes the execution model, processor registers, address and data formats among other things. The Intel Core microarchitecture microarchitecture includes the constituent parts of the processor and how these interconnect and interoperate to implement the ISA. The microarchitecture of a machine is usually represented as (more or less detailed) diagrams that describe the interconnections of the various microarchitectural elements of the machine, which may be everything from single gates and registers, to complete arithmetic logic units (ALU)s and even larger elements. -
Foundations of Scientific Research
2012 FOUNDATIONS OF SCIENTIFIC RESEARCH N. M. Glazunov National Aviation University 25.11.2012 CONTENTS Preface………………………………………………….…………………….….…3 Introduction……………………………………………….…..........................……4 1. General notions about scientific research (SR)……………….……….....……..6 1.1. Scientific method……………………………….………..……..……9 1.2. Basic research…………………………………………...……….…10 1.3. Information supply of scientific research……………..….………..12 2. Ontologies and upper ontologies……………………………….…..…….…….16 2.1. Concepts of Foundations of Research Activities 2.2. Ontology components 2.3. Ontology for the visualization of a lecture 3. Ontologies of object domains………………………………..………………..19 3.1. Elements of the ontology of spaces and symmetries 3.1.1. Concepts of electrodynamics and classical gauge theory 4. Examples of Research Activity………………….……………………………….21 4.1. Scientific activity in arithmetics, informatics and discrete mathematics 4.2. Algebra of logic and functions of the algebra of logic 4.3. Function of the algebra of logic 5. Some Notions of the Theory of Finite and Discrete Sets…………………………25 6. Algebraic Operations and Algebraic Structures……………………….………….26 7. Elements of the Theory of Graphs and Nets…………………………… 42 8. Scientific activity on the example “Information and its investigation”……….55 9. Scientific research in Artificial Intelligence……………………………………..59 10. Compilers and compilation…………………….......................................……69 11. Objective, Concepts and History of Computer security…….………………..93 12. Methodological and categorical apparatus of scientific research……………114 13. Methodology and methods of scientific research…………………………….116 13.1. Methods of theoretical level of research 13.1.1. Induction 13.1.2. Deduction 13.2. Methods of empirical level of research 14. Scientific idea and significance of scientific research………………………..119 15. Forms of scientific knowledge organization and principles of SR………….121 1 15.1. Forms of scientific knowledge 15.2. -
Ultrasparc Architecture 2007
UltraSPARC Architecture 2007 One Architecture ... Multiple Innovative Implementations Draft D0.9.4, 27 Sep 2010 Privilege Levels: Hyperprivileged, Privileged, and Nonprivileged Distribution: Public Part No.No: 950-5554-15 ReleaseRevision: 1.0, Draft 2002 D0.9.4, 27 Sep 2010 Oracle Corporation 4150 Network Circle Santa Clara, CA 95054 U.S.A. 650-960-1300 ii UltraSPARC Architecture 2007 • Draft D0.9.4, 27 Sep 2010 Copyright © 2007, 2011, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. AMD, Opteron, the AMD logo, and the AMD Opteron logo are trademarks or registered trademarks of Advanced Micro Devices. Intel and Intel Xeon are trademarks or registered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. UNIX is a registered trademark licensed through X/Open Company, Ltd.. Comments and "bug reports” regarding this document are welcome; they should be submitted to email address: [email protected] iv UltraSPARC Architecture 2007 • Draft D0.9.4, 27 Sep 2010 Contents Preface. i 1 Document Overview . 1 1.1 Navigating UltraSPARC Architecture 2007 . 1 1.2 Fonts and Notational Conventions . 2 1.2.1 Implementation Dependencies . 3 1.2.2 Notation for Numbers. 3 1.2.3 Informational Notes . 3 1.3 Reporting Errors in this Specification . 4 2 Definitions . 5 3 Architecture Overview. 15 3.1 The UltraSPARC Architecture 2007. 15 3.1.1 Features. 15 3.1.2 Attributes . 16 3.1.2.1 Design Goals . -
Compiler Optimizations
Compiler Optimizations CS 498: Compiler Optimizations Fall 2006 University of Illinois at Urbana-Champaign A note about the name of optimization • It is a misnomer since there is no guarantee of optimality. • We could call the operation code improvement, but this is not quite true either since compiler transformations are not guaranteed to improve the performance of the generated code. Outline Assignment Statement Optimizations Loop Body Optimizations Procedure Optimizations Register allocation Instruction Scheduling Control Flow Optimizations Cache Optimizations Vectorization and Parallelization Advanced Compiler Design Implementation. Steven S. Muchnick, Morgan and Kaufmann Publishers, 1997. Chapters 12 - 19 Historical origins of compiler optimization "It was our belief that if FORTRAN, during its first months, were to translate any reasonable "scientific" source program into an object program only half as fast as its hand coded counterpart, then acceptance of our system would be in serious danger. This belief caused us to regard the design of the translator as the real challenge, not the simple task of designing the language."... "To this day I believe that our emphasis on object program efficiency rather than on language design was basically correct. I believe that has we failed to produce efficient programs, the widespread use of language like FORTRAN would have been seriously delayed. John Backus FORTRAN I, II, and III Annals of the History of Computing Vol. 1, No 1, July 1979 Compilers are complex systems “Like most of the early hardware and software systems, Fortran was late in delivery, and didn’t really work when it was delivered. At first people thought it would never be done. -
Processor Architectures
CS143 Handout 18 Summer 2008 30 July, 2008 Processor Architectures Handout written by Maggie Johnson and revised by Julie Zelenski. Architecture Vocabulary Let’s review a few relevant hardware definitions: register: a storage location directly on the CPU, used for temporary storage of small amounts of data during processing. memory: an array of randomly accessible memory bytes each identified by a unique address. Flat memory models, segmented memory models, and hybrid models exist which are distinguished by the way locations are referenced and potentially divided into sections. instruction set: the set of instructions that are interpreted directly in the hardware by the CPU. These instructions are encoded as bit strings in memory and are fetched and executed one by one by the processor. They perform primitive operations such as "add 2 to register i1", "store contents of o6 into memory location 0xFF32A228", etc. Instructions consist of an operation code (opcode) e.g., load, store, add, etc., and one or more operand addresses. CISC: Complex instruction set computer. Older processors fit into the CISC family, which means they have a large and fancy instruction set. In addition to a set of common operations, the instruction set has special purpose instructions that are designed for limited situations. CISC processors tend to have a slower clock cycle, but accomplish more in each cycle because of the sophisticated instructions. In writing an effective compiler back-end for a CISC processor, many issues revolve around recognizing how to make effective use of the specialized instructions. RISC: Reduced instruction set computer. Many modern processors are in the RISC family, which means they have a relatively lean instruction set, containing mostly simple, general-purpose instructions.