Evaluating Signal Processing and Multimedia Applications on SIMD, VLIW and Superscalar Architectures
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GPU Implementation Over IPTV Software Defined Networking
Esmeralda Hysenbelliu. Int. Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 7, Issue 8, ( Part -1) August 2017, pp.41-45 RESEARCH ARTICLE OPEN ACCESS GPU Implementation over IPTV Software Defined Networking Esmeralda Hysenbelliu* Information Technology Faculty, Polytechnic University of Tirana, Sheshi “Nënë Tereza”, Nr.4, Tirana, Albania Corresponding author: Esmeralda Hysenbelliu ABSTRACT One of the most important issue in IPTV Software defined Network is Bandwidth Issue and Quality of Service at the client side. Decidedly, it is required high level quality of images in low bandwidth and for this reason it is needed different transcoding standards (Compression of image as much as it is possible without destroying the quality of it) as H.264, H265, VP8 and VP9. During a test performed in SMC IPTV SDN Cloud Network, it was observed that with a server HP ProLiant DL380 g6 with two physical processors there was not possible to transcode in format H.264 more than 30 channels simultaneously because CPU’s achieved 100%. This is the reason why it was immediately needed to use Graphic Processing Units called GPU’s which offer high level images processing. After GPU superscalar processor was integrated and done functional via module NVENC of FFEMPEG Program, number of channels transcoded simultaneously was tremendous increased (more than 100 channels). The aim of this paper is to real implement GPU superscalar processors in IPTV Cloud Networks by achieving improvement of performance to more than 60%. Keywords - GPU superscalar processor, Performance Improvement, NVENC, CUDA --------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: 01 -05-2017 Date of acceptance: 19-08-2017 --------------------------------------------------------------------------------------------------------------------------------------- I. -
Computer Hardware Architecture Lecture 4
Computer Hardware Architecture Lecture 4 Manfred Liebmann Technische Universit¨atM¨unchen Chair of Optimal Control Center for Mathematical Sciences, M17 [email protected] November 10, 2015 Manfred Liebmann November 10, 2015 Reading List • Pacheco - An Introduction to Parallel Programming (Chapter 1 - 2) { Introduction to computer hardware architecture from the parallel programming angle • Hennessy-Patterson - Computer Architecture - A Quantitative Approach { Reference book for computer hardware architecture All books are available on the Moodle platform! Computer Hardware Architecture 1 Manfred Liebmann November 10, 2015 UMA Architecture Figure 1: A uniform memory access (UMA) multicore system Access times to main memory is the same for all cores in the system! Computer Hardware Architecture 2 Manfred Liebmann November 10, 2015 NUMA Architecture Figure 2: A nonuniform memory access (UMA) multicore system Access times to main memory differs form core to core depending on the proximity of the main memory. This architecture is often used in dual and quad socket servers, due to improved memory bandwidth. Computer Hardware Architecture 3 Manfred Liebmann November 10, 2015 Cache Coherence Figure 3: A shared memory system with two cores and two caches What happens if the same data element z1 is manipulated in two different caches? The hardware enforces cache coherence, i.e. consistency between the caches. Expensive! Computer Hardware Architecture 4 Manfred Liebmann November 10, 2015 False Sharing The cache coherence protocol works on the granularity of a cache line. If two threads manipulate different element within a single cache line, the cache coherency protocol is activated to ensure consistency, even if every thread is only manipulating its own data. -
Superscalar Fall 2020
CS232 Superscalar Fall 2020 Superscalar What is superscalar - A superscalar processor has more than one set of functional units and executes multiple independent instructions during a clock cycle by simultaneously dispatching multiple instructions to different functional units in the processor. - You can think of a superscalar processor as there are more than one washer, dryer, and person who can fold. So, it allows more throughput. - The order of instruction execution is usually assisted by the compiler. The hardware and the compiler assure that parallel execution does not violate the intent of the program. - Example: • Ordinary pipeline: four stages (Fetch, Decode, Execute, Write back), one clock cycle per stage. Executing 6 instructions take 9 clock cycles. I0: F D E W I1: F D E W I2: F D E W I3: F D E W I4: F D E W I5: F D E W cc: 1 2 3 4 5 6 7 8 9 • 2-degree superscalar: attempts to process 2 instructions simultaneously. Executing 6 instructions take 6 clock cycles. I0: F D E W I1: F D E W I2: F D E W I3: F D E W I4: F D E W I5: F D E W cc: 1 2 3 4 5 6 Limitations of Superscalar - The above example assumes that the instructions are independent of each other. So, it’s easily to push them into the pipeline and superscalar. However, instructions are usually relevant to each other. Just like the hazards in pipeline, superscalar has limitations too. - There are several fundamental limitations the system must cope, which are true data dependency, procedural dependency, resource conflict, output dependency, and anti- dependency. -
The Multiscalar Architecture
THE MULTISCALAR ARCHITECTURE by MANOJ FRANKLIN A thesis submitted in partial ful®llment of the requirements for the degree of DOCTOR OF PHILOSOPHY (Computer Sciences) at the UNIVERSITY OF WISCONSIN Ð MADISON 1993 THE MULTISCALAR ARCHITECTURE Manoj Franklin Under the supervision of Associate Professor Gurindar S. Sohi at the University of Wisconsin-Madison ABSTRACT The centerpiece of this thesis is a new processing paradigm for exploiting instruction level parallelism. This paradigm, called the multiscalar paradigm, splits the program into many smaller tasks, and exploits ®ne-grain parallelism by executing multiple, possibly (control and/or data) depen- dent tasks in parallel using multiple processing elements. Splitting the instruction stream at statically determined boundaries allows the compiler to pass substantial information about the tasks to the hardware. The processing paradigm can be viewed as extensions of the superscalar and multiprocess- ing paradigms, and shares a number of properties of the sequential processing model and the data¯ow processing model. The multiscalar paradigm is easily realizable, and we describe an implementation of the multis- calar paradigm, called the multiscalar processor. The central idea here is to connect multiple sequen- tial processors, in a decoupled and decentralized manner, to achieve overall multiple issue. The mul- tiscalar processor supports speculative execution, allows arbitrary dynamic code motion (facilitated by an ef®cient hardware memory disambiguation mechanism), exploits communication localities, and does all of these with hardware that is fairly straightforward to build. Other desirable aspects of the implementation include decentralization of the critical resources, absence of wide associative searches, and absence of wide interconnection/data paths. -
Trends in Processor Architecture
A. González Trends in Processor Architecture Trends in Processor Architecture Antonio González Universitat Politècnica de Catalunya, Barcelona, Spain 1. Past Trends Processors have undergone a tremendous evolution throughout their history. A key milestone in this evolution was the introduction of the microprocessor, term that refers to a processor that is implemented in a single chip. The first microprocessor was introduced by Intel under the name of Intel 4004 in 1971. It contained about 2,300 transistors, was clocked at 740 KHz and delivered 92,000 instructions per second while dissipating around 0.5 watts. Since then, practically every year we have witnessed the launch of a new microprocessor, delivering significant performance improvements over previous ones. Some studies have estimated this growth to be exponential, in the order of about 50% per year, which results in a cumulative growth of over three orders of magnitude in a time span of two decades [12]. These improvements have been fueled by advances in the manufacturing process and innovations in processor architecture. According to several studies [4][6], both aspects contributed in a similar amount to the global gains. The manufacturing process technology has tried to follow the scaling recipe laid down by Robert N. Dennard in the early 1970s [7]. The basics of this technology scaling consists of reducing transistor dimensions by a factor of 30% every generation (typically 2 years) while keeping electric fields constant. The 30% scaling in the dimensions results in doubling the transistor density (doubling transistor density every two years was predicted in 1975 by Gordon Moore and is normally referred to as Moore’s Law [21][22]). -
Intro Evolution of Superscalar Processor
Superscalar Processors Superscalar Processors vs. VLIW • 7.1 Introduction • 7.2 Parallel decoding • 7.3 Superscalar instruction issue • 7.4 Shelving • 7.5 Register renaming • 7.6 Parallel execution • 7.7 Preserving the sequential consistency of instruction execution • 7.8 Preserving the sequential consistency of exception processing • 7.9 Implementation of superscalar CISC processors using a superscalar RISC core • 7.10 Case studies of superscalar processors TECH Computer Science CH01 Superscalar Processor: Intro Emergence and spread of superscalar processors • Parallel Issue • Parallel Execution • {Hardware} Dynamic Instruction Scheduling • Currently the predominant class of processors 4Pentium 4PowerPC 4UltraSparc 4AMD K5- 4HP PA7100- 4DEC α Evolution of superscalar processor Specific tasks of superscalar processing Parallel decoding {and Dependencies check} Decoding and Pre-decoding • What need to be done • Superscalar processors tend to use 2 and sometimes even 3 or more pipeline cycles for decoding and issuing instructions • >> Pre-decoding: 4shifts a part of the decode task up into loading phase 4resulting of pre-decoding f the instruction class f the type of resources required for the execution f in some processor (e.g. UltraSparc), branch target addresses calculation as well 4the results are stored by attaching 4-7 bits • + shortens the overall cycle time or reduces the number of cycles needed The principle of perdecoding Number of perdecode bits used Specific tasks of superscalar processing: Issue 7.3 Superscalar instruction issue • How and when to send the instruction(s) to EU(s) Instruction issue policies of superscalar processors: Issue policies ---Performance, tread-----Æ Issue rate {How many instructions/cycle} Issue policies: Handing Issue Blockages • CISC about 2 • RISC: Issue stopped by True dependency Issue order of instructions • True dependency Æ (Blocked: need to wait) Aligned vs. -
Parallel Architectures MICHAEL J
Parallel Architectures MICHAEL J. FLYNN AND KEVIN W. RUDD Stanford University ^[email protected]&; ^[email protected]& PARALLEL ARCHITECTURES currently performing different phases of processing an instruction. This does not Parallel or concurrent operation has achieve concurrency of execution (with many different forms within a computer system. Using a model based on the multiple actions being taken on objects) different streams used in the computa- but does achieve a concurrency of pro- tion process, we represent some of the cessing—an improvement in efficiency different kinds of parallelism available. upon which almost all processors de- A stream is a sequence of objects such pend today. as data, or of actions such as instruc- Techniques that exploit concurrency tions. Each stream is independent of all of execution, often called instruction- other streams, and each element of a level parallelism (ILP), are also com- stream can consist of one or more ob- mon. Two architectures that exploit ILP jects or actions. We thus have four com- are superscalar and VLIW (very long binations that describe most familiar parallel architectures: instruction word). These techniques schedule different operations to execute (1) SISD: single instruction, single data concurrently based on analyzing the de- stream. This is the traditional uni- pendencies between the operations processor [Figure 1(a)]. within the instruction stream—dynami- (2) SIMD: single instruction, multiple cally at run time in a superscalar pro- data stream. This includes vector cessor and statically at compile time in processors as well as massively par- a VLIW processor. Both ILP approaches allel processors [Figure 1(b)]. -
Implementing Elliptic Curve Cryptography (A Narrow Survey)
Implementing Elliptic Curve Cryptography (a narrow survey) Institute of Computing – UNICAMP Campinas, Brazil April 2005 Darrel Hankerson Auburn University Implementing ECC – 1/110 Overview Objective: sample selected topics of practical interest. Talk will favor: I Software solutions on general-purpose processors rather than dedicated hardware. I Techniques with broad applicability. I Methods targeted to standardized curves. Goals: I Present proposed methods in context. I Limit coverage of technical details (but “implementing” necessarily involves platform considerations). Implementing ECC – 2/110 Focus: higher-performance processors “Higher-performance” includes processors commonly associated with workstations, but also found in surprisingly small portable devices. Sun and IBM workstations RIM pager circa 1999 SPARC or Intel x86 (Pentium) Intel x86 (custom 386) 256 MB – 8 GB 2 MB “disk”, 304 KB RAM 0.5 GHz – 3 GHz 10 MHz, single AA battery heats entire building fits in shirt pocket Implementing ECC – 3/110 Optimizing ECC Elliptic Curve Digital Signature Algorithm (ECDSA) Random number Big number and Curve generation modular arithmetic arithmetic Fq field arithmetic General categories of optimization efforts: 1. Field-level optimizations. 2. Curve-level optimizations. 3. Protocol-level optimizations. Constraints: security requirements, hardware limitations, bandwidth, interoperability, and patents. Implementing ECC – 4/110 Optimizing ECC... 1. Field-level optimizations. I Choose fields with fast multiplication and inversion. I Use special-purpose hardware (cryptographic coprocessors, DSP, floating-point, SIMD). 2. Curve-level optimizations. I Reduce the number of point additions (windowing). I Reduce the number of field inversions (projective coords). I Replace point doubles (endomorphism methods). 3. Protocol-level optimizations. I Develop efficient protocols. I Choose methods and protocols that favor your computations or hardware. -
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. -
2 the VIS Instruction Set Pdist Instruction
The VISTM Instruction Set Version 1.0 June 2002 A White Paper This document provides an overview of the Visual Instruction Set. ® 4150 Network Circle Santa Clara, CA 95054 USA www.sun.com Copyright © 2002 Sun Microsystems, Inc. All Rights reserved. THE INFORMATION CONTAINED IN THIS DOCUMENT IS PROVIDED"AS IS" WITHOUT ANY EXPRESS REPRESENTATIONS OR WARRANTIES. IN ADDITION, SUN MICROSYSTEMS, INC. DISCLAIMS ALL IMPLIED REPRESENTATIONS AND WARRANTIES, INCLUDING ANY WARRANTY OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NON-INFRINGEMENT OF THIRD PARTY INTELLECTUAL PROPERTY RIGHTS. This document contains proprietary information of Sun Microsystems, Inc. or under license from third parties. No part of this document may be reproduced in any form or by any means or transferred to any third party without the prior written consent of Sun Microsystems, Inc. Sun, Sun Microsystems, the Sun Logo, VIS, Java, and mediaLib are trademarks or registered trademarks of Sun Microsystems, Inc. in the United States and other countries. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. in the United States and other countries. Products bearing SPARC trademarks are based upon an architecture developed by Sun Microsystems, Inc. UNIX is a registered trademark in the United States and other countries, exclusively licensed through x/Open Company Ltd. The information contained in this document is not designed or intended for use in on-line control of aircraft, air traffic, aircraft navigation or aircraft communications; or in the design, construction, operation or maintenance of any nuclear facility. Sun disclaims any express or implied warranty of fitness for such uses. -
Modern Processor Design: Fundamentals of Superscalar
Fundamentals of Superscalar Processors John Paul Shen Intel Corporation Mikko H. Lipasti University of Wisconsin WAVELAND PRESS, INC. Long Grove, Illinois To Our parents: Paul and Sue Shen Tarja and Simo Lipasti Our spouses: Amy C. Shen Erica Ann Lipasti Our children: Priscilla S. Shen, Rachael S. Shen, and Valentia C. Shen Emma Kristiina Lipasti and Elias Joel Lipasti For information about this book, contact: Waveland Press, Inc. 4180 IL Route 83, Suite 101 Long Grove, IL 60047-9580 (847) 634-0081 info @ waveland.com www.waveland.com Copyright © 2005 by John Paul Shen and Mikko H. Lipasti 2013 reissued by Waveland Press, Inc. 10-digit ISBN 1-4786-0783-1 13-digit ISBN 978-1-4786-0783-0 All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without permission in writing from the publisher. Printed in the United States of America 7 6 5 4 3 2 1 Table of Contents PrefaceAbout the Authors x ix 1 Processor Design 1 1.1 The Evolution of Microprocessors 2 1.21.2.1 Instruction Digital Set Systems Processor Design Design 44 1.2.2 Architecture,Realization Implementation, and 5 1.2.3 Instruction Set Architecture 6 1.2.4 Dynamic-Static Interface 8 1.3 Principles of Processor Performance 10 1.3.1 Processor Performance Equation 10 1.3.2 Processor Performance Optimizations 11 1.3.3 Performance Evaluation Method 13 1.4 Instruction-Level Parallel Processing 16 1.4.1 From Scalar to Superscalar 16 1.4.2 Limits of Instruction-Level Parallelism 24 1.51.4.3 Machines Summary for Instruction-Level -
Multithreading
CS 152 Computer Architecture and Engineering CS252 Graduate Computer Architecture Lecture 14 – Multithreading Krste Asanovic Electrical Engineering and Computer Sciences University of California at Berkeley http://www.eecs.berkeley.edu/~krste http://inst.eecs.berkeley.edu/~cs152 Last Time Lecture 13: VLIW § In a classic VLIW, compiler is responsible for avoiding all hazards -> simple hardware, complex compiler. § Later VLIWs added more dynamic hardware interlocks, which reduce relative hardware benefits § Use loop unrolling and software pipelining for loops, trace scheduling for more irregular code § Static scheduling difficult in presence of unpredictable branches and variable latency memory § VLIW has failed in general-purpose computing, but still used in deeply embedded processors and DSPs 2 Thread-Level Parallelism (TLP) § Difficult to continue to extract instruction-level parallelism (ILP) from a single sequential thread of control § Many workloads can make use of thread-level parallelism: – TLP from multiprogramming (run independent sequential jobs) – TLP from multithreaded applications (run one job faster using parallel threads) § Multithreading uses TLP to improve utilization of a single processor 3 Multithreading How can we guarantee no dependencies between instructions in a pipeline? One way is to interleave execution of instructions from different program threads on same pipeline Interleave 4 threads, T1-T4, on non-bypassed 5-stage pipe t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 T1:LD x1,0(x2) F D X M W Prior instruction in a T2:ADD x7,x1,x4