Heterogeneous Multicore Openamp
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Microprocessor
MICROPROCESSOR www.MPRonline.com THE REPORTINSIDER’S GUIDE TO MICROPROCESSOR HARDWARE EEMBC’S MULTIBENCH ARRIVES CPU Benchmarks: Not Just For ‘Benchmarketing’ Any More By Tom R. Halfhill {7/28/08-01} Imagine a world without measurements or statistical comparisons. Baseball fans wouldn’t fail to notice that a .300 hitter is better than a .100 hitter. But would they welcome a trade that sends the .300 hitter to Cleveland for three .100 hitters? System designers and software developers face similar quandaries when making trade-offs EEMBC’s role has evolved over the years, and Multi- with multicore processors. Even if a dual-core processor Bench is another step. Originally, EEMBC was conceived as an appears to be better than a single-core processor, how much independent entity that would create benchmark suites and better is it? Twice as good? Would a quad-core processor be certify the scores for accuracy, allowing vendors and customers four times better? Are more cores worth the additional cost, to make valid comparisons among embedded microproces- design complexity, power consumption, and programming sors. (See MPR 5/1/00-02, “EEMBC Releases First Bench- difficulty? marks.”) EEMBC still serves that role. But, as it turns out, most The Embedded Microprocessor Benchmark Consor- EEMBC members don’t openly publish their scores. Instead, tium (EEMBC) wants to help answer those questions. they disclose scores to prospective customers under an NDA or EEMBC’s MultiBench 1.0 is a new benchmark suite for use the benchmarks for internal testing and analysis. measuring the throughput of multiprocessor systems, Partly for this reason, MPR rarely cites EEMBC scores including those built with multicore processors. -
Programmers' Tool Chain
Reduce the complexity of programming multicore ++ Offload™ for PlayStation®3 | Offload™ for Cell Broadband Engine™ | Offload™ for Embedded | Custom C and C++ Compilers | Custom Shader Language Compiler www.codeplay.com It’s a risk to underestimate the complexity of programming multicore applications Software developers are now presented with a rapidly-growing range of different multi-core processors. The common feature of many of these processors is that they are difficult and error-prone to program with existing tools, give very unpredictable performance, and that incompatible, complex programming models are used. Codeplay develop compilers and programming tools with one primary goal - to make it easy for programmers to achieve big performance boosts with multi-core processors, but without needing bigger, specially-trained, expensive development teams to get there. Introducing Codeplay Based in Edinburgh, Scotland, Codeplay Software Limited was founded by veteran games developer Andrew Richards in 2002 with funding from Jez San (the founder of Argonaut Games and ARC International). Codeplay introduced their first product, VectorC, a highly optimizing compiler for x86 PC and PlayStation®2, in 2003. In 2004 Codeplay further developed their business by offering services to processor developers to provide them with compilers and programming tools for their new and unique architectures, using VectorC’s highly retargetable compiler technology. Realising the need for new multicore tools Codeplay started the development of the company’s latest product, the Offload™ C++ Multicore Programming Platform. In October 2009 Offload™: Community Edition was released as a free-to-use tool for PlayStation®3 programmers. Experience and Expertise Codeplay have developed compilers and software optimization technology since 1999. -
Multiprocessing Contents
Multiprocessing Contents 1 Multiprocessing 1 1.1 Pre-history .............................................. 1 1.2 Key topics ............................................... 1 1.2.1 Processor symmetry ...................................... 1 1.2.2 Instruction and data streams ................................. 1 1.2.3 Processor coupling ...................................... 2 1.2.4 Multiprocessor Communication Architecture ......................... 2 1.3 Flynn’s taxonomy ........................................... 2 1.3.1 SISD multiprocessing ..................................... 2 1.3.2 SIMD multiprocessing .................................... 2 1.3.3 MISD multiprocessing .................................... 3 1.3.4 MIMD multiprocessing .................................... 3 1.4 See also ................................................ 3 1.5 References ............................................... 3 2 Computer multitasking 5 2.1 Multiprogramming .......................................... 5 2.2 Cooperative multitasking ....................................... 6 2.3 Preemptive multitasking ....................................... 6 2.4 Real time ............................................... 7 2.5 Multithreading ............................................ 7 2.6 Memory protection .......................................... 7 2.7 Memory swapping .......................................... 7 2.8 Programming ............................................. 7 2.9 See also ................................................ 8 2.10 References ............................................. -
Multiprocessing and Scalability
Multiprocessing and Scalability A.R. Hurson Computer Science and Engineering The Pennsylvania State University 1 Multiprocessing and Scalability Large-scale multiprocessor systems have long held the promise of substantially higher performance than traditional uni- processor systems. However, due to a number of difficult problems, the potential of these machines has been difficult to realize. This is because of the: Fall 2004 2 Multiprocessing and Scalability Advances in technology ─ Rate of increase in performance of uni-processor, Complexity of multiprocessor system design ─ This drastically effected the cost and implementation cycle. Programmability of multiprocessor system ─ Design complexity of parallel algorithms and parallel programs. Fall 2004 3 Multiprocessing and Scalability Programming a parallel machine is more difficult than a sequential one. In addition, it takes much effort to port an existing sequential program to a parallel machine than to a newly developed sequential machine. Lack of good parallel programming environments and standard parallel languages also has further aggravated this issue. Fall 2004 4 Multiprocessing and Scalability As a result, absolute performance of many early concurrent machines was not significantly better than available or soon- to-be available uni-processors. Fall 2004 5 Multiprocessing and Scalability Recently, there has been an increased interest in large-scale or massively parallel processing systems. This interest stems from many factors, including: Advances in integrated technology. Very -
Design of a Message Passing Interface for Multiprocessing with Atmel Microcontrollers
DESIGN OF A MESSAGE PASSING INTERFACE FOR MULTIPROCESSING WITH ATMEL MICROCONTROLLERS A Design Project Report Presented to the Engineering Division of the Graduate School of Cornell University in Partial Fulfillment of the Requirements of the Degree of Master of Engineering (Electrical) by Kalim Moghul Project Advisor: Dr. Bruce R. Land Degree Date: May 2006 Abstract Master of Electrical Engineering Program Cornell University Design Project Report Project Title: Design of a Message Passing Interface for Multiprocessing with Atmel Microcontrollers Author: Kalim Moghul Abstract: Microcontrollers are versatile integrated circuits typically incorporating a microprocessor, memory, and I/O ports on a single chip. These self-contained units are central to embedded systems design where low-cost, dedicated processors are often preferred over general-purpose processors. Embedded designs may require several microcontrollers, each with a specific task. Efficient communication is central to such systems. The focus of this project is the design of a communication layer and application programming interface for exchanging messages among microcontrollers. In order to demonstrate the library, a small- scale cluster computer is constructed using Atmel ATmega32 microcontrollers as processing nodes and an Atmega16 microcontroller for message routing. The communication library is integrated into aOS, a preemptive multitasking real-time operating system for Atmel microcontrollers. Report Approved by Project Advisor:__________________________________ Date:___________ Executive Summary Microcontrollers are versatile integrated circuits typically incorporating a microprocessor, memory, and I/O ports on a single chip. These self-contained units are central to embedded systems design where low-cost, dedicated processors are often preferred over general-purpose processors. Some embedded designs may incorporate several microcontrollers, each with a specific task. -
Piotr Warczak Quarter: Fall 2011 Student ID: 99XXXXX Credit: 2 Grading: Decimal
CSS 600: Independent Study Contract Final Report Student: Piotr Warczak Quarter: Fall 2011 Student ID: 99XXXXX Credit: 2 Grading: Decimal Independent Study Title The GPU version of the MASS library. Focus and Goals The current version of the MASS library is written in java programming language and combines the power of every computing node on the network by utilizing both multithreading and multiprocessing. The new version will be implemented in C and C++. It will also support multithreading and multiprocessing and finally CUDA parallel computing architecture utilizing both single and multiple devices. This version will harness the power of the GPU a general purpose parallel processor. My specific goals of the past quarter were: • To create a baseline in C language using Wave2D application as an example. • To implement single thread Wave2D • To implement multithreading Wave2D • To implement CUDA enabled Wave2D Work Completed During this quarter I have created Wave2D application in C language using a single thread, multithreaded and CUDA enabled application. The reason for this is that CUDA is an extension of C and thus we need to create baseline against which other versions of the program can be measured. This baseline illustrates how much faster the CUDA enabled program is versus single thread and multithreaded versions. Furthermore, once the GPU version of the MASS library is implemented, we can compare the results to identify if there is any difference in program’s total execution time due to the potential MASS library overhead. And if any major delays in execution are found, the problem ares will be identified and corrected. -
Message Passing Fundamentals
1. Message Passing Fundamentals Message Passing Fundamentals As a programmer, you may find that you need to solve ever larger, more memory intensive problems, or simply solve problems with greater speed than is possible on a serial computer. You can turn to parallel programming and parallel computers to satisfy these needs. Using parallel programming methods on parallel computers gives you access to greater memory and Central Processing Unit (CPU) resources not available on serial computers. Hence, you are able to solve large problems that may not have been possible otherwise, as well as solve problems more quickly. One of the basic methods of programming for parallel computing is the use of message passing libraries. These libraries manage transfer of data between instances of a parallel program running (usually) on multiple processors in a parallel computing architecture. The topics to be discussed in this chapter are The basics of parallel computer architectures. The difference between domain and functional decomposition. The difference between data parallel and message passing models. A brief survey of important parallel programming issues. 1.1. Parallel Architectures Parallel Architectures Parallel computers have two basic architectures: distributed memory and shared memory. Distributed memory parallel computers are essentially a collection of serial computers (nodes) working together to solve a problem. Each node has rapid access to its own local memory and access to the memory of other nodes via some sort of communications network, usually a proprietary high-speed communications network. Data are exchanged between nodes as messages over the network. In a shared memory computer, multiple processor units share access to a global memory space via a high-speed memory bus. -
Composable Multi-Threading and Multi-Processing for Numeric Libraries
18 PROC. OF THE 17th PYTHON IN SCIENCE CONF. (SCIPY 2018) Composable Multi-Threading and Multi-Processing for Numeric Libraries Anton Malakhov‡∗, David Liu‡, Anton Gorshkov‡†, Terry Wilmarth‡ https://youtu.be/HKjM3peINtw F Abstract—Python is popular among scientific communities that value its sim- Scaling parallel programs is challenging. There are two fun- plicity and power, especially as it comes along with numeric libraries such as damental laws which mathematically describe and predict scala- [NumPy], [SciPy], [Dask], and [Numba]. As CPU core counts keep increasing, bility of a program: Amdahl’s Law and Gustafson-Barsis’ Law these modules can make use of many cores via multi-threading for efficient [AGlaws]. According to Amdahl’s Law, speedup is limited by multi-core parallelism. However, threads can interfere with each other leading the serial portion of the work, which effectively puts a limit on to overhead and inefficiency if used together in a single application on machines scalability of parallel processing for a fixed-size job. Python is with a large number of cores. This performance loss can be prevented if all multi-threaded modules are coordinated. This paper continues the work started especially vulnerable to this because it makes the serial part of in [AMala16] by introducing more approaches to coordination for both multi- the same code much slower compared to implementations in other threading and multi-processing cases. In particular, we investigate the use of languages due to its deeply dynamic and interpretative nature. In static settings, limiting the number of simultaneously active [OpenMP] parallel addition, the GIL serializes operations that could be potentially regions, and optional parallelism with Intel® Threading Building Blocks (Intel® executed in parallel, further adding to the serial portion of a [TBB]). -
14. Parallel Computing 14.1 Introduction 14.2 Independent
14. Parallel Computing 14.1 Introduction This chapter describes approaches to problems to which multiple computing agents are applied simultaneously. By "parallel computing", we mean using several computing agents concurrently to achieve a common result. Another term used for this meaning is "concurrency". We will use the terms parallelism and concurrency synonymously in this book, although some authors differentiate them. Some of the issues to be addressed are: What is the role of parallelism in providing clear decomposition of problems into sub-problems? How is parallelism specified in computation? How is parallelism effected in computation? Is parallel processing worth the extra effort? 14.2 Independent Parallelism Undoubtedly the simplest form of parallelism entails computing with totally independent tasks, i.e. there is no need for these tasks to communicate. Imagine that there is a large field to be plowed. It takes a certain amount of time to plow the field with one tractor. If two equal tractors are available, along with equally capable personnel to man them, then the field can be plowed in about half the time. The field can be divided in half initially and each tractor given half the field to plow. One tractor doesn't get into another's way if they are plowing disjoint halves of the field. Thus they don't need to communicate. Note however that there is some initial overhead that was not present with the one-tractor model, namely the need to divide the field. This takes some measurements and might not be that trivial. In fact, if the field is relatively small, the time to do the divisions might be more than the time saved by the second tractor. -
Educational Goals for Embedded Systems in the Multicore Era
AC 2009-414: EDUCATIONAL GOALS FOR EMBEDDED SYSTEMS IN THE MULTICORE ERA James Holt, Freescale Semiconductor, Inc. Jim leads the Multicore Design Evaluation team for Freescale’s NMG/NSD division. Jim has 27 years of industry experience focused on distributed systems, microprocessor and SoC architecture, design verification, and optimization. Jim is an IEEE Senior Member, and is a board member for the Multicore Association. He is also chair of the Integrated Systems & Circuits Science area for the Semiconductor Research Corporation (SRC), and chair of the Multicore Resource API Working group for the Multicore Association. Jim earned a Ph.D. in Electrical and Computer Engineering from the University of Texas at Austin, and an MS in Computer Science from Texas State University. Hongchi Shi, Texas State University, San Marcos Hongchi Shi is Professor and Chair of the Computer Science Department at Texas State University-San Marcos. Prior to joining Texas State University, he has been an Assistant/Associate/Full Professor of Computer Science and Electrical and Computer Engineering at the University of Missouri. He obtained his BS degree and MS degree in Computer Science and Engineering from Beijing University of Aeronautics and Astronautics in 1983 and 1986, respectively. He obtained his PhD degree in Computer and Information Sciences from the University of Florida in 1994. Hongchi Shi's research interests include parallel and distributed computing, wireless sensor networks, neural networks, and image processing. He has served on many organizing and/or technical program committees of international conferences in his research areas. He is a member of ACM and a senior member of IEEE. -
Efficient Parallel Approaches to Financial Derivatives and Rapid Stochastic Convergence
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-2015 Efficient Parallel Approaches to Financial Derivatives and Rapid Stochastic Convergence Mario Harper Utah State University Follow this and additional works at: https://digitalcommons.usu.edu/gradreports Part of the Business Commons Recommended Citation Harper, Mario, "Efficient Parallel Approaches to Financial Derivatives and Rapid Stochastic Convergence" (2015). All Graduate Plan B and other Reports. 519. https://digitalcommons.usu.edu/gradreports/519 This Thesis is brought to you for free and open access by the Graduate Studies at DigitalCommons@USU. It has been accepted for inclusion in All Graduate Plan B and other Reports by an authorized administrator of DigitalCommons@USU. For more information, please contact [email protected]. EFFICIENT PARALLEL APPROACHES TO FINANCIAL DERIVATIVES AND RAPID STOCHASTIC CONVERGENCE by Mario Y. Harper A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in Financial Economics Approved: Dr. Tyler Brough Dr. Jason Smith Major Professor Committee Member Dr. Jared DeLisle Dr. Richard Inouye Committee Member Associate Dean of the School of Gradu- ate Studies UTAH STATE UNIVERSITY Logan, Utah 2015 ii Copyright c Mario Y. Harper 2015 All Rights Reserved iii Abstract Efficient Parallel Approaches to Financial Derivatives and Rapid Stochastic Convergence by Mario Y. Harper, Master of Science Utah State University, 2015 Major Professor: Dr. Tyler Brough Department: Economics and Finance This Thesis explores the use of different programming paradigms, platforms and lan- guages to maximize the speed of convergence in Financial Derivatives Models. The study focuses on the strengths and drawbacks of various libraries and their associated languages as well as the difficulty of porting code into massively parallel processes. -
Performance Analysis and Tuning in Multicore Environments
Computer Architecture and Operative Systems Department Master in High Performance Computing Performance Analysis and Tuning in Multicore Environments MSc research Project for the “Master in High Performance Computing” submitted by MARIO ADOLFO ZAVALA JIMENEZ, advised by Eduardo Galobardes. Dissertation done at Escola Tècnica Superior d’Enginyeria (Computer Architecture and Operative Systems Department). 2009 Abstract Performance analysis is the task of monitor the behavior of a program execution. The main goal is to find out the possible adjustments that might be done in order improve the performance. To be able to get that improvement it is necessary to find the different causes of overhead. Nowadays we are already in the multicore era, but there is a gap between the level of development of the two main divisions of multicore technology (hardware and software). When we talk about multicore we are also speaking of shared memory systems, on this master thesis we talk about the issues involved on the performance analysis and tuning of applications running specifically in a shared Memory system. We move one step ahead to take the performance analysis to another level by analyzing the applications structure and patterns. We also present some tools specifically addressed to the performance analysis of OpenMP multithread application. At the end we present the results of some experiments performed with a set of OpenMP scientific application. Keywords: Performance analysis, application patterns, Tuning, Multithread, OpenMP, Multicore. Resumen Análisis de rendimiento es el área de estudio encargada de monitorizar el comportamiento de la ejecución de programas informáticos. El principal objetivo es encontrar los posibles ajustes que serán necesarios para mejorar el rendimiento.