The Anatomy of the ARM Cortex-M0+ Processor
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08-02-2021 Agenda Packet.Pdf
AGENDA DELANO CITY COUNCIL REGULAR MEETING August 2, 2021 DELANO CITY HALL, 1015 – 11th Avenue 5:30 P.M. IN ACCORDANCE WITH THE GOVERNOR NEWSOM’S EXECUTIVE ORDER #N-08-21, THIS MEETING WILL BE CONDUCTED FULLY VIA TELECONFERENCE, DUE TO THE CURRENT RESTRICTIONS BY SAID ORDER AND CENTERS FOR DISEASES CONTROL AND PREVENTION (CDC) GUIDELINES. THE PUBLIC WILL HAVE ACCESS TO CALL IN, LISTEN TO THE MEETING AND PROVIDE PUBLIC COMMENT. IN ACCORDANCE WITH GOVERNOR NEWSOM’S EXECUTIVE ORDER N-08-21, THERE WILL NOT BE A PHYSICAL LOCATION FROM WHICH THE PUBLIC MAY ATTEND. IN ORDER TO CALL INTO TH E MEETING PLEASE SEE THE DIRECTIONS BELOW. CALL TO ORDER INVOCATION FLAG SALUTE ROLL CALL PRESENTATIONS AND AWARDS Featured Pet – Tabitha PUBLIC COMMENT: The public may address the Council on items which do not appear on the agenda. The Council cannot respond nor take action on items that do not appear on the agenda but may refer the item to staff for further study or for placement on a future agenda. Comments are limited to 3 minutes for each person and 15 minutes on each subject. Please state your name and address for the record. CONSENT AGENDA: The Consent Agenda consists of items that in staff’s opinion are routine and non-controversial. These items are approved in one motion unless a Councilmember or member of the public removes a particular item. 1) Authorization to waive the reading of any ordinance in its entirety and consenting to the reading of such ordinances by title only 2) Warrant Register in the amount of $3,044,398.54 3) Minutes of regular City Council Meeting of July 19, 2021 4) Acceptance and Approval of the City of Delano Quarterly Investment Report 5) Resolution adopting the 2020-2021 Kern Multi-Jurisdiction Hazard Mitigation Plan (MJHMP) 6) Approval of agreement with Youth Educational Sports, Inc. -
45-Year CPU Evolution: One Law and Two Equations
45-year CPU evolution: one law and two equations Daniel Etiemble LRI-CNRS University Paris Sud Orsay, France [email protected] Abstract— Moore’s law and two equations allow to explain the a) IC is the instruction count. main trends of CPU evolution since MOS technologies have been b) CPI is the clock cycles per instruction and IPC = 1/CPI is the used to implement microprocessors. Instruction count per clock cycle. c) Tc is the clock cycle time and F=1/Tc is the clock frequency. Keywords—Moore’s law, execution time, CM0S power dissipation. The Power dissipation of CMOS circuits is the second I. INTRODUCTION equation (2). CMOS power dissipation is decomposed into static and dynamic powers. For dynamic power, Vdd is the power A new era started when MOS technologies were used to supply, F is the clock frequency, ΣCi is the sum of gate and build microprocessors. After pMOS (Intel 4004 in 1971) and interconnection capacitances and α is the average percentage of nMOS (Intel 8080 in 1974), CMOS became quickly the leading switching capacitances: α is the activity factor of the overall technology, used by Intel since 1985 with 80386 CPU. circuit MOS technologies obey an empirical law, stated in 1965 and 2 Pd = Pdstatic + α x ΣCi x Vdd x F (2) known as Moore’s law: the number of transistors integrated on a chip doubles every N months. Fig. 1 presents the evolution for II. CONSEQUENCES OF MOORE LAW DRAM memories, processors (MPU) and three types of read- only memories [1]. The growth rate decreases with years, from A. -
Cuda C Best Practices Guide
CUDA C BEST PRACTICES GUIDE DG-05603-001_v9.0 | June 2018 Design Guide TABLE OF CONTENTS Preface............................................................................................................ vii What Is This Document?..................................................................................... vii Who Should Read This Guide?...............................................................................vii Assess, Parallelize, Optimize, Deploy.....................................................................viii Assess........................................................................................................ viii Parallelize.................................................................................................... ix Optimize...................................................................................................... ix Deploy.........................................................................................................ix Recommendations and Best Practices.......................................................................x Chapter 1. Assessing Your Application.......................................................................1 Chapter 2. Heterogeneous Computing.......................................................................2 2.1. Differences between Host and Device................................................................ 2 2.2. What Runs on a CUDA-Enabled Device?...............................................................3 Chapter 3. Application Profiling............................................................................. -
8-Bit All Flash 78K0 Microcontrollers 78K0S
For further information, please contact: NEC Electronics Corporation 1753, Shimonumabe, Nakahara-ku, Kawasaki, Kanagawa 211-8668, Japan Tel: 044-435-5111 http://www.necel.com/ [America] [Europe] [Asia & Oceania] NEC Electronics America, Inc. NEC Electronics (Europe) GmbH NEC Electronics (China) Co., Ltd 8-bit All Flash 2880 Scott Blvd. Arcadiastrasse 10 7th Floor, Quantum Plaza, No. 27 ZhiChunLu Haidian Santa Clara, CA 95050-2554, U.S.A. 40472 Düsseldorf, Germany District, Beijing 100083, P.R.China Tel: 408-588-6000 Tel: 0211-65030 Tel: 010-8235-1155 800-366-9782 http://www.eu.necel.com/ http://www.cn.necel.com/ http://www.am.necel.com/ Hanover Office Shanghai Branch Podbielskistrasse 166 B Room 2509-2510, Bank of ChinaTower, 30177 Hannover 200Y incheng Road Central, Tel: 0 511 33 40 2-0 Pudong New Area, Shanghai, P.R.China P.C:200120 Tel:021-5888-5400 Munich Office http://www.cn.necel.com/ Werner-Eckert-Strasse 9 81829 München Shenzhen Branch Tel: 0 89 92 10 03-0 Unit 01, 39/F, Excellence Times Square Building, Stuttgart Office No. 4068 Yi Tian Road, Futian District, Shenzhen, Industriestrasse 3 P.R.China P.C:518048 70565 Stuttgart Tel:0755-8282-9800 Tel: 0 711 99 01 0-0 http://www.cn.necel.com/ United Kingdom Branch NEC Electronics Hong Kong Ltd. Cygnus House, Sunrise Parkway Unit 1601-1613, 16/F., Tower 2, Grand Century Place, Linford Wood, Milton Keynes 193 Prince Edward Road West, Mongkok, Kowloon, Hong Kong MK14 6NP, U.K. Tel: 2886-9318 Tel: 01908-691-133 http://www.hk.necel.com/ Succursale Française 9, rue Paul Dautier, B.P. -
Multi-Cycle Datapathoperation
Book's Definition of Performance • For some program running on machine X, PerformanceX = 1 / Execution timeX • "X is n times faster than Y" PerformanceX / PerformanceY = n • Problem: – machine A runs a program in 20 seconds – machine B runs the same program in 25 seconds 1 Example • Our favorite program runs in 10 seconds on computer A, which hasa 400 Mhz. clock. We are trying to help a computer designer build a new machine B, that will run this program in 6 seconds. The designer can use new (or perhaps more expensive) technology to substantially increase the clock rate, but has informed us that this increase will affect the rest of the CPU design, causing machine B to require 1.2 times as many clockcycles as machine A for the same program. What clock rate should we tellthe designer to target?" • Don't Panic, can easily work this out from basic principles 2 Now that we understand cycles • A given program will require – some number of instructions (machine instructions) – some number of cycles – some number of seconds • We have a vocabulary that relates these quantities: – cycle time (seconds per cycle) – clock rate (cycles per second) – CPI (cycles per instruction) a floating point intensive application might have a higher CPI – MIPS (millions of instructions per second) this would be higher for a program using simple instructions 3 Performance • Performance is determined by execution time • Do any of the other variables equal performance? – # of cycles to execute program? – # of instructions in program? – # of cycles per second? – average # of cycles per instruction? – average # of instructions per second? • Common pitfall: thinking one of the variables is indicative of performance when it really isn’t. -
CS2504: Computer Organization
CS2504, Spring'2007 ©Dimitris Nikolopoulos CS2504: Computer Organization Lecture 4: Evaluating Performance Instructor: Dimitris Nikolopoulos Guest Lecturer: Matthew Curtis-Maury CS2504, Spring'2007 ©Dimitris Nikolopoulos Understanding Performance Why do we study performance? Evaluate during design Evaluate before purchasing Key to understanding underlying organizational motivation How can we (meaningfully) compare two machines? Performance, Cost, Value, etc Main issue: Need to understand what factors in the architecture contribute to overall system performance and the relative importance of these factors Effects of ISA on performance 2 How will hardware change affect performance CS2504, Spring'2007 ©Dimitris Nikolopoulos Airplane Performance Analogy Airplane Passengers Range Speed Boeing 777 375 4630 610 Boeing 747 470 4150 610 Concorde 132 4000 1250 Douglas DC-8-50 146 8720 544 Fighter Jet 4 2000 1500 What metric do we use? Concorde is 2.05 times faster than the 747 747 has 1.74 times higher throughput What about cost? And the winner is: It Depends! 3 CS2504, Spring'2007 ©Dimitris Nikolopoulos Throughput vs. Response Time Response Time: Execution time (e.g. seconds or clock ticks) How long does the program take to execute? How long do I have to wait for a result? Throughput: Rate of completion (e.g. results per second/tick) What is the average execution time of the program? Measure of total work done Upgrading to a newer processor will improve: response time Adding processors to the system will improve: throughput 4 CS2504, Spring'2007 ©Dimitris Nikolopoulos Example: Throughput vs. Response Time Suppose we know that an application that uses both a desktop client and a remote server is limited by network performance. -
Analysis of Body Bias Control Using Overhead Conditions for Real Time Systems: a Practical Approach∗
IEICE TRANS. INF. & SYST., VOL.E101–D, NO.4 APRIL 2018 1116 PAPER Analysis of Body Bias Control Using Overhead Conditions for Real Time Systems: A Practical Approach∗ Carlos Cesar CORTES TORRES†a), Nonmember, Hayate OKUHARA†, Student Member, Nobuyuki YAMASAKI†, Member, and Hideharu AMANO†, Fellow SUMMARY In the past decade, real-time systems (RTSs), which must in RTSs. These techniques can improve energy efficiency; maintain time constraints to avoid catastrophic consequences, have been however, they often require a large amount of power since widely introduced into various embedded systems and Internet of Things they must control the supply voltages of the systems. (IoTs). The RTSs are required to be energy efficient as they are used in embedded devices in which battery life is important. In this study, we in- Body bias (BB) control is another solution that can im- vestigated the RTS energy efficiency by analyzing the ability of body bias prove RTS energy efficiency as it can manage the tradeoff (BB) in providing a satisfying tradeoff between performance and energy. between power leakage and performance without affecting We propose a practical and realistic model that includes the BB energy and the power supply [4], [5].Itseffect is further endorsed when timing overhead in addition to idle region analysis. This study was con- ducted using accurate parameters extracted from a real chip using silicon systems are enabled with silicon on thin box (SOTB) tech- on thin box (SOTB) technology. By using the BB control based on the nology [6], which is a novel and advanced fully depleted sili- proposed model, about 34% energy reduction was achieved. -
A Performance Analysis Tool for Intel SGX Enclaves
sgx-perf: A Performance Analysis Tool for Intel SGX Enclaves Nico Weichbrodt Pierre-Louis Aublin Rüdiger Kapitza IBR, TU Braunschweig LSDS, Imperial College London IBR, TU Braunschweig Germany United Kingdom Germany [email protected] [email protected] [email protected] ABSTRACT the provider or need to refrain from offloading their workloads Novel trusted execution technologies such as Intel’s Software Guard to the cloud. With the advent of Intel’s Software Guard Exten- Extensions (SGX) are considered a cure to many security risks in sions (SGX)[14, 28], the situation is about to change as this novel clouds. This is achieved by offering trusted execution contexts, so trusted execution technology enables confidentiality and integrity called enclaves, that enable confidentiality and integrity protection protection of code and data – even from privileged software and of code and data even from privileged software and physical attacks. physical attacks. Accordingly, researchers from academia and in- To utilise this new abstraction, Intel offers a dedicated Software dustry alike recently published research works in rapid succession Development Kit (SDK). While it is already used to build numerous to secure applications in clouds [2, 5, 33], enable secure network- applications, understanding the performance implications of SGX ing [9, 11, 34, 39] and fortify local applications [22, 23, 35]. and the offered programming support is still in its infancy. This Core to all these works is the use of SGX provided enclaves, inevitably leads to time-consuming trial-and-error testing and poses which build small, isolated application compartments designed to the risk of poor performance. -
Familias De Microcontroladores
INTRODUCCION Un microcontrolador es un circuito integrado tiene en su interior todas las características de un computador, es decir, programa y circuitos periféricos para CPU, RAM, una memoria de entrada y salida. Muy regularmente los microcontroladores poseen además convertidores análogo - digital, temporizadores, contadores y un sistema para permitir la comunicación en serie y en paralelo. Se pueden crear muchas aplicaciones con los microcontroladores. Estas aplicaciones de los microcontroladores son ilimitadas, entre ellas podemos mencionar: sistemas de alarmas, iluminación, paneles publicitarios, etc. Controles automáticos para la Industria en general. Entre ellos control de motores DC/AC y motores de paso a paso, control de máquinas, control de temperatura, tiempo; adquisición de datos mediante sensores, etc. HISTORIA El primer microprocesador fue el Intel 4004 de 4 bits, lanzado en 1971, seguido por el Intel 8008 y otros más capaces. Sin embargo, ambos procesadores requieren circuitos adicionales para implementar un sistema de trabajo, elevando el costo del sistema total. Los ingenieros de Texas Instruments Gary Boone y Michael Cochran lograron crear el primer microcontrolador, TMS 1000, en 1971; fue comercializado en 1974. Combina memoria ROM, memoria RAM, microprocesador y reloj en un chip y estaba destinada a los sistemas embebidos.2 Debido en parte a la existencia del TMS 1000,3 Intel desarrolló un sistema de ordenador en un chip optimizado para aplicaciones de control, el Intel 8048, que comenzó a comercializarse en 1977.3 Combina memoria RAM y ROM en el mismo chip y puede encontrarse en más de mil millones de teclados de compatible IBM PC, y otras numerosas aplicaciones. El en ese momento presidente de Intel, Luke J. -
ESC-470: ARM 9 Instruction Set Architecture with Performance
ARM 9 Instruction Set Architecture Introduction with Performance Perspective Joe-Ming Cheng, Ph.D. ARM-family processors are positioned among the leaders in key embedded applications. Many presentations and short lectures have already addressed the ARM’s applications and capabilities. In this introduction, we intend to discuss the ARM’s instruction set uniqueness from the performance prospective. This introduction is also trying to follow the approaches established by two outstanding textbooks of David Patterson and John Hennessey [PetHen00] [HenPet02]. 1.0 ARM Instruction Set Architecture Processor instruction set architecture (ISA) choices have evolved from accumulator, stack, register-to- memory, to register-register (load-store) organization. ARM 9 ISA is a load-store machine. ARM 9 ISA takes advantage of its smaller set of registers (16 vs. many 32-register processors) to incorporate more direct controls and achieve high encoding density. ARM’s load or store multiple register instruction, for example , allows enlisting of all possible registers and conditional execution in one instruction. The Thumb mode instruction set is another exa mple of how ARM ISA facilitates higher encode density. Rather than compressing the code, Thumb -mode instructions are two 16-bit instructions packed in a 32-bit ARM-mode instruction space. The Thumb -mode instructions are a subset of ARM instructions. When executing in Thumb mode, a single 32-bit instruction fetch cycle effectively brings in two instructions. Thumb code reduces access bandwidth, code size, and improves instruction cache hit rate. Another way ARM achieves cycle time reduction is by using Harvard architecture. The architecture facilitates independent data and instruction buses. -
A Characterization of Processor Performance in the VAX-1 L/780
A Characterization of Processor Performance in the VAX-1 l/780 Joel S. Emer Douglas W. Clark Digital Equipment Corp. Digital Equipment Corp. 77 Reed Road 295 Foster Street Hudson, MA 01749 Littleton, MA 01460 ABSTRACT effect of many architectural and implementation features. This paper reports the results of a study of VAX- llR80 processor performance using a novel hardware Prior related work includes studies of opcode monitoring technique. A micro-PC histogram frequency and other features of instruction- monitor was buiit for these measurements. It kee s a processing [lo. 11,15,161; some studies report timing count of the number of microcode cycles execute z( at Information as well [l, 4,121. each microcode location. Measurement ex eriments were performed on live timesharing wor i loads as After describing our methods and workloads in well as on synthetic workloads of several types. The Section 2, we will re ort the frequencies of various histogram counts allow the calculation of the processor events in 5 ections 3 and 4. Section 5 frequency of various architectural events, such as the resents the complete, detailed timing results, and frequency of different types of opcodes and operand !!Iection 6 concludes the paper. specifiers, as well as the frequency of some im lementation-s ecific events, such as translation bu h er misses. ?phe measurement technique also yields the amount of processing time spent, in various 2. DEFINITIONS AND METHODS activities, such as ordinary microcode computation, memory management, and processor stalls of 2.1 VAX-l l/780 Structure different kinds. This paper reports in detail the amount of time the “average’ VAX instruction The llf780 processor is composed of two major spends in these activities. -
Autotuning GPU Kernels Via Static and Predictive Analysis
Autotuning GPU Kernels via Static and Predictive Analysis Robert V. Lim, Boyana Norris, and Allen D. Malony Computer and Information Science University of Oregon Eugene, OR, USA froblim1, norris, [email protected] Abstract—Optimizing the performance of GPU kernels is branches occur, threads that do not satisfy branch conditions challenging for both human programmers and code generators. are masked out. If the kernel programmer is unaware of the For example, CUDA programmers must set thread and block code structure or the hardware underneath, it will be difficult parameters for a kernel, but might not have the intuition to make a good choice. Similarly, compilers can generate working for them to make an effective decision about thread and block code, but may miss tuning opportunities by not targeting GPU parameters. models or performing code transformations. Although empirical CUDA developers face two main challenges, which we autotuning addresses some of these challenges, it requires exten- aim to alleviate with the approach described in this paper. sive experimentation and search for optimal code variants. This First, developers must correctly select runtime parameters research presents an approach for tuning CUDA kernels based on static analysis that considers fine-grained code structure and as discussed above. A developer or user may not have the the specific GPU architecture features. Notably, our approach expertise to decide on parameter settings that will deliver does not require any program runs in order to discover near- high performance. In this case, one can seek guidance from optimal parameter settings. We demonstrate the applicability an optimization advisor. The advisor could consult a perfor- of our approach in enabling code autotuners such as Orio to mance model based on static analysis of the kernel properties, produce competitive code variants comparable with empirical- based methods, without the high cost of experiments.