CSE2021 Computer Organization. Computer Abstractions and Technology

Transcription

1 CSE2021 Computer Organization Chapter 1 Computer Abstractions and Technology Instructor: Prof. Peter Lian Department of Electrical Engineering & Computer Science Lassonde School of Engineering York University Acknowledgement The slides are adapted from Computer Organization and Design, 5 th Edition, by David A. Patterson and John L. Hennessy, 2013, published by MK (Elsevier) 1

2 CSE2021 Computer Organization Instructor: Prof. Peter Lian tel: ext Couse Web: Schedule: Lectures: MW 17: , Room CLH A Labs: Lab-01 M 19:00 22:00, LAS 1006 Lab-02 T 19:00 22:00, LAS 1006/1004 Office hours: MW 15:00 LAS 1012C CSE2021 Computer Organization Text book: Computer Organization and Design -- The Hardware/Software Interface 5th Edition by David A. Patterson and John L. Hennessy Morgan Kaufmann Publishers (Elsevier) ISBN

3 Assessment (No Makeup) Quizzes: 20% (5:30-5:50pm) Quiz 1 for Chapter 1 on Jan. 21 Quiz 2 for Chapter 2 on Feb. 2 Quiz 3 for Chapter 3 on Feb. 11 Quiz 4 for Appendix on Mar. 4 Quiz 5 for Chapter 4 Parts 1 and 2 on Apr. 1 Lab: 25% 7 lab sessions Starts in week 4 Midterm test: 20% on Feb. 25, 5:30-6:45pm Final exam: 35% CSE2021 Computer Organization Topics covered: Introduction Computer abstractions and technology Language of the computer: high lever language versus assembly language versus machine language Arithmetic for computers The processor 3

4 CSE2021 Computer Organization Introduction The Computer Revolution Moore s Law Source: ISSCC 2003 G. Moore No exponential is forever, but forever can be delayed 4

5 Moore s Law Year of introduction Transistors , , , , , , DX ,180,000 Pentium ,100,000 Pentium II ,500,000 Pentium III ,000,000 Pentium ,000,000 Moore s Law Ending? 5

6 Bell s Law Source: B Bell, Bell s Law for the Birth and Death of Computer Classes, Comms of ACM, 2008 The 1 st Generation Computer EDSAC, University of Cambridge, UK, 1949 Source: 6

7 Computers Now Routers Data Center Over three years, the power bill for a single server can be higher than the cost of the computer itself. Jeffrey W. Clarke Vice Chairman of Operations & Technology Sun Microsystems (now Oracle) One Google search consumes 0.3 watt-hours. Powering a Google search The Official Google Blog 7

8 Future Direction Source: Next Generation 8

9 The Computer Revolution Progress in computer technology Underpinned by Moore s Law Makes novel applications feasible Computers in automobiles Cell phones Human genome project World Wide Web Search Engines Computers are pervasive Classes of Computers Desktop computers General purpose, variety of software Subject to cost/performance tradeoff Server computers Network based High capacity, performance, reliability Range from small servers to building sized Embedded computers Hidden as components of systems Stringent power/performance/cost constraints 9

10 What You Will Learn How programs are translated into the machine language And how the hardware executes them The hardware/software interface What determines program performance And how it can be improved How hardware designers improve performance What is parallel processing Understanding Performance Algorithm Determines number of operations executed Programming language, compiler, architecture Determine number of machine instructions executed per operation Processor and memory system Determine how fast instructions are executed I/O system (including OS) Determines how fast I/O operations are executed 10

11 Below Your Program Application software Written in high-level language System software Compiler: translates HLL code to machine code Operating System: service code Hardware Handling input/output Managing memory and storage Scheduling tasks & sharing resources Processor, memory, I/O controllers Levels of Program Code High-level language Level of abstraction closer to problem domain Provides for productivity and portability Assembly language Textual representation of instructions Hardware representation Binary digits (bits) Encoded instructions and data 11

12 Components of a Computer The BIG Picture Same components for all kinds of computer Desktop, server, embedded Input/output includes User-interface devices Display, keyboard, mouse Storage devices Hard disk, CD/DVD, flash Network adapters For communicating with other computers Anatomy of a Computer Output device Network cable Input device Input device 12

13 Opening the Box Opening the Box 13

14 Inside the Processor (CPU) Datapath: performs operations on data Control: sequences datapath, memory,... Cache memory Small fast SRAM memory for immediate access to data Abstractions The BIG Picture Abstraction helps us deal with complexity Hide lower-level detail Instruction set architecture (ISA) The hardware/software interface Application binary interface The ISA plus system software interface Implementation The details underlying and interface 14

15 A Safe Place for Data Volatile main memory Loses instructions and data when power off Non-volatile secondary memory Magnetic disk Flash memory Optical disk (CDROM, DVD) Networks Communication and resource sharing Local area network (LAN): Ethernet Within a building Wide area network (WAN: the Internet Wireless network: WiFi, Bluetooth 15

16 Technology Trends Electronics technology continues to evolve Increased capacity and performance Reduced cost DRAM capacity Year Technology Relative performance/cost 1951 Vacuum tube Transistor Integrated circuit (IC) Very large scale IC (VLSI) 2,400, Ultra large scale IC 6,200,000,000 Defining Performance Which airplane has the best performance? Boeing 777 Boeing 747 BAC/Sud Concorde Douglas DC-8-50 Boeing 777 Boeing 747 BAC/Sud Concorde Douglas DC Passenger Capacity Cruising Range (miles) Boeing 777 Boeing 747 BAC/Sud Concorde Douglas DC-8-50 Boeing 777 Boeing 747 BAC/Sud Concorde Douglas DC Cruising Speed (mph) Passengers x mph 16

17 Response Time and Throughput Response time (execution time) How long it takes to do a task Important to computer users Throughput (bandwidth) Total amount of work done per unit time Important to server, data center Different performance metrics are needed to benchmark different systems. Single application is not sufficient to measure the performance of computers Response Time vs. Throughput How are response time and throughput affected by Replacing the processor with a faster version? Adding more processors? We will focus on response time by now. 17

18 Relative Performance Define Performance = 1/(Execution Time) X is n time faster than Y Performance Performance X Y = Execution time Y Execution time X = n Example: time taken to run a program 10s on A, 15s on B Execution Time B / Execution Time A = 15s / 10s = 1.5 So A is 1.5 times faster than B Measuring Execution Time Elapsed time Total response time, including all aspects Processing, I/O, OS overhead, idle time Determines system performance CPU time Time spent processing a given job Discounts I/O time, other jobs shares Comprises user CPU time and system CPU time Different programs are affected differently by CPU and system performance 18

19 Measuring Execution Time Unix command time can be used to determine the elapsed time and CPU time CPU Clocking Operation of digital hardware governed by a constant-rate clock Clock (cycles) Data transfer and computation Update state Clock period Clock period: duration of a clock cycle e.g., 250ps = 0.25ns = s Clock frequency (rate): cycles per second e.g., 4.0GHz = 4000MHz = Hz 19

20 CPU Time CPU Time = CPU Clock Cycles Clock Cycle Time = CPU Clock Cycles Clock Rate Performance improved by Reducing number of clock cycles Increasing clock rate Hardware designer must often trade off clock rate against cycle count CPU Time Example Computer A: 2GHz clock, 10s CPU time Designing Computer B Aim for 6s CPU time Can do faster clock, but causes 1.2 clock cycles of A How fast must Computer B clock be? Clock Rate B Clock Cycles = CPU Time B B 1.2 Clock Cycles = 6s A Clock Cycles A = CPU Time A Clock Rate A = 10s 2GHz = Clock Rate B = 6s = 6s 9 = 4GHz 20

21 Instruction Performance Clock Cycles = Instruction Count Ave Cycles per Instruction CPU Time = Instruction Count CPI Clock Cycle Time Instruction Count: no. of instruction for a program Determined by program, Instruction Set Architecture (ISA) and compiler Average cycles per instruction (CPI) Determined by CPU hardware If different instructions have different CPI Instruction Count CPI = Clock Rate Average CPI affected by instruction mix CPI Example Computer A: Cycle Time = 250ps, CPI = 2.0 Computer B: Cycle Time = 500ps, CPI = 1.2 Same ISA Which is faster, and by how much? CPU Time A = Instruction Count CPI A Cycle Time A = I ps = I 500ps A is faster CPU Time B = Instruction Count CPI B Cycle Time B By how much? = I ps = I 600ps 21

22 CPI in More Detail If different instruction classes take different numbers of cycles Clock Cycles = n i= 1 Weighted average CPI (CPIi Instruction Counti ) CPI = Clock Cycles Instruction Count = n i= 1 Instruction Counti CPIi Instruction Count Relative frequency CPI Example Alternative compiled program using instructions in classes A, B, C Class A B C CPI for class IC in program IC in program Program 1: IC = 5 Clock Cycles = = 10 Avg. CPI = 10/5 = 2.0 Program 2: IC = 6 Clock Cycles = = 9 Avg. CPI = 9/6 =

23 Performance Summary The BIG Picture Instructions Clock cycles CPU Time = Program Instruction Performance depends on Algorithm: affects IC, possibly CPI Programming language: affects IC, CPI Compiler: affects IC, CPI Seconds Clock cycle Instruction set architecture: affects IC, CPI, T c SPEC CPU Benchmark Programs used to measure performance Supposedly typical of actual workload Standard Performance Evaluation Corp (SPEC) Develops benchmarks for CPU, I/O, Web, SPEC CPU2006 Elapsed time to execute a selection of programs Negligible I/O, so focuses on CPU performance Normalize relative to reference machine Summarize as geometric mean of performance ratios CINT2006 (integer) and CFP2006 (floating-point) n n Execution time ratio i i= 1 23

24 CINT2006 for Opteron X Name Description IC 10 9 CPI Tc (ns) Exec time Ref time SPECratio perl Interpreted string processing 2, , bzip2 Block-sorting compression 2, , gcc GNU C Compiler 1, , mcf Combinatorial optimization ,345 9, go Go game (AI) 1, , hmmer Search gene sequence 2, , sjeng Chess game (AI) 2, , libquantum Quantum computer simulation 1, ,047 20, h264avc Video compression 3, , omnetpp Discrete event simulation , astar Games/path finding 1, , xalancbmk XML parsing 1, ,143 6, Geometric mean 11.7 SPEC Power Benchmark Power consumption of server at different workload levels Performance: ssj_ops/sec Power: Watts (Joules/sec) Overall ssj_ops per Watt = ssj_ops i poweri i= 0 i= 0 24

25 SPECpower_ssj2008 for X4 Target Load % Performance (ssj_ops/sec) Average Power (Watts) 100% 231, % 211, % 185, % 163, % 140, % 118, % 920, % 70, % 47, % 23, % Overall sum 1,283,590 2,605 ssj_ops/ power 493 Power Trends Watts/cm Sun s Surface Atom Itanium water cooling > $100 Xeon Rocket 100 Nozzle air cooling $10 Pentium 4 Nuclear Reactor Pentium III 10 Pentium II Pentium Pro i386 Pentium 190W at 65- nm ð 135 W/cm 2! i486 P = VI ð 190W at 1.1V = 170A! 1 500nm 350 nm 250nm 180nm 130nm 90nm 65nm 45nm 32nm 25

26 The Thermal Crisis Power Trends In CMOS IC technology Power = Capacitive load Voltage 2 Frequency 3 0 5V 1V

27 Reducing Power Suppose a new CPU has 85% of capacitive load of old CPU 15% voltage and 15% frequency reduction P 4 2 new Cold 0.85 (Vold 0.85) Fold 0.85 = = 0.85 = P C V F old old old The power wall We can t reduce voltage further We can t remove more heat How else can we improve performance? old Uniprocessor Performance Constrained by power, instruction-level parallelism, memory latency 27

28 Multiprocessors Multicore microprocessors More than one processor per chip Requires explicitly parallel programming Compare with instruction level parallelism Hardware executes multiple instructions at once Hidden from the programmer Hard to do Programming for performance Load balancing Optimizing communication and synchronization Pitfall: Amdahl s Law Improving an aspect of a computer and expecting a proportional improvement in overall performance Taffected T improved = + Tunaffected improvement factor Example: multiply accounts for 80s/100s How much improvement in multiply performance to get 5 overall? = + 20 Can t be done! n Corollary: make the common case fast 28

29 Fallacy: Low Power at Idle Look back at X4 power benchmark At 100% load: 295W At 50% load: 246W (83%) At 10% load: 180W (61%) Google data center Mostly operates at 10% 50% load At 100% load less than 1% of the time Consider designing processors to make power proportional to load Importance of Standby Power Of the $250 billion spent globally each year powering computers, about 85% of that energy is simply wasted idling. Source: Economist, August 11,

30 Pitfall: MIPS as a Performance Metric MIPS: Millions of Instructions Per Second Doesn t account for Differences in ISAs between computers Differences in complexity between instructions Instruction count MIPS = Execution time 10 Instruction count = Instruction count CPI 10 Clock rate 6 6 Clock rate = 6 CPI 10 CPI varies between programs on a given CPU Concluding Remarks Cost/performance is improving Due to underlying technology development Hierarchical layers of abstraction In both hardware and software Instruction set architecture The hardware/software interface Execution time: the best performance measure Power is a limiting factor Use parallelism to improve performance 30