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Computer Organization & Design. The importance of computer organization. Why should a computer scientist study computer organization? You probably won’t be designing hardware, but … … you might work on embedded systems … you could be designing compilers

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the importance of computer organization
The importance of computer organization
  • Why should a computer scientist study computer organization?
    • You probably won’t be designing hardware, but …
      • … you might work on embedded systems
      • … you could be designing compilers
      • … you want your software to perform well
    • In all of these cases, you need to understand the hardware!
what is a computer
What is a computer?
  • From The Dictionarys:
    • “One who computes”
      • We could argue that people are computers
    • “A device that computes, especially a programmable electronic machine that performs high-speed mathematical or logical operations that assembles, stores, correlates, or otherwise processes information.”
      • Anything from a simple abacus to the microprocessor-based computers of today
the computer revolution
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
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
what you will learn
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

Chapter 1 — Computer Abstractions and Technology — 8

understanding performance
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

Chapter 1 — Computer Abstractions and Technology — 9

below your program
Below Your Program
  • Application software
    • Written in high-level language
  • System software
    • Compiler: translates HLL code to machine code
    • Operating System: service code
      • Handling input/output
      • Managing memory and storage
      • Scheduling tasks & sharing resources
  • Hardware
    • Processor, memory, I/O controllers

§1.2 Below Your Program

Chapter 1 — Computer Abstractions and Technology — 10

levels of program code
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
  • using English words
  • according to their intended use
  • less time to develop programs
  • programs to be independent of the computer
components of a computer
Components of a Computer
  • Same components forall 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

The BIG Picture

components of a computer1
Components of a computer

CIS 273: Lecture 1

anatomy of a mouse
Anatomy of a Mouse
  • Optical mouse
    • LED illuminates desktop
    • Small low-res camera
    • Basic image processor
      • Looks for x, y movement
    • Buttons & wheel
  • Supersedes roller-ball mechanical mouse

Chapter 1 — Computer Abstractions and Technology — 14

through the looking glass
Through the Looking Glass
  • LCD screen: picture elements (pixels)
    • Mirrors content of frame buffer memory

Chapter 1 — Computer Abstractions and Technology — 15

inside the amd barcelona microprocessor
Inside the AMD Barcelona microprocessor
  • Cache memory (SRAM vs DRAM)
abstractions
Abstractions
  • 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

The BIG Picture

a safe place for data
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)

Chapter 1 — Computer Abstractions and Technology — 18

networks
Networks
  • Network
    • Communication
    • Resource sharing
    • Nonlocal access
  • Local area network (LAN): Ethernet
    • Within a building
  • Wide area network (WAN: the Internet
  • Wireless network: WiFi, Bluetooth

Chapter 1 — Computer Abstractions and Technology — 19

technology trends
Technology Trends
  • Electronics technology continues to evolve
    • Increased capacity and performance
    • Reduced cost

DRAM capacity

Chapter 1 — Computer Abstractions and Technology — 21

defining performance
Defining Performance
  • What is Performance
  • Which airplane has the best performance?
response time and throughput
Response Time and Throughput
  • Response time (execution time)
    • How long it takes to do a task
  • Throughput (bandwidth)
    • Total work done per unit time
      • e.g., tasks/transactions/… per hour
  • How are response time and throughput affected by
    • Replacing the processor with a faster version?
    • Adding more processors?
  • We’ll focus on response time for now…
relative performance
Relative Performance
  • Example: time taken to run a program
    • 10s on A, 15s on B
    • Execution TimeB / Execution TimeA
    • = 15s / 10s = 1.5
    • So A is 1.5 times faster than B
measuring performance
Measuring Performance
  • Wall clock time, response time or elapsed time
  • CPU execution time Also called CPU time.
    • The actual time the CPU spends computing for a specific task.
  • 1. user CPU time The
    • CPU time spent in a program itself.
  • 2. system CPU time
    • The CPU time spent in the operating system performing tasks on behalf of the program.

System performance

CPU Performance

cpu clocking
CPU Clocking
  • Operation of digital hardware governed by a constant-rate clock

Clock period

Clock (cycles)

Data transferand computation

Update state

  • 1. Clock period: duration of a clock cycle
    • e.g., 250ps = 0.25ns = 250×10–12s
  • 2. Clock frequency (rate): cycles per second
    • e.g., 4.0GHz = 4000MHz = 4.0×109Hz
cpu performance and its factors
CPU Performance and Its Factors
  • 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
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
  • How fast must Computer B clock be?
cpu performance and its factors1
CPU Performance and Its Factors

Example: Improving Performance

instruction performance
Instruction Performance

CPI

  • Instruction Count for a program
    • Determined by program, ISA and compiler
  • Average cycles per instruction (CPI)
    • Determined by CPU hardware
    • If different instructions have different CPI
      • Average CPI affected by instruction mix
cpi example
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?
instruction performance1
Instruction Performance

Example: Using the Performance Equation

cpi in more detail
CPI in More Detail
  • If different instruction classes take different numbers of cycles
  • Weighted average CPI

Relative frequency

cpi example1
CPI Example
  • Alternative compiled code sequences using instructions in classes A, B, C
  • Sequence 1: IC = 5
    • Clock Cycles= 2×1 + 1×2 + 2×3= 10
    • Avg. CPI = 10/5 = 2.0
  • Sequence 2: IC = 6
    • Clock Cycles= 4×1 + 1×2 + 1×3= 9
    • Avg. CPI = 9/6 = 1.5

Chapter 1 — Computer Abstractions and Technology — 36

performance summary
Performance Summary
  • How can we determine the value of these factors?
  • Performance depends on
    • Algorithm: affects IC, possibly CPI
    • Programming language: affects IC, CPI
    • Compiler: affects IC, CPI
    • Instruction set architecture: affects IC, CPI, Tc

The BIG Picture

Chapter 1 — Computer Abstractions and Technology — 37

power wall
Power wall

×30

5V → 1V

×1000

How could clock rates grow by a factor of 1000 while power grew by only a

factor of 30?

relative power
Relative Power
  • Suppose a new CPU has
    • 85% of capacitive load of old CPU
    • 15% voltage and 15% frequency reduction
  • The power wall
    • We can’t reduce voltage further
    • We can’t remove more heat
  • How else can we improve performance?
leakage
leakage
  • leakage is typically responsible for 40% of the power consumption in 2008
  • increasing the number of transistors increases power dissipation, even if the transistors are always off.
the sea change
The sea change!

Constrained by power, instruction-level parallelism, memory latency

multicore
Multicore

In the past, programmers could rely on innovations in hardware, architecture, and compilers to double performance of their programs every 18 months without having to change a line of code.

multiprocessors
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

Chapter 1 — Computer Abstractions and Technology — 43

real stuff manufacturing and benchmarking the amd opteron x4
Real Stuff: Manufacturing andBenchmarking the AMD Opteron X4
  • manufacture of a chip : silicon (semiconductor)
    • Excellent conductors of electricity
    • Excellent insulators from electricity
    • Areas that can conduct or insulate under special conditions
  • A VLSI circuit
  • `
amd opteron x2 wafer
AMD Opteron X2 Wafer
  • X2: 300mm wafer, 117 chips, 90nm technology
  • X4: 45nm technology

Chapter 1 — Computer Abstractions and Technology — 46

spec cpu benchmark
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 12) and CFP2006 (floating-point 17)

Chapter 1 — Computer Abstractions and Technology — 48

cint2006 for opteron x4 2356
CINT2006 for Opteron X4 2356

High cache miss rates

Chapter 1 — Computer Abstractions and Technology — 49

spec power benchmark
SPEC Power Benchmark
  • Power consumption of server at different workload levels
    • Performance: ssj_ops/sec
    • Power: Watts (Joules/sec)

Chapter 1 — Computer Abstractions and Technology — 50

specpower ssj2008 for x4
SPECpower_ssj2008 for X4

Chapter 1 — Computer Abstractions and Technology — 51

fallacies and pitfalls
Fallacies and Pitfalls
  • Pitfall: Expecting the improvement of one aspect of a computer to increase overall performance by an amount proportional to the size of the improvement.

Amdahl

fallacies and pitfalls1
Fallacies and Pitfalls
  • Fallacy: Computers at low utilization use little power.
fallacies and pitfalls2
Fallacies and Pitfalls
  • Pitfall: Using a subset of the performance equation as a performance metric.
  • MIPS (million instructions per second)
  • MIPS Problems:
    • computers have different instruction sets
    • MIPS varies between programs on the same computer
  • if a new program executes more instructions but each
  • instruction is faster, MIPS can vary independently from performance
slide55
Test

which Fallacies do match?

  • What is MIPS and which one is faster?

Higher MIPS

Faster

problems until 88 12 8
Problems (until 88/12/8)
  • Problems 1.8
  • Problems 1.12
  • Problems 1.14
  • Problems 1.16