CAQA5e_ch1 (3).pptx

1
Copyright © 2012, Elsevier Inc. All rights reserved.
Module 1
Fundamentals of Quantitative
Design and Analysis
Computer Architecture
A Quantitative Approach, sixth Edition

1970: The IBM 7044 Computer
Introduction
Copyright © 2013, Daniel A. Menasce. All rights reserved. 2

1970: Magnetic Core Memory
Introduction
1core =
1 bit

How Does a Computer Work?
 What is the Von Neumann computer
architecture?
 What are the elements of a Von Neumann
computer?
 How do these elements interact to execute
programs?
Introduction

Von Neumann Architecture
 What is the Von Neumann computer
architecture?
 What are the elements of a Von Neumann
computer?
 How do these elements interact to execute
programs?
Introduction

Introduction
Stored-program
computer
Program
ALU Data
Registers
system bus

Introduction
Program
ALU Data
Registers
Stored-program
computer
Sequence of
instructions:
(1) Load/Store from
memory to/from
registers, (2)
Arithmetic/Logical
Ops., (3) Conditional
and unconditional
branches.
General
registers,
FP registers,
Program
counter, etc.

Example of Instruction Formats
Introduction
opcode RX RY RZ
opcode RX Address
RZ RX op RY
RX  Mem(address)
Mem(address) RX
opcode Address
Unconditional or
Conditional Jump to
Address

Computer Technology
 Performance improvements:
 Improvements in semiconductor technology
 Feature size, clock speed
 Improvements in computer architectures
 Enabled by HLL compilers, UNIX
 Lead to RISC architectures
 Together have enabled:
 Lightweight computers
 Productivity-based managed/interpreted
programming languages
Introduction
Copyright © 2012, Elsevier Inc. All rights reserved. 9

Single Processor Performance
Introduction
RISC
Move to multi-processor. From ILP to DLP and TLP

11
Moore’s Law
Copyright © 2013, Daniel A. Menasce. All rights reserved.
Introduction
Source: Wikimedia Commons
The number of
transistors on
integrated
circuits doubles
approximately
every two years.
(Gordon Moore,
1965)

12
Copyright © 2012, Elsevier Inc. All rights reserved.
Current Trends in Architecture
 Cannot continue to leverage Instruction-Level
parallelism (ILP)
 Single processor performance improvement ended in
2003
 New models for performance:
 Data-level parallelism (DLP)
 Thread-level parallelism (TLP)
 Request-level parallelism (RLP)
 These require explicit restructuring of the
application
Introduction

Instruction Level Parallelism (ILP)
Introduction
In high-level language:
e= a + b;
f = c + d;
g = e * f;
c d
a b
+
e
+
f
*
g
3. ADD
4. STO
* f = c + d
7. ADD
8. STO
In assembly language:
* e = a+b
001 LOAD A,R1
002 LOAD B,R2
R1,R2,R3
R3,E
5. LOAD C,R4
6. LOAD D,R5
R4,R5,R6
R6,F
* g = e * f
9. MULT R3,R6,R7
10. STO R7,G

Instruction Level Parallelism
Introduction
In assembly language:
* e = a+b
001 LOAD A,R1
002 LOAD B,R2
3. ADD
4. STO
R1,R2,R3
R3,E
* f = c + d
005 LOAD C,R4
006 LOAD D,R5
7. ADD
8. STO
R4,R5,R6
R6,F
* g = e * f
9. MULT R3,R6,R7
10. STO R7,G
Instructions that can
potentially be executed in
parallel:
001, 002, 005, 006
003, 007
004, 008, 009
010

Data Level Parallelism (DLP)
Introduction
Task
Data
Task Data

Task Level Parallelism (TLP)
Introduction
Tasks
Task

Classes of Computers
 Personal Mobile Device (PMD)
 e.g. smart phones, tablet computers
 Emphasis on energy efficiency and real-time
 Desktop Computing
 Emphasis on price-performance
 Servers
 Emphasis on availability, scalability, throughput
 Clusters / Warehouse Scale Computers
 Used for “Software as a Service (SaaS)”
 Emphasis on availability and price-performance
 Sub-class: Supercomputers, emphasis: floating-point
performance and fast internal networks
 Embedded Computers
 Emphasis: price
Classes
of
Computers

Parallelism
 Classes of parallelism in applications:
 Data-Level Parallelism (DLP)
 Task-Level Parallelism (TLP)
 Classes of architectural parallelism:
 Instruction-Level Parallelism (ILP)
 Vector architectures/Graphic Processor Units (GPUs)
 Thread-Level Parallelism
 Request-Level Parallelism
Classes
of
Computers

Flynn’s Taxonomy
 Single instruction stream, single data stream (SISD)
 Single instruction stream, multiple data streams (SIMD)
 Vector architectures
 Multimedia extensions
 Graphics processor units
 Multiple instruction streams, single data stream (MISD)
 No commercial implementation
 Multiple instruction streams, multiple data streams
(MIMD)
 Tightly-coupled MIMD (thread-level parallelism)
 Loosely-coupled MIMD (cluster or warehouse-scale computing)
Classes
of
Computers

Defining Computer Architecture
 “Old” view of computer architecture:
 Instruction Set Architecture (ISA) design
 i.e. decisions regarding:
 Registers (register-to-memory or load-store), memory
addressing (e.g., byte addressing, alignment), addressing
modes e.g., register, immediate, displacement), instruction
operands (type and size), available operations (CISC or
RISC), control flow instructions, instruction encoding (fixed
vs. variable length)
 “Real” computer architecture:
 Specific requirements of the target machine
 Design to maximize performance within constraints:
cost, power, and availability
 Includes ISA, microarchitecture, hardware
Defining
Computer
Architecture

Trends in Technology
 Integrated circuit technology
 Transistor density: 35%/year
 Die size: 10-20%/year
 Integration overall (Moore’s Law): 40-55%/year
 DRAM capacity: 25-40%/year (slowing)
 Flash capacity (non-volatile semiconductor memory):
50-60%/year
 15-20X cheaper/bit than DRAM
 Magnetic disk technology: density increase: 40%/year
 15-25X cheaper/bit then Flash
 300-500X cheaper/bit than DRAM
Trends
in
Technology

Bandwidth and Latency
 Bandwidth or throughput
 Total work done in a given time (e.g., MB/sec, MIPS)
 10,000-25,000X improvement for processors
 300-1200X improvement for memory and disks
 Latency or response time
 Time between start and completion of an event
 30-80X improvement for processors
 6-8X improvement for memory and disks
Trends
in
Technology

Bandwidth and Latency
Log-log plot of bandwidth and latency milestones
Trends
in
Technology

Transistors and Wires
 Feature size
 Minimum size of transistor or wire in x or y
dimension
 10 microns in 1971 to .032 microns in 2011
 Transistor performance scales linearly
 Wire delay does not improve with feature size!
 Integration density scales quadratically
Trends
in
Technology

Power (1 Watt = 1 Joule/sec) and
Energy (Joules)
 Problem: Get power in, get power out
 Thermal Design Power (TDP)
 Characterizes sustained power consumption
 Used as target for power supply and cooling system
 Lower than peak power, higher than average power
consumption
 Clock rate can be reduced dynamically to limit
power consumption
 Energy per task is often a better measurement
Trends
in
Power
and
Energy

26
Energy and Power Example
Copyright © 2013, Daniel A. Menasce. All rights reserved.
Introduction
Processor A Processor B
20% higher average power
consumption: 1.2 P
P
Executes a task in 70% of
the time needed by B:
0.7 * T
T
Energy consumption: 1.2 *
0.7 * T = 0.84 P * T
Energy consumption = P * T
More energy efficient!
It is better to use energy instead of
power for comparing a fixed workload.

Dynamic Energy and Power
 Dynamic energy
 Transistor switch from 0 -> 1 or 1 -> 0
 ½ x Capacitive load x Voltage2
 Dynamic power
 ½ x Capacitive load x Voltage2 x Frequency switched
 Reducing clock rate reduces power, not energy
Trends
in
Power
and
Energy

Power
 Intel 80386
consumed ~ 2 W
 3.3 GHz Intel
Core i7 consumes
130 W
 Heat must be
dissipated from
1.5 x 1.5 cm chip
 This is the limit of
what can be
cooled by air
Trends
in
Power
and
Energy

Reducing Power
 Techniques for reducing power:
 Do nothing well: turn-off clock of inactive
modules.
 Dynamic Voltage-Frequency Scaling
 Low power state for DRAM, disks (spin-down)
 Overclocking some cores and turning off other
cores
Trends
in
Power
and
Energy

Static Power
 Static power consumption
 Leakage current flows even when a transistor
is off
 Leakage can be as high as 50% due to large
SRAM caches that need power to maintain
values.
 Powerstatic = Currentstatic x Voltage
 Scales with number of transistors
 To reduce: power gating (i.e., turn off the
power supply).
Trends
in
Power
and
Energy

Trends in Cost
 Cost driven down by learning curve
 Yield (% of manufactured devices that survive
testing).
 DRAM: price closely tracks cost
 Microprocessors: price depends on
volume (less time to get down the learning
curve and increase in manufacturing
efficiency)
 10% less for each doubling of volume
Trends
in
Cost

Integrated Circuit Cost
 Integrated circuit
 Bose-Einstein formula:
 Defects per unit area = 0.016-0.057 defects per square cm (2010)
 N = process-complexity factor = 11.5-15.5 (40 nm, 2010)
Trends
in
Cost
# dies along the
wafer’s perimeter
empirical formula

Dependability
 Service Level Agreement (SLA) or Service Level
Objectives (SLO)
 Module reliability
 Mean time to failure (MTTF) – reliability measure
 Mean time to repair (MTTR) – service interruption
 Mean time between failures (MTBF) = MTTF + MTTR
 Availability = MTTF / MTBF
Dependability

Dependability Example
 A disk subsystem has the following components
and MTTF values:
 Assume that lifetimes are exponentially
distributed and independent, what is the
system’s MTTF?
Dependability
Component MTTF (hours)
10 disks Each at 1,000,000
hours
1 ATA controller 500,000
1 power supply 200,000
1 fan 200,00
1 ATA cable 1,000,000

Dependability Example Cont’d
Dependability
Component MTTF (hours)
10 disks Each at 1,000,000
hours
1 ATA controller 500,000
1 power supply 200,000
1 fan 200,00
1 ATA cable 1,000,000
Failure Ratesyst
10 
1

1

1

1

1
1,000,000 500,000 200,000 200,000 1,000,000
 23,000/billion hours
MTTFsyst
Failure Ratesyst
1
  43,500 hours  4.96 yrs

Measuring Performance
 Typical performance metrics:
 Response time (of interest to users)
 Throughput (of interest to operators)
 Speedup of X relative to Y
 Execution timeY / Execution timeX
 Execution time
 Wall clock time: includes all system overheads
 CPU time: only computation time
 Benchmarks
 Kernels (e.g., matrix multiply)
 Toy programs (e.g., sorting)
 Synthetic benchmarks (e.g., Dhrystone)
 Benchmark suites (e.g., SPEC, TPC)
Measuring
Performance

SPECRate and SPECRatio
 SPECrate: a throughput metrics. Measures the number jobs of a
given type that can be processed in a given time interval.
 SPECratio = ratio between elapsed time for a given job at a
reference machines and the elapsed time of the same job at a given
machine.
Measuring
Performance
reference
Execution Time 10.5sec
Execution Timemachine A  5.25sec
SPECratiomachine A 10.5/5.25  2
 Comparing machines A and B:
Execution Timemachine B  21sec
Execution Timemachine A  5.25sec
Execution Timereference
Execution Timereference
Execution Timemachine B
SPECratiomachine A
 Execution Timemachine A
 Execution Timemachine B
SPECratiomachine B Execution Timemachine A
 21/5.25  4
Copyright © 2013, Daniel A. Menasce 41

Geometric Mean of SPECratios
 When computing the average of SPECratios one should use the
geometric mean and not the arithmetic mean:
n
Geometric mean = n xi
i=1
Measuring
Performance
Program SPECRatio
A 10.2
B 21.5
C 15.2
Geometric mean 14.94
Geometric mean = 3
10.2  21.5 15.5  3 3,333.36 14.94
Copyright © 2013, Daniel A. Menasce 42

Principles of Computer Design
 Take Advantage of Parallelism
 e.g., multiple processors, disks, memory banks,
pipelining, multiple functional units
 Principle of Locality
 Reuse of data and instructions
 Focus on the Common Case
 Amdahl’s Law
Principles

Principles of Computer Design
 The Processor Performance Equation
Principles
(average) Clock cycles per
instruction

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