Computer Systems: A Programmer's Perspective - Course Syllabus, Lecture notes of Network Technologies and TCP/IP

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Introduction

to^ Computer

Systems

Spring^2009 st^1 Lecture,^

thAug. 25 Instructors: Roger^ Dannenberg

and^ Greg^ Ganger

The^ course

that^ gives

CMU^ its

“Zip”!

Overview^ „^ Course

role^ and^ theme

„^ Five^ realities „^ Logistics

Course

Perspective

„^ Most^ Systems

Courses^ are

Builder‐Centric

ƒ^ Computer

Architecture ƒ Design^ pipelined^ processor

in^ Verilog

ƒ^ Operating

Systems ƒ Implement^ large^ portions

of^ operating

system

ƒ^ Embedded

Systems ƒ Implement^ small‐scale

embedded^

systems

ƒ^ Networking^ ƒ^ Implement

and^ simulate

network^ protocols

Course

Perspective

(Cont.)

„^ Our^ Course

is^ Programmer

‐Centric

ƒ^ Purpose^ is

to^ show^ how

by^ knowing

more^ about

the^ underlying

system,^ one

can^ be^ more

effective^ as

a^ programmer

ƒ^ Enable^ you

to ƒ Write programs^ that

are^ more^ reliable

and^ efficient

ƒ^ Incorporate

features^ that

require^ hooks

into^ OS

• E.g.,^ concurrency,

signal^ handlers

ƒ^ Not^ just^ a

course^ for^ dedicated

hackers ƒ^ We^ bring^

out^ the^ hidden

hacker^ in^ everyone

ƒ^ Cover^ material

in^ this^ course

that^ you^ won’t

see^ elsewhere

Great^ Reality

Int’s are

not^ Integers,

Float’s

are^ not

Reals

„^ Example

2 1: Is x ≥^ 0? ƒ Float’s: Yes! ƒ Int’s: ƒ 40000 * 40000

‐‐>^1600000000

ƒ^50000 *^50000

‐‐>^ ??

„^ Example

2:^ Is^ (x^ +^ y)

+^ z^ =^ x^ +

(y^ +^ z)?

ƒ^ Unsigned

&^ Signed^ Int’s:

Yes!

ƒ^ Float’s:^ ƒ^ (1e

+^ ‐1e20)^ +^ 3.

‐‐>^ 3.

ƒ^ 1e20^ +^ (‐

1e20^ +^ 3.14)

‐‐>^ ??

Computer

Arithmetic

„^ Does^ not

generate

random^ values

ƒ^ Arithmetic

operations^

have^ important

mathematical

properties

„^ Cannot

assume^ all

“usual” mathematical

properties

ƒ^ Due^ to^ finiteness

of^ representations

ƒ^ Integer^ operations

satisfy^ “ring” properties ƒ^ Commutativity,

associativity,

distributivity

ƒ^ Floating^ point

operations^

satisfy^ “ordering” properties ƒ^ Monotonicity,

values^ of^ signs

„^ Observation^ ƒ^ Need

to^ understand

which^ abstractions

apply^ in^ which

contexts

ƒ^ Important

issues^ for^ compiler

writers^ and

serious^ application

programmers

Great^ Reality

#3:^ Memory

Matters

Random

Access^ Memory

Is^ an^ Unphysical

Abstraction

„^ Memory

is^ not^ unbounded ƒ It must^ be^ allocated

and^ managed

ƒ^ Many^ applications

are^ memory

dominated

„^ Memory

referencing

bugs^ especially

pernicious

ƒ^ Effects^ are

distant^ in^ both

time^ and^ space

„^ Memory

performance

is^ not^ uniform

ƒ^ Cache^ and

virtual^ memory

effects^ can^

greatly^ affect

program

performance ƒ Adapting^ program

to^ characteristics

of^ memory

system^ can

lead^ to

major^ speed

improvements

Memory

Referencing

Bug^ Example

double fun(int i){ volatile double d[1] = {3.14};volatile long int a[2];a[i] = 1073741824; / Possibly out of bounds /return d[0];} fun(0)^ –>

3. fun(1)^ –>

3. fun(2)^ –>

3. fun(3)^ –>

2. fun(4)^ –>

3.14, then segmentation fault

Memory

Referencing

Errors

„^ C^ and^ C++

do^ not^ provide

any^ memory

protection

ƒ^ Out^ of^ bounds

array^ references

ƒ^ Invalid^ pointer

values

ƒ^ Abuses^ of

malloc/free

„^ Can^ lead

to^ nasty^ bugs ƒ Whether^ or^ not^ bug

has^ any^ effect

depends^ on

system^ and

compiler

ƒ^ Action^ at

a^ distance ƒ Corrupted^ object

logically^ unrelated

to^ one^ being

accessed

ƒ^ Effect^ of^ bug

may^ be^ first

observed^ long

after^ it^ is^ generated

„^ How^ can

I^ deal^ with

this?

ƒ^ Program^

in^ Java^ or^ ML

ƒ^ Understand

what^ possible

interactions

may^ occur

ƒ^ Use^ or^ develop

tools^ to^ detect

referencing

errors

Great^ Reality

#4:^ There’s

more^

to

performance

than^ asymptotic

complexity

„^ Constant

factors^ matter

too!

„^ And^ even

exact^ op^

count^ does

not^ predict

performance

ƒ^ Easily^ see

10:1^ performance

range^ depending

on^ how^ code

written

ƒ^ Must^ optimize

at^ multiple^

levels:^ algorithm,

data^ representations,

procedures,

and^ loops

„^ Must^ understand

system^ to

optimize^

performance

ƒ^ How^ programs

compiled^ and

executed

ƒ^ How^ to^ measure

program^ performance

and^ identify

bottlenecks

ƒ^ How^ to^ improve

performance

without^ destroying

code^ modularity

and^ generality

Great^ Reality

Computers

do^ more

than^ execute

programs

„^ They^ need

to^ get^ data

in^ and^ out

ƒ^ I/O^ system

critical^ to^ program

reliability^ and

performance

„^ They^ communicate

with^ each

other^ over

networks

ƒ^ Many^ system

‐level^ issues

arise^ in^ presence

of^ network

ƒ^ Concurrent

operations^

by^ autonomous

processes

ƒ^ Coping^ with

unreliable^ media ƒ^ Cross^ platform

compatibility ƒ^ Complex^ performance

issues

Overview^ „^ Course

role^ and^ theme

„^ Five^ realities „^ Logistics

Textbooks^ „^ Randal

E.^ Bryant

and^ David

R.^ O’Hallaron,

ƒ^ “Computer

Systems:^ A^

Programmer’s

Perspective”,

Prentice^ Hall

ƒ^ http://csapp.cs.cmu.edu ƒ^ This^ book

really^ matters

for^ the^ course! ƒ^ How^ to^ solve

labs ƒ^ Practice^ problems

typical^ of^ exam

problems

„^ Brian^ Kernighan

and^ Dennis

Ritchie,

ƒ^ “The^ C^ Programming

Language,^ Second

Edition”,^ Prentice

Hall,^1988

Course

Components

„^ Lectures^ ƒ^ Higher

level^ concepts

„^ Recitations^ ƒ^ Applied

concepts,^ important

tools^ and^ skills

for^ labs,^ clarification

of

lectures,^ exam

coverage

„^ Labs^ (6)^ ƒ^ The^ heart

of^ the^ course

ƒ^2 or^3 weeks ƒ^ Provide^ in

‐depth^ understanding

of^ an^ aspect

of^ systems

ƒ^ Programming

and^ measurement

„^ Exams

(2^ +^ final) ƒ Test your^ understanding

of^ concepts

&^ mathematical

principles