Download ppt - Back to George One More Time Before they invented drawing boards, what did they go back to? If all the world is a stage, where is the audience sitting?

Back to George One More Time• Before they invented drawing boards, what did they

go back to?• If all the world is a stage, where is the audience

sitting?• If the #2 pencil is the most popular, why is it still #2?• If work is so terrific, how come they have to pay you

to do it?• If you ate pasta and antipasto, would you still be

hungry?• If you try to fail, and succeed, which have you done?• "People who think they know everything are a great

annoyance to those of us who do.” - Anon

O() Analysis Reasonable vs. Unreasonable

Algorithms Using O() Analysis in Design

Concurrent Systems Parallelism

Recipe for Determining O()

• Break algorithm down into “known” pieces

– We’ll learn the Big-Os in this section

• Identify relationships between pieces

– Sequential is additive

– Nested (loop / recursion) is multiplicative• Drop constants• Keep only dominant factor for each variable

Comparing Data Structures and Methods

Data Structure Traverse Search Insert

Unsorted L List N N 1

Sorted L List N N N

Unsorted Array N N 1

Sorted Array N Log N N

Binary Tree N N 1

BST N N N

F&B BST N Log N Log N

LB

Reasonable vs. UnreasonableAlgorithms

Algorithmic Performance Thus Far

• Some examples thus far:– O(1) Insert to front of linked list– O(N) Simple/Linear Search– O(N Log N) MergeSort– O(N2) BubbleSort

• But it could get worse:– O(N5), O(N2000), etc.

An O(N5) Example

For N = 256

N5 = 2565 = 1,100,000,000,000

If we had a computer that could execute a million instructions per second…

• 1,100,000 seconds = 12.7 days to complete

But it could get worse…

The Power of Exponents

A rich king and a wise peasant…

The Wise Peasant’s Pay

Day(N) Pieces of Grain

1 2

2 4

3 8

4 16

...

2N

63 9,223,000,000,000,000,000

64 18,450,000,000,000,000,000

How Bad is 2N?

• Imagine being able to grow a billion (1,000,000,000) pieces of grain a second…

• It would take– 585 years to grow enough grain

just for the 64th day– Over a thousand years to fulfill

the peasant’s request!

So the King cut off the peasant’s head.

LB

The Towers of Hanoi

A B C

Goal: Move stack of rings to another peg– Rule 1: May move only 1 ring at a time– Rule 2: May never have larger ring on top of

smaller ring

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

The Towers of Hanoi

A B C

Towers of Hanoi - Complexity

For 1 rings we have 1 operations.




In general, the cost is 2N – 1 = O(2N)Each time we increment N, we double the

amount of work.

This grows incredibly fast!

Towers of Hanoi (2N) Runtime

For N = 64

2N = 264 = 18,450,000,000,000,000,000

If we had a computer that could execute a million instructions per second…

• It would take 584,000 years to complete

But it could get worse…

The Bounded Tile Problem

Match up the patterns in thetiles. Can it be done, yes or no?

The Bounded Tile Problem

Matching tiles

Tiling a 5x5 Area

25 availabletiles remaining

Tiling a 5x5 Area


Tiling a 5x5 Area


Tiling a 5x5 Area


Tiling a 5x5 Area


Analysis of the Bounded Tiling Problem

Tile a 5 by 5 area (N = 25 tiles)1st location: 25 choices2nd location: 24 choicesAnd so on…

Total number of arrangements:– 25 * 24 * 23 * 22 * 21 * .... * 3 * 2 * 1– 25! (Factorial) =

15,500,000,000,000,000,000,000,000Bounded Tiling Problem is O(N!)

Tiling (N!) Runtime

For N = 25

25! = 15,500,000,000,000,000,000,000,000

If we could “place” a million tiles per second…

• It would take 470 billion years to complete

Why not a faster computer?

A Faster Computer

• If we had a computer that could execute a trillion instructions per second (a million times faster than our MIPS computer)…

• 5x5 tiling problem would take 470,000 years

• 64-ring Tower of Hanoi problem would take 213 days

Why not an even faster computer!

The Fastest Computer Possible?

• What if:– Instructions took ZERO time to execute– CPU registers could be loaded at the speed of

light

• These algorithms are still unreasonable!• The speed of light is only so fast!

Where Does this Leave Us?

• Clearly algorithms have varying runtimes.

• We’d like a way to categorize them:

– Reasonable, so it may be useful – Unreasonable, so why bother running

Performance Categories of Algorithms

Sub-linear O(Log N)

Linear O(N)

Nearly linear O(N Log N)

Quadratic O(N2)

Exponential O(2N)

O(N!)

O(NN)

Po

lyn

om

ial

Reasonable vs. Unreasonable

Reasonable algorithms have polynomial factors– O (Log N)– O (N)– O (NK) where K is a constant

Unreasonable algorithms have exponential factors– O (2N)– O (N!)– O (NN)

Reasonable vs. Unreasonable

Reasonable algorithms• May be usable depending upon the input size

Unreasonable algorithms• Are impractical and useful to theorists• Demonstrate need for approximate solutions

Remember we’re dealing with large N (input size)

Two Categories of Algorithms

2 4 8 16 32 64 128 256 512 1024Size of Input (N)

1035

1030

1025

1020

1015

trillionbillionmillion100010010

N

N5

2NNN

Unreasonable

Don’t Care!

Reasonable

Ru

nti

me

Summary

• Reasonable algorithms feature polynomial factors in their O() and may be usable depending upon input size.

• Unreasonable algorithms feature exponential factors in their O() and have no practical utility.

Questions?

Using O() Analysis in Design

Air Traffic Control

Coast, add, delete

Conflict Alert

Problem Statement

• What data structure should be used to store the aircraft records for this system?

• Normal operations conducted are:– Data Entry: adding new aircraft entering the

area– Radar Update: input from the antenna– Coast: global traversal to verify that all aircraft

have been updated [coast for 5 cycles, then drop]

– Query: controller requesting data about a specific aircraft by location

– Conflict Analysis: make sure no two aircraft are too close together

Air Traffic Control System

Freq LLU LLS AU AS BT F/B BST

#1 15 1 N 1 N 1 LogN

#2 12 N^2 N^2 N^2 NlogN N^2 NlogN

#3 60 N N N N N N

#4 1 N N N LogN N LogN

#5 12 N^2 N^2 N^2 NlogN N^2 NlogN

Program Algorithm Freq 1. Data Entry / Exit Insert 15 2. Radar Data Update N*Search 12 3. Coast / Drop Traverse 60 4. Query Search 1 5. Conflict Analysis Traverse*Search 12

Questions?

Concurrent Systems

Sequential Processing

• All of the algorithms we’ve seen so far are sequential:– They have one “thread” of execution– One step follows another in sequence– One processor is all that is needed to

run the algorithm

A Non-sequential Example

• Consider a house with a burglar alarm system.• The system continually monitors:

– The front door– The back door– The sliding glass door– The door to the deck– The kitchen windows– The living room windows– The bedroom windows

• The burglar alarm is watching all of these “at once” (at the same time).

Another Non-sequential Example

• Your car has an onboard digital dashboard that simultaneously:– Calculates how fast you’re going and

displays it on the speedometer– Checks your oil level– Checks your fuel level and calculates

consumption– Monitors the heat of the engine and

turns on a light if it is too hot– Monitors your alternator to make sure it

is charging your battery

Concurrent Systems

• A system in which:– Multiple tasks can be executed at

the same time– The tasks may be duplicates of

each other, or distinct tasks– The overall time to perform the

series of tasks is reduced

Advantages of Concurrency

• Concurrent processes can reduce duplication in code.

• The overall runtime of the algorithm can be significantly reduced.

• More real-world problems can be solved than with sequential algorithms alone.

• Redundancy can make systems more reliable.

Disadvantages of Concurrency

• Runtime is not always reduced, so careful planning is required

• Concurrent algorithms can be more complex than sequential algorithms

• Shared data can be corrupted• Communications between tasks is

needed

Achieving Concurrency

CPU 1 CPU 2

Memory

bus

• Many computers today have more than one processor (multiprocessor machines)

Achieving Concurrency

CPU

task 1task 2

task 3ZZZZ

ZZZZ

• Concurrency can also be achieved on a computer with only one processor:– The computer “juggles” jobs, swapping its

attention to each in turn– “Time slicing” allows many users to get CPU

resources– Tasks may be suspended while they wait for

something, such as device I/O

Concurrency vs. Parallelism

• Concurrency is the execution of multiple tasks at the same time, regardless of the number of processors.

• Parallelism is the execution of multiple processors on the same task.

Types of Concurrent Systems

• Multiprogramming• Multiprocessing• Multitasking• Distributed Systems

Multiprogramming

• Share a single CPU among many users or tasks.

• May have a time-shared algorithm or a priority algorithm for determining which task to run next

• Give the illusion of simultaneous processing through rapid swapping of tasks (interleaving).

MultiprogrammingMemoryUser 1User 2

CPU

User1 User2

Multiprogramming

1

2

3

4

1 2 3 4CPU’s

Tasks/Users

Multiprocessing

• Executes multiple tasks at the same time

• Uses multiple processors to accomplish the tasks

• Each processor may also timeshare among several tasks

• Has a shared memory that is used by all the tasks

MultiprocessingMemoryUser 1: Task1User 1: Task2User 2: Task1

CPU

User1 User2

CPU CPU

Multiprocessing

1

2

3

4

1 2 3 4CPU’s

Tasks/Users

SharedMemory

Multitasking

• A single user can have multiple tasks running at the same time.

• Can be done with one or more processors.

• Used to be rare and for only expensive multiprocessing systems, but now most modern operating systems can do it.

MultitaskingMemoryUser 1: Task1User 1: Task2User 1: Task3

CPU

User1

Multitasking

1

2

3

4

1 2 3 4CPU’s

Tasks Single User

Distributed Systems

CentralBank

ATM Buford ATM Perimeter

ATM Student CtrATM North Ave

Multiple computers working together with no central program “in charge.”

Distributed Systems

• Advantages:– No bottlenecks from sharing processors– No central point of failure– Processing can be localized for efficiency

• Disadvantages:– Complexity– Communication overhead– Distributed control

Questions?

Parallelism

Parallelism

• Using multiple processors to solve a single task.

• Involves:– Breaking the task into meaningful

pieces– Doing the work on many

processors– Coordinating and putting the

pieces back together.

Parallelism

CPU

Memory

NetworkInterface

Parallelism

1

2

3

4

1 2 3 4CPU’s

Tasks

Pipeline Processing

Repeating a sequence of operations or pieces of a task.

Allocating each piece to a separate processor and chaining them together produces a pipeline, completing tasks faster.

A B C Dinput output

Example

• Suppose you have a choice between a washer and a dryer each having a 30 minutes cycle or

• A washer/dryer with a one hour cycle

• The correct answer depends on how much work you have to do.

One Load

wash dry

combo

TransferOverhead

Three Loads

wash dry

combo

wash

wash

dry

dry

combocombo

Examples of Pipelined Tasks

• Automobile manufacturing• Instruction processing within a computer

1 5432

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

A

B

C

D1 2 3 4 5 6 70

time

Task Queues

P1 P2 P3 Pn

Super Task Queue

• A supervisor processor maintains a queue of tasks to be performed in shared memory.

• Each processor queries the queue, dequeues the next task and performs it.

• Task execution may involve adding more tasks to the task queue.

Parallelizing Algorithms

How much gain can we get from parallelizing an algorithm?

Parallel Bubblesort

93 87 74 65 57 45 33 27

9387 7465 5745 3327

9387 7465 5745 3327

We can use N/2 processors to do all the comparisons at once, “flopping” the pair-wise comparisons.

Runtime of Parallel Bubblesort

9387 7465 5745 33273

9387 7465 5745 33274

9387 7465 5745 33275

9387 7465 5745 33276

9387 7465 5745 33277

93877465574533278

Completion Time of Bubblesort

• Sequential bubblesort finishes in N2 time.• Parallel bubblesort finishes in N time.

Bubble Sort

parallel

O(N2)

O(N)

Product Complexity

• Got done in O(N) time, better than O(N2)• Each time “chunk” does O(N) work• There are N time chunks.• Thus, the amount of work is still O(N2)

• Product complexity is the amount of work per “time chunk” multiplied by the number of “time chunks” – the total work done.

Ceiling of Improvement

• Parallelization can reduce time, but it cannot reduce work. The product complexity cannot change or improve.

• How much improvement can parallelization provide?– Given an O(NLogN) algorithm and Log N

processors, the algorithm will take at least O(?) time.

– Given an O(N3) algorithm and N processors, the algorithm will take at least O(?) time.

O(N) time.

O(N2) time.

Number of Processors

• Processors are limited by hardware.• Typically, the number of processors is a

power of 2• Usually: The number of processors is a

constant factor, 2K

• Conceivably: Networked computers joined as needed (ala Borg?).

Adding Processors

• A program on one processor– Runs in X time

• Adding another processor– Runs in no more than X/2 time– Realistically, it will run in X/2 + time

because of overhead• At some point, adding processors will not

help and could degrade performance.

Overhead of Parallelization

• Parallelization is not free.• Processors must be controlled and coordinated.• We need a way to govern which processor does

what work; this involves extra work.• Often the program must be written in a special

programming language for parallel systems.• Often, a parallelized program for one machine

(with, say, 2K processors) doesn’t work on other machines (with, say, 2L processors).

What We Know about Tasks

• Relatively isolated units of computation• Should be roughly equal in duration• Duration of the unit of work must be much

greater than overhead time• Policy decisions and coordination

required for shared data• Simpler algorithm are the easiest to

parallelize

Questions?

More?

Matrix Multiplication

443434332432143134

44

34

24

14

3433323134

1

...

....

....

....

....

...

....

....

babababac

b

b

b

b

aaaac

bac

bacn

kkjikij

Inner Product Procedure

Procedure inner_prod(a, b, c isoftype in/out Matrix, i, j isoftype in Num)

// Compute inner product of a[i][*] and b[*][j]

Sum isoftype Num

k isoftype Num

Sum <- 0

k <- 1

loop

exitif(k > n)

sum <- sum + a[i][k] * b[k][j]

k < k + 1

endloop

endprocedure // inner_prod

Matrix definesa Array[1..N][1..N] of Num

N is // Declare constant defining size

// of arrays

Algorithm P_Demo

a, b, c isoftype Matrix Shared

server isoftype Num

Initialize(NUM_SERVERS)

// Input a and b here

// (code not shown)

i, j isoftype Num

i <- 1

loop

exitif(i > N)

server <- (i * NUM_SERVERS) DIV N

j <- 1

loop

exitif(j > N)

RThread(server, inner_prod(a, b, c, i, j ))

j <- j + 1

endloop

i <- i + 1

endloop

Parallel_Wait(NUM_SERVERS)

// Output c here

endalgorithm // P_Demo

Questions?