1 cs 501 spring 2007 cs 501: software engineering lecture 22 performance of computer systems

33
1 CS 501 Spring 2007 CS 501: Software Engineering Lecture 22 Performance of Computer Systems

Post on 21-Dec-2015

217 views

Category:

Documents


3 download

TRANSCRIPT

1 CS 501 Spring 2007

CS 501: Software Engineering

Lecture 22

Performance of Computer Systems

2 CS 501 Spring 2007

Administration

Next week

Tuesday, April 17: no class

Thursday, April 19: Quiz 4

Remember that the quiz may contain material from all lectures up to and including today.

3 CS 501 Spring 2007

Performance of Computer Systems

In most computer systems

The cost of people is much greater than the cost of hardware

Yet performance is important

Future loads may be much greater than predicted

A single bottleneck can slow down an entire system

The choice of systems architecture may lead to a system that places great demands on the skills of the implementers.

4 CS 501 Spring 2007

Performance Challenges

Tasks

• Predict performance problems before a system is implemented

• Identify causes and fix problems after a system is implemented

Basic techniques

• Understand how the underlying hardware and networking components interact when executing the system

• For each component calculate the capacity and load

• Identify components that are near peak capacity

5 CS 501 Spring 2007

Understand the Interactions between Hardware and Software

Example: execution of http://www.cs.cornell.edu/

Client Servers

domain name service

TCP connection

HTTP get

6 CS 501 Spring 2007

Understand the Interactions between Hardware and Software

:Thread :Toolkit :ComponentPeer target:HelloWorld

runrun callbackLoop

handleExpose

paint

7 CS 501 Spring 2007

Decompress

Stream audioStream video

fork

join

start state

stop state

Understand Interactions between Hardware and Software

8 CS 501 Spring 2007

Look for Bottlenecks

Possible areas of congestion

Network load

Database accesshow many joins to build a record?

Locks and sequential processing

CPU performance is rarely a factor, except in mathematical algorithms. More likely bottlenecks are:

Reading data from disk (including paging)

Moving data from memory to CPU

9 CS 501 Spring 2007

Timescale

Operations per second

CPU instruction: 1,000,000,000

Disk latency: 60 read: 25,000,000 bytes

Network LAN: 10,000,000 bytesdial-up modem: 6,000 bytes

10 CS 501 Spring 2007

Predicting System Performance

• Direct measurement on subsystem (benchmark)

• Mathematical models

• Simulation

• Rules of thumb

All require detailed understanding of the interaction between software and hardware systems.

11 CS 501 Spring 2007

Look for Bottlenecks: Utilization

utilization =

mean service timemean inter-arrival time

When the utilization of any hardware component exceeds 30%, be prepared for congestion.

Peak loads and temporary increases in demand can be much greater than the average.

Utilization is the proportion of the capacity of a service that is used on average.

12 CS 501 Spring 2007

Mathematical Models

Queueing theory

Good estimates of congestion can be made for single-server queues with:

• arrivals that are independent, random events (Poisson process)

• service times that follow families of distributions (e.g., negative exponential, gamma)

Many of the results can be extended to multi-server queues.

13 CS 501 Spring 2007

Mathematical Models: Queues

arrive wait in line service depart

Single server queue

14 CS 501 Spring 2007

Queues

arrive wait in line

service

depart

Multi-server queue

15 CS 501 Spring 2007

Behavior of Queues: Utilization

meandelay

utilization10

16 CS 501 Spring 2007

Software development for high-performance systems

High-performance computing:

• Large data collections (e.g., Amazon)• Internet services (e.g., Google)• Large computations (e.g, weather forecasting)

Must balance cost of hardware against cost of software development

Some configurations are very difficult to program and debug

Sometimes it is possible to isolate applications programmers from the system complexities

CS 530, Architecture of Large-Scale Information Systems

17 CS 501 Spring 2007

Software development for high-performance systems

Wayback Machine

INTERNET ARCHIVE

Text indexes

Web Collection

File server

Computer clusters

Text indexesPage store

Structure database

National super-

computers

CORNELL UNIVERSITY

TeraGrid

18 CS 501 Spring 2007

Software development: very large databases

Database: symmetric multiprocessing (SMP) on a shared memory machine.

WebLab uses half a 32 CPU machine with 64 GBytes memory

• Hardware is expensive.

• Software development uses conventional database system, which does not need specialist knowledge.

• But high specialist knowledge is needed to organize data on disks, connect disks to memory, configure database for backup and restore.

19 CS 501 Spring 2007

Software development: cluster computing

Computer clusters are built on large numbers of commodity computers. Hardware is cheap(ish).

Web Lab uses two cluster (Linux and Windows). The Linux cluster is:108 2.4 MHz dual processors, each with 2 GB memory, and 72 GB disk, connected by 10 Gbit/sec Ethernet.

• Programming requires software techniques that understand the hardware configuration, e.g., MPI (message passing interface).

• New techniques (e.g., map/reduce) emphasize simple applications code, so that moderately skilled programmers do not need to understand complexities of parallel programming.

20 CS 501 Spring 2007

Measurements on Operational Systems

• Benchmarks: Run system on standard problem sets, sample inputs, or a simulated load on the system.

• Instrumentation: Clock specific events.

If you have any doubt about the performance of part

of a system, experiment with a simulated load.

21 CS 501 Spring 2007

Example: Web Laboratory

Benchmark: Throughput v. number of CPUs on SMP

total MB/s

average / CPU

22 CS 501 Spring 2007

Techniques: Simulation

Model the system as set of states and events

advance simulated time determine which events occurred update state and event listrepeat

Discrete time simulation: Time is advanced in fixed steps (e.g., 1 millisecond)

Next event simulation: Time is advanced to next event

Events can be simulated by random variables (e.g., arrival of next customer, completion of disk latency)

23 CS 501 Spring 2007

Case Study: Performance of Disk Array

When many transaction use a disk array, each transaction must:

wait for specific disk platter

wait for I/O channel

signal to move heads on disk platter

wait for I/O channel

pause for disk rotation

read data

Close agreement between: results from queuing theory, simulation, and direct measurement (within 15%).

24 CS 501 Spring 2007

Example: Web Laboratory

Balance of Resources

Ideal Realistic

Networking 500 Mbit/sec 100 Mbit/sec

Data online allfew crawls/year

Metadata online all all?

Disk 750 TB 240 TB

Tape archive all few crawls/year

Computers research sharedseparate with storage

25 CS 501 Spring 2007

Fixing Bad Performance

If a system performs badly, begin by identifying the cause:

• Instrumentation. Add timers to the code. Often this will reveal that the delays are centered in one specific part of the system.

• Test loads. Run the system with varying loads, e.g., high transaction rates, large input files, many users, etc. This may reveal the characteristics of when the system runs badly.

• Design and code reviews. Have a team review the system design and suspect sections of code for performance problems. This may reveal an algorithm that is running very slowly, e.g., a sort, locking procedure, etc.

Fix the underlying cause or the problem will return!

26 CS 501 Spring 2007

Predicting Performance Change:Moore's Law

Original version:

The density of transistors in an integrated circuit will double every year. (Gordon Moore, Intel, 1965)

Current version:

Cost/performance of silicon chips doubles every 18 months.

27 CS 501 Spring 2007

Moore's Law: Rules of Thumb

Planning assumptions:

Every year: cost/performance of silicon chips improves 25% cost/performance of magnetic media improves 30%

10 years = 100:120 years = 10,000:1

28 CS 501 Spring 2007

Moore's Law and System Design

Design system: 2006

Production use: 2009

Withdrawn from production: 2019

Processor speeds: 1 1.9 28

Memory sizes: 1 1.9 28

Disk capacity: 1 2.2 51

System cost: 1 0.4 0.01

29 CS 501 Spring 2007

Moore's Law Example

Will this be a typical personal computer?

2007 2019

Processor 2 GHz 50 GHz

Memory 512 MB 14 GB

Disc 50 GB 2 TB

Network 100 Mb/s 1 Gb/s

Surely there will be some fundamental changes in how this this power is packaged and used.

30 CS 501 Spring 2007

Parkinson's Law

Original: Work expands to fill the time available. (C. Northcote Parkinson)

Planning assumptions:

(a) Demand will expand to use all the hardware available.

(b) Low prices will create new demands.

(c) Your software will be used on equipment that you have not envisioned.

31 CS 501 Spring 2007

False Assumptions from the Past

Unix file system will never exceed 2 Gbytes (232 bytes).

AppleTalk networks will never have more than 256 hosts (28 bits).

GPS software will not last 1024 weeks.

Nobody at Dartmouth will ever earn more than $10,000 per month.

etc., etc., .....

32 CS 501 Spring 2007

Moore's Law and the Long Term

1965

What level?

2005

33 CS 501 Spring 2007

Moore's Law and the Long Term

1965 When?

What level?

2006?

Within your working life?