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1 CS 501 Spring 2005
CS 501: Software Engineering
Lecture 22
Performance of Computer Systems
2 CS 501 Spring 2005
Administration
3 CS 501 Spring 2005
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
4 CS 501 Spring 2005
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.
5 CS 501 Spring 2005
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
6 CS 501 Spring 2005
Moore's Law and System Design
Design system: 2005
Production use: 2008
Withdrawn from production: 2018
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
7 CS 501 Spring 2005
Moore's Law Example
Will this be a typical personal computer?
2005 2018
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.
8 CS 501 Spring 2005
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.
9 CS 501 Spring 2005
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., .....
10 CS 501 Spring 2005
Moore's Law and the Long Term
1965
What level?
2005
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Moore's Law and the Long Term
1965 When?
What level?
2005?
Within your working life?
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Predicting System Performance
• Mathematical models
• Simulation
• Direct measurement
• Rules of thumb
All require detailed understanding of the interaction between software and hardware systems.
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Understand the Interactions between Hardware and Software
Example: execution of http://www.cs.cornell.edu/
Client Servers
domain name service
TCP connection
HTTP get
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Understand the Interactions between Hardware and Software
:Thread :Toolkit :ComponentPeer target:HelloWorld
runrun callbackLoop
handleExpose
paint
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Decompress
Stream audioStream video
fork
join
start state
stop state
Understand Interactions between Hardware and Software
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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
17 CS 501 Spring 2005
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.
18 CS 501 Spring 2005
Mathematical Models: Queues
arrive wait in line service depart
Single server queue
19 CS 501 Spring 2005
Queues
arrive wait in line
service
depart
Multi-server queue
20 CS 501 Spring 2005
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.
21 CS 501 Spring 2005
Behavior of Queues: Utilization
meandelay
utilization10
22 CS 501 Spring 2005
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!
23 CS 501 Spring 2005
Techniques for Eliminating Bottlenecks
Serial and Parallel Processing
Single thread v. multi-thread
e.g., Unix fork
Granularity of locks on data
e.g., record locking
Network congestion
e.g., back-off algorithms
24 CS 501 Spring 2005
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.
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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)
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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
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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%).
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