CENG 450 Computer Systems & Architecture Lecture 3 Amirali Baniasadi amirali@ece.uvic.ca

CENG 450Computer Systems &

Architecture

Lecture 3

Amirali Baniasadiamirali@ece.uvic.ca

Performance

Purchasing perspective given a collection of machines, which has the

best performance ? least cost ? best performance / cost ?

Design perspective faced with design options, which has the

best performance improvement ? least cost ? best performance / cost ?

Both require basis for comparison metric for evaluation

Our goal is to understand cost & performance implications of architectural choices

Two notions of “performance”

° Time to do the task (Execution Time)

– execution time, response time, latency

° Tasks per day, hour, week, sec, ns. ..

– throughput, bandwidth

Response time and throughput often are in opposition

DC to Paris

6.5 hours

3 hours

Plane

Boeing 747

BAD/Sud Concorde

Speed

610 mph

1350 mph

Passengers

470

132

Throughput

286,700

178,200

Which has higher performance?

Example

• Time of Concorde vs. Boeing 747?

• Concord is 1350 mph / 610 mph = 2.2 times faster

= 6.5 hours / 3 hours

• Throughput of Concorde vs. Boeing 747 ?

• Concord is 178,200 pmph / 286,700 pmph = 0.62 “times faster”

• Boeing is 286,700 pmph / 178,200 pmph = 1.6 “times faster”

• Boeing is 1.6 times (“60%”)faster in terms of throughput

• Concord is 2.2 times (“120%”) faster in terms of flying time

We will focus primarily on execution time for a single job

Definitions

Performance is in units of things-per-second bigger is better

If we are primarily concerned with response time performance(x) = 1

execution_time(x)

" X is n times faster than Y" means

Performance(X)

n = ----------------------

Performance(Y)

Performance measurement

How about collection of programs? Example:

Three machines: A, B and C. Two Programs: P1 and P2.

A B C W(1) W(2) W(3)

P1 1 10 20 .5 .9 .99

P2 1000 100 20 .5 .1 .01

W(1) 500.5 55 20

Arithmetic mean: Weight i * Time i

W(2) 91.9 18 20

W(3) 2 10 20

Performance measurement

Other option: Geometric Means (Self study pages 37-39 text book)

Metrics of performance

Compiler

Programming Language

Application

DatapathControl

Transistors Wires Pins

ISA

Function Units

(millions) of Instructions per second – MIPS(millions) of (F.P.) operations per second – MFLOP/s

Cycles per second (clock rate)

Megabytes per second

Answers per monthOperations per second

Relating Processor Metrics

CPU execution time = CPU clock cycles X clock cycle time

or CPU execution time = CPU clock cycles ÷ clock rate

CPU clock cycles= Instructions X avg. clock cycles per instr.

or CPI = CPU clock cycles÷ Instructions

CPI tells us something about the Instruction Set Architecture, the Implementation of that architecture, and the program measured

Aspects of CPU PerformanceCPU time = Seconds = Instructions x Cycles x

Seconds

Program Program Instruction Cycle

CPU time = Seconds = Instructions x Cycles x Seconds

Program Program Instruction Cycle

instr. count CPI clock rate

Program

Compiler

Instr. Set Arch.

Organization

Technology

Aspects of CPU PerformanceCPU time = Seconds = Instructions x Cycles x

Seconds

Program Program Instruction Cycle

CPU time = Seconds = Instructions x Cycles x Seconds

Program Program Instruction Cycle

instr count CPI clock rate

Program X

Compiler X (x)

Instr. Set. X X

Organization X X

Technology X

Organizational Trade-offs

Compiler

Programming Language

Application

DatapathControl

Transistors Wires Pins

ISA

Function Units

Instruction Mix

Cycle Time

CPI

CPI

CPU time = ClockCycleTime * SUM CPI * Ii = 1

n

ii

CPI = SUM CPI * F where F = I i = 1

n

i i i i

Instruction Count

"instruction frequency"

Invest Resources where time is Spent!

CPI = (CPU Time * Clock Rate) / Instruction Count = Clock Cycles / Instruction Count

“Average cycles per instruction”

Example (RISC processor)

Typical Mix

Base Machine (Reg / Reg)

Op Freq Cycles CPI(i) % Time

ALU 50% 1 .5 23%

Load 20% 5 1.0 45%

Store 10% 3 .3 14%

Branch 20% 2 .4 18%

2.2

How much faster would the machine be if a better data cachereduced the average load time to 2 cycles?

How does this compare with using branch prediction to shave a cycle off the branch time?

What if two ALU instructions could be executed at once?

Example (RISC processor)

Typical Mix

Base Machine (Reg / Reg)

Op Freq Cycles CPI(i) % Time

ALU 50% 1 .5 23%

Load 20% 5 1.0 45%

Store 10% 3 .3 14%

Branch 20% 2 .4 18%

2.2

How much faster would the machine be if:A) Loads took “0” cycles? B) Stores took “0” cycles?C) ALU ops took “0” cycles?D)Branches took “0” cycles?

MAKE THE COMMON CASE FAST

Amdahl's Law

Speedup due to enhancement E:

ExTime w/o E Performance w/ E

Speedup(E) = -------------------- = ---------------------

ExTime w/ E Performance w/o E

Suppose that enhancement E accelerates a fraction F of the task

by a factor S and the remainder of the task is unaffected then,

ExTime(with E) = ((1-F) + F/S) X ExTime(without E)

Speedup(with E) = ExTime(without E) ÷ ((1-F) + F/S) X ExTime(without E)

Speedup(with E) =1/ ((1-F) + F/S)

Amdahl's Law-example

A new CPU makes Web serving 10 times faster. The old CPU spent 40% of the time on computation and 60% on waiting for I/O. What is the overall enhancement?

Fraction enhanced= 0.4

Speedup enhanced = 10

Speedup overall = 1 = 1.56

0.6 +0.4/10

Example from Quiz 1-2004

a)A program consists of 80% initialization code and of 20% code being the main iteration loop, which is run 1000 times. The total runtime of the program is 100 seconds. Calculate the fraction of the total run time needed for the initialization and the iteration. Which part would you optimize? B) The program should have a total run time of 60 seconds. How can this be achieved? (15 points)

Marketing Metrics

MIPS = Instruction Count / Time * 10^6

= Clock Rate / CPI * 10^6

•machines with different instruction sets ?

•programs with different instruction mixes ?

• dynamic frequency of instructions

• uncorrelated with performance

GFLOPS = FP Operations / Time * 10^9 playstation: 6.4 GFLOPS

•machine dependent

•often not where time is spent

Why Do Benchmarks? How we evaluate differences

Different systems Changes to a single system

Provide a target Benchmarks should represent large class of important

programs Improving benchmark performance should help many

programs For better or worse, benchmarks shape a field Good ones accelerate progress

good target for development Bad benchmarks hurt progress

help real programs v. sell machines/papers? Inventions that help real programs don’t help

benchmark

Basis of Evaluation

Actual Target Workload

Full Application Benchmarks

Small “Kernel” Benchmarks

Microbenchmarks

Cons

• representative• very specific• non-portable• difficult to run, or measure• hard to identify cause

• portable• widely used• improvements useful in reality

• easy to run, early in design cycle

• identify peak capability and potential bottlenecks

•less representative

• easy to “fool”

• “peak” may be a long way from application performance

Successful Benchmark: SPEC

1987 RISC industry mired in “bench marketing”:(“That is 8 MIPS machine, but they claim 10 MIPS!”)

EE Times + 5 companies band together to perform Systems Performance Evaluation Committee (SPEC) in 1988: Sun, MIPS, HP, Apollo, DEC

Create standard list of programs, inputs, reporting: some real programs, includes OS calls, some I/O

SPEC first round

First round 1989; 10 programs, single number to summarize performance

One program: 99% of time in single line of code New front-end compiler could improve dramatically

Benchmark

SPE

C P

erf

0

100

200

300

400

500

600

700

800

gcc

epre

sso

spic

e

doduc

nasa7

li

eqnto

tt

matr

ix300

fpppp

tom

catv

SPEC95

Eighteen application benchmarks (with inputs) reflecting a technical computing workload

Eight integer go, m88ksim, gcc, compress, li, ijpeg, perl, vortex

Ten floating-point intensive tomcatv, swim, su2cor, hydro2d, mgrid, applu,

turb3d, apsi, fppp, wave5 Must run with standard compiler flags

eliminate special undocumented incantations that may not even generate working code for real programs

Summary

Time is the measure of computer performance! Good products created when have:

Good benchmarks Good ways to summarize performance

If not good benchmarks and summary, then choice between improving product for real programs vs. improving product to get more sales=> sales almost always wins

Remember Amdahl’s Law: Speedup is limited by unimproved part of program

CPU time = Seconds = Instructions x Cycles x Seconds

Program Program Instruction Cycle

CPU time = Seconds = Instructions x Cycles x Seconds

Program Program Instruction Cycle

Readings & More…

Reminder:

READ:

TEXTBOOK: Chapter 1 pages 1 to 47 Moore paper (posted on course web site).