Power calculation for transistor operation

Power calculation for transistor operation

• What will cause power consumption to increase?

CS2710 Computer Organization 1

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2clock

P CapacitiveLoad Voltage ClockFrequencyor

P CV f


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f(x) = 1.37064407211569E-06 x + 21.4060648531567R² = 0.992074254384158

ATMEGA 32 Current versus Crystal Frequency

CurrentLinear (Current)

Crystal Frequency (Hz)

Micr

opro

cess

or C

urre

nt (m

A)2

:

:

clock

clock

P CV fAlsoP IVThusI CVf

Measuring the current used by the Atmega microprocessor shows a linear relationship

Note: V=5v for in this case


2 2.5 3 3.5 4 4.5 5 5.50

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100

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f(x) = 3.42804303590267 x^2.58744148306454R² = 0.998420723836924

Power versus Microprocessor Voltage

PowerPower (Power)

Microprocessor Voltage

Micr

opro

cess

or P

ower

(mW

)

What effect does increasing voltage to a microprocessor have on power? On speed?

Below around 2.5v (for this microprocessor), the transistors simply stop working

The Power Wall: Why haven’t clock rates continued to increase at historical rates?


Manufacturers have turned to multi-core architectures to bypass the Power Wall


Clock speed decrease, but overall performanceincrease

Lecture Objectives:

Benchmarking

1) Explain the SPEC benchmarks.2) Define Amdahl's law3) Define MIPS

Amdahl’s Law (p51)

• The performance enhancement possible with a given improvement is limited by the amount that the improved feature is used


ImIm

AffectedBy provementproved Unaffected

ExecutionTimeExecutionTime ExecutionTime

AmountOfImprovement

Amdahl’s Law Applied• A Program spends 40 seconds

performing network transfers and 60 seconds generating reports. – Suppose we could rewrite the report

generator to make it more efficient.– What improvement in performance in the

report generator would be necessary to increase the overall speed of the program by a factor of 2?

– How about by a factor of 3?


A Performance Metric: MIPS

Units: millions of instructions per second


610InstructionCountMIPS

ExecutionTime

Issues with MIPS metrics1. Measures instruction execution rate, but doesn’t

consider the complexity of the instructions performed

2. Average instruction complexity varies between programs executing on a single computer

3. Different microprocessors implement instructions of differing complexities

• MIPS may vary independently from performance• We cannot compare computers with different

instruction sets using MIPS!CS2710 Computer Organization 10

Benchmarking: How do you decide which computer to buy?


SPEC Benchmark• A set of programs used to measure performance

– Supposedly typical of actual workload• Standard Performance Evaluation Corp (SPEC)

– Develops benchmarks for CPU, I/O, Web, …

• SPEC CPU2006– Elapsed time to execute a selection of programs

• Negligible I/O, so focuses on CPU performance– Normalize relative to reference machine– Summarize as geometric mean of performance ratios

• CINT2006 (integer) and CFP2006 (floating-point)


n

n

1iiratio time Execution

Geometric vs. Arithmetic Mean

• Arithmetic mean:

• Geometric mean:


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ii

xn

1

nn i

i

x

Which computer has better overall performance?


Computer A Computer B Computer C

Program 1 1 10 20

Program 2 1000 100 20

Which computer has better overall performance?



Program 1 1 10 20

Program 2 1000 100 20

Arithmetic mean 500.5 55 20

Geometric mean 31.622 . . . 31.622 . . . 20

A is fastest via Arithmetic mean.A and B are tied via Geometric mean.

Geometric mean is the appropriate mean when the ranges of the values being compared vary significantly.

Benchmarking often computes performance relative to a standard reference



Program 1 1 10 20

Program 2 1000 100 20

Computer A(reference) Computer B Computer C

Program 1 1 10 20

Program 2 1 0.1 0.02

Scaling the results in this manner is called normalization.Note that no normalization was needed for Program 1 sincethe reference computer’s value was already 1.

Let’s say A is the “reference” computer. We adjust all performance values by dividing each value by the reference computer’s value. In this example, we divide all results for Program 2 by the reference computer’s performance value of 1000, giving:

Arithmetic and Geometric means based on the normalized values:


Program 1 1 10 20

Program 2 1 0.1 0.02

Arithmetic mean 1 5.05 10.01

Geometric mean 1 1 0.632 . . .


Now C is fastest via Arithmetic mean!A and B are still tied via Geometric mean.

Now consider computer B to be the “reference” computer and normalize A and C w.r.t. B


Now A is fastest via Arithmetic mean!A and B are still tied via Geometric mean.

The Geometric mean is consistent regardless of normalization!

Computer A Computer B(reference) Computer C

Program 1 0.1 1 2

Program 2 10 1 0.2

Arithmetic mean 5.05 1 1.1

Geometric mean 1 1 0.632

The SPECjvm2008 application– SPECjvm2008 is a benchmark suite for

measuring the performance of a Java Runtime Environment (JRE), containing several real life applications and benchmarks focusing on core java functionality.

– The SPECjvm2008 workload mimics a variety of common general purpose application computations.


CINT2006 integer performance benchmarks for the Opteron X4 2356

• Highlight the high cache misses of a few, and how this impacts the overall performance

• Highlight how different apps might be useful for different teamsCS2710 Computer Organization 20

Name Description IC×109 CPI Tc (ns) Exec time Ref time SPECratio

perl Interpreted string processing 2,118 0.75 0.40 637 9,777 15.3

bzip2 Block-sorting compression 2,389 0.85 0.40 817 9,650 11.8

gcc GNU C Compiler 1,050 1.72 0.47 24 8,050 11.1

mcf Combinatorial optimization 336 10.00 0.40 1,345 9,120 6.8

go Go game (AI) 1,658 1.09 0.40 721 10,490 14.6

hmmer Search gene sequence 2,783 0.80 0.40 890 9,330 10.5

sjeng Chess game (AI) 2,176 0.96 0.48 37 12,100 14.5

libquantum Quantum computer simulation

1,623 1.61 0.40 1,047 20,720 19.8

h264avc Video compression 3,102 0.80 0.40 993 22,130 22.3

omnetpp Discrete event simulation 587 2.94 0.40 690 6,250 9.1

astar Games/path finding 1,082 1.79 0.40 773 7,020 9.1

xalancbmk XML parsing 1,058 2.70 0.40 1,143 6,900 6.0

Geometric mean 11.7

SPEC and power: ssj_ops(server-side java operations/sec)

• Power consumption of server at different workload levels– Performance: ssj_ops/sec– Power: Watts (Joules/sec)


10

0ii

10

0ii powerssj_ops Wattper ssj_ops Overall

A Power benchmark: SPEC Power versus loadSPECpower_ssj2008 for X4


Target Load % Performance (ssj_ops/sec) Average Power (Watts)100% 231,867 295

90% 211,282 28680% 185,803 27570% 163,427 26560% 140,160 25650% 118,324 24640% 920,35 23330% 70,500 22220% 47,126 20610% 23,066 1800% 0 141

Overall sum 1,283,590 2,605∑ssj_ops/ ∑power 493

Low power at low usage? No!

• Look back at X4 power benchmark– At 100% load: 295W– At 50% load: 246W (83%)– At 10% load: 180W (61%)

• Google data center– Mostly operates at 10% – 50% load– At 100% load less than 1% of the time

• Future research/development: Design processors to make power proportional to load


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Power calculation for transistor operation