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Lecture 2: Measuring Performance/Cost/Power

• Today’s topics: (Sections 1.6, 1.4, 1.7, 1.8)

Quantitative principles of computer design Measuring cost Real industrial examples

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Summarizing Performance

Recall discussion on AM versus GM• GM: does not require a reference machine, but does not predict performance very well

So we multiplied execution times and determined that sys-A is 1.2x faster…but on what workload?

• AM: does predict performance for a specific workload, but that workload was determined by executing programs on a reference machine

Every year or so, the reference machine will have to be updated

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Example

• We fixed a reference machine X and ran 4 programs A, B, C, D on it such that each program ran for 1 second

• The exact same workload (the four programs execute the same number of instructions that they did on machine X) is run on a new machine Y and the execution times for each program are 0.8, 1.1, 0.5, 2

• With AM of normalized execution times, we can conclude that Y is 1.1 times slower than X – perhaps, not for all workloads, but definitely for one specific workload (where all programs run on the ref-machine for an equal #cycles)

• With GM, you may find inconsistencies

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GM Example

Computer-A Computer-B Computer-CP1 1 sec 10 secs 20 secsP2 1000 secs 100 secs 20 secs

Conclusion with GMs: (i) A=B (ii) C is ~1.6 times faster

• For (i) to be true, P1 must occur 100 times for every occurrence of P2

• With the above assumption, (ii) is no longer true

Hence, GM can lead to inconsistencies

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CPU Performance Equation

• CPU time = clock cycle time x cycles per instruction x number of instructions

• Influencing factors for each: clock cycle time: technology and organization CPI: organization and instruction set design instruction count: instruction set design and compiler

• CPI (cycles per instruction) or IPC (instructions per cycle) can not be accurately estimated analytically

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Measuring System CPI

• Assume that an architectural innovation only affects CPI

• For 3 programs, base CPIs: 1.2, 1.8, 2.5 CPIs for proposed model: 1.4, 1.9, 2.3

• What is the best way to summarize performance with a single number? AM, HM, or GM of CPIs?

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Example

• AM of CPI for base case = 1.2 cyc + 1.8 cyc + 2.5 cyc instr instr instr 5.5 cycles is execution time if each program ran for one instruction – therefore, AM of CPI defines a workload where every program runs for an equal #instrs

• HM of CPI = 1 / AM of IPC ; defines a workload where every program runs for an equal number of cycles

• GM of CPI: warm fuzzy number, not necessarily representing any workload

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Amdahl’s Law

• Architecture design is very bottleneck-driven – make the common case fast, do not waste resources on a component that has little impact on overall performance/power

• Amdahl’s Law: performance improvements through an enhancement is limited by the fraction of time the enhancement comes into play

• Example: a web server spends 40% of time in the CPU and 60% of time doing I/O – a new processor that is ten times faster results in a 36% reduction in execution time (speedup of 1.56) – Amdahl’s Law states that maximum execution time reduction is 40% (max speedup of 1.66)

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Principle of Locality

• Most programs are predictable in terms of instructions executed and data accessed

• The 90-10 Rule: a program spends 90% of its execution time in only 10% of the code

• Temporal locality: a program will shortly re-visit X

• Spatial locality: a program will shortly visit X+1

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Exploit Parallelism

• Most operations do not depend on each other – hence, execute them in parallel

• At the circuit level, simultaneously access multiple ways of a set-associative cache

• At the organization level, execute multiple instructions at the same time

• At the system level, execute a different program while one is waiting on I/O

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Factors Determining Cost

• Cost: amount spent by manufacturer to produce a finished good

• High volume faster learning curve, increased manufacturing efficiency (10% lower cost if volume doubles), lower R&D cost per produced item

• Commodities: identical products sold by many vendors in large volumes (keyboards, DRAMs) – low cost because of high volume and competition among suppliers

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Wafers and Dies

An entire wafer is produced and chopped into dies that undergo testing and packaging

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Integrated Circuit Cost

• Cost of an integrated circuit = (cost of die + cost of packaging and testing) / final test yield

• Cost of die = cost of wafer / (dies per wafer x die yield)

• Dies/wafer = wafer area / die area - wafer diam / die diag

• Die yield = wafer yield x (1 + (defect rate x die area) / ) -

• Thus, die yield depends on die area and complexity arising from multiple manufacturing steps ( ~ 4.0)

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Integrated Circuit Cost Examples

• A 30 cm diameter wafer cost $5-6K in 2001

• Such a wafer yields about 366 good 1 cm2 dies and 1014 good 0.49 cm2 dies (note the effect of area and yield)

• Die sizes: Alpha 21264 1.15 cm2 , Itanium 3.0 cm2 , embedded processors are between 0.1 – 0.25 cm2

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Contribution of IC Costs to Total System Cost

Subsystem Fraction of total cost

Cabinet: sheet metal, plastic, power supply, fans, cables, nuts, bolts, manuals, shipping box

6%

Processor 22%

DRAM (128 MB) 5%

Video card 5%

Motherboard 5%

Processor board subtotal 37%

Keyboard and mouse 3%

Monitor 19%

Hard disk (20 GB) 9%

DVD drive 6%

I/O devices subtotal 37%

Software (OS + Office) 20%

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Cost and Price

• A $1000 increase in cost may result in a $3000 increase in price – hence, important to understand the relationship

• The relationship is complex – for example, a company may underprice a product that has heavy competition and overprice a product that has no competition

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Computing Price

• Component costs: developing wafers, testing, packaging

• Direct costs: 10-30% of component costs: labor, warranty

• Gross margin (indirect costs): 10-45% of sum of these three (average selling price): R&D, marketing, sales, building rental, profits

• Retail mark-up: sum of all the above gives list price

• Low-end PCs may have low gross margins – low R&D, low cost for sales, low profits

• R&D costs are only 4-12%

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Desktop Prices

All systems have similar configurations – price variations due to expandability,expensive disks/memory/processor/OS, commoditization

Vendor Model Processor Clock speed MHz

Price

Compaq Presario 7000 AMD Athlon 1,400 $2,091

Dell Precision 420 Intel Pentium III 1,000 $3,834

Dell Precision 530 Intel Pentium 4 1,700 $4,175

HP Workstation c3600 PA 8600 552 $12,631

IBM RS6000 44P/170 IBM III-2 450 $13,889

Sun Sunblade 100 UltraSPARC II-e 500 $2,950

Sun Sunblade 1000 UltraSPARC III 750 $9,950

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Desktop Price-Performance (SPEC CPU2k)

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Server Prices

System CPUs Price

IBM xSeries 370 c/s 280 Pentium III $15.5 M

Compaq AlphaServer GS 320 32 Alpha 21264 $10.3 M

Fujitsu PRIMEPOWER 20000 48 SPARC64 GP $9.7 M

IBM pSeries 680 7017-S85 24 IBM RS64-IV $7.5 M

HP 9000 Enterprise Server 48 HP PA-RISC 8600 $8.5 M

IBM iSeries 400 840-2420 24 iSeries400 Model 840 $8.4 M

Dell PowerEdge 6400 3 Pentium III $131 K

IBM xSeries 250 c/s 4 Pentium III $297 K

Compaq Proliant ML570 4 Pentium III $375 K

HP NetServer LH 6000 6 Pentium III $373 K

NEC Express 5800/180 8 Pentium III $683 K

HP 9000/L2000 4 PA-RISC 8500 $368 K

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Server Performance

Using the OLTP benchmark TPC-C

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Server Price-Performance

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Embedded Prices

• Does not include the prices and power of support chips• High variance in functionality, price, performance, power• The IBM and AMD processors are used in network switches and laptops, the NEC VR 5432 is used in laser printers, the NEC VR 4122 is used in PDAs

Processor Issue rate Typical power (mW) Price

AMD Elan SC520 1 1600 $38

AMD K6-2E+ 3+ 9600 $78

IBM PowerPC 750CX 4 6000 $94

NEC VR 5432 2 2088 $25

NEC VR 4122 1 700 $33

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Embedded Price-Performance-Power

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Title

• Bullet