1. Fundamentals of Computer Design
Introduction
Classes of Computers
Defining Computer Architecture
Trends in Technology
Trends in Power and Energy in Integrated Circuits
Tends in Cost
Dependability
Measuring and Reporting Performance
Quantitative Principles of Computer Design
CDA – Fall Copyright © Prabhat Mishra

2. Microprocessor Performance Trends
Move to multi-processor
RISC

3. Design Complexity
Exponential Growth – doubling of transistors every couple of years

4. Technology and Demand #of transistors are doubling every 2 years
Communication, multimedia, entertainment, networking
Exponential growth of design complexity verification complexity

5. Fundamentals of Computer Design
Introduction
Classes of Computers
Defining Computer Architecture
Trends in Technology
Trends in Power and Energy in Integrated Circuits
Tends in Cost
Dependability
Measuring and Reporting Performance
Quantitative Principles of Computer Design
Conclusion

6. Computer Market Desktop Server Embedded Systems
Driven by price-performance
$ $10,000 [$100 - $1000 per processor]
Server
Throughput, availability, scalability
$10K - $10M [$200 - $2000 per processor]
Embedded Systems
Application specific
Low cost, low power, real-time performance
$10 - $100,000 [$ $200 per processor]

7. An Example Embedded System
Digital Camera Block Diagram

8. Components of Embedded Systems
Controllers
Memory
Interface
Software
(Application Programs)
Processor
Coprocessors
ASIC
Converters
Analog
Digital
Analog

9. Fundamentals of Computer Design
Introduction
Classes of Computers
Defining Computer Architecture
Trends in Technology
Trends in Power and Energy in Integrated Circuits
Tends in Cost
Dependability
Measuring and Reporting Performance
Quantitative Principles of Computer Design
Conclusion

10. Computer Architecture
Definition
Instruction set architecture (ISA)
Programmer (user) View
Implementation
Organization: CPU, memory, buses, I/O
Hardware: logic design, packaging technology
Computer design must meet
Functional requirements
Area, performance, cost, power goals
Optimize, evaluate, and explore to find best possible architecture
Consider other factors
Time-to-market, technology trend, safety, reliability, …

11. Instruction-Set Architecture (ISA)
An instruction set architecture is a specification of a standardized programmer-visible interface to hardware, comprised of:
A set of instructions (instruction types and operations)
With associated argument fields, assembly syntax, binary encoding.
A set of named storage locations and addressing
Registers, memory, … programmer-accessible caches?
A set of addressing modes (ways to name locations)
Types and sizes of operands
Control flow instructions
Often an I/O interface (usually memory-mapped)

12. Example: MIPS r0 Programmable storage r1 Data types ? 232 x bytes °
Programmable storage
232 x bytes
31 x 32-bit GPRs (R0=0)
32 x 32-bit FP regs (paired DP)
HI, LO, PC
Data types ?
Format ?
Addressing Modes? PC lo hi
Arithmetic logical
ADD, ADDU, SUB, SUBU, AND, OR, XOR, NOR, SLT, SLTU,
ADDI, ADDIU, SLTI, SLTIU, ANDI, ORL, XORL, LUI
SLL, SRL, SRA, SLLV, SRLV, SRAV
Memory Access
LB, LBU, LH, LHU, LW, LWL, LWR
SB, SH, SW, SWL, SWR
Control
J, JAL, JR, JALR
BEQ, BNE, BLEZ, BGTZ, BLTZ, BGEZ, BLTZAL, BGEZAL

13. MIPS64 Instruction Format

14. Morgan Kaufmann Publishers
19 May, 2018
MIPS Implementation
Chapter 4 — The Processor

15. Pipelined Implementation
Morgan Kaufmann Publishers
19 May, 2018
Pipelined Implementation
Chapter 4 — The Processor

16. Fundamentals of Computer Design
Introduction
Classes of Computers
Defining Computer Architecture
Trends in Technology
Trends in Power and Energy in Integrated Circuits
Tends in Cost
Dependability
Measuring and Reporting Performance
Quantitative Principles of Computer Design
Conclusion

17. Technology Trend Component Scaling of performance, wires and power
IC technology: transistor/chip increases 55% per year
DRAM: density increases 40-60% per year
Magnetic disk: density increases 100% per year
Network: Ethernet from 10  100Mb took 10 years;
100Mb  1Gb in 5 years
Scaling of performance, wires and power
Feature size: 10 micron in 1971; 0.18 in 2001, …
Microprocessor organization improvement
Wiring delay
Power issue: ~100 watts for 2GHz Pentium 4

18. Bandwidth vs. Latency
Latency improvement is 6-80X while bandwidth improvement is X.

19. Fundamentals of Computer Design
Introduction
Classes of Computers
Defining Computer Architecture
Trends in Technology
Trends in Power and Energy
Tends in Cost
Dependability
Measuring and Reporting Performance
Quantitative Principles of Computer Design
Conclusion

21. The University of Adelaide, School of Computer Science
19 May 2018
Power
Intel consumed ~ 2 W
3.3 GHz Intel Core i7 consumes 130 W
Heat must be dissipated from 1.5 x 1.5 cm chip
This is the limit of what can be cooled by air
Chapter 2 — Instructions: Language of the Computer

22. Power and Energy P E t
In many cases, faster execution means less energy, but the opposite may be true if power has to be increased to allow faster execution.

23. Power and Energy Power is drawn from a voltage source Power: Energy:
Average Power:

24. Dynamic Power
Power needed to charge and discharge load capacitances when transistors switch.
The capacitor needs to charge for output to be ‘1’
For output to be ‘0’, capacitor needs to discharge
This repeats T.fsw times over an interval of T
Here,  is activity factor
and f is clock frequency. 

25. Static Power Because leakage current flows even when a
transistor is off, now static power important too
Leakage current increases in processors with smaller transistor sizes
Increasing the number of transistors increases power even if they are turned off
In 2006, goal for leakage is 25% of total power consumption; high performance designs at 40%
Very low power systems even gate voltage to inactive modules to control loss due to leakage

26. Reducing Energy Consumption
Pentium
Crusoe
Running the same multimedia application. [
Infrared Cameras (FLIR) can be used to detect thermal distribution.

27. Dynamic Power Management (DPM)
RUN: operational
IDLE: a SW routine may stop the CPU when
not in use, while monitoring interrupts
SLEEP: Shutdown of on-chip activity
400mW
RUN
10µs
90µs
160ms
STRONGARM
SA1100
10µs
IDLE
90µs
SLEEP
50mW
160µW

28. Dynamic Voltage Scaling (DVS)
E = P x T
P  V2
E (energy), P (power), T (time), V (voltage)
Example
A task is given with workload (W) and deadline (D). Assume that idle energy is negligible.
E1  V12.T1 = V2.T
E2  V22.T2 = V2/4.2T
= E1/2 V
V/2 T D T 2T D

29. Multicores – Low Power? Multicore New challenges
One core with frequency 2 GHz
Two cores with 1 GHz frequency (each)
Same performance
Two 1 GHz cores require half power/energy
Power  freq2
1GHz core needs one-fourth power compared to 2GHz core.
New challenges
Performance concerns – how to keep them busy?
Reliability concerns – MTTF goes worse!
and more …

30. Fundamentals of Computer Design
Introduction
Classes of Computers
Defining Computer Architecture
Trends in Technology
Trends in Power and Energy in Integrated Circuits
Tends in Cost
Dependability
Measuring and Reporting Performance
Quantitative Principles of Computer Design
Conclusion

31. DRAM Pricing
© 2003 Elsevier Science (USA). All rights reserved.

32. Processor Pricing (Intel Pentium III)
© 2003 Elsevier Science (USA). All rights reserved.

33. Silicon Wafer This 300 mm wafer contains 280 Intel Core i7 dies,
each 20.7 by 10.5 mm in a 32 nm process.

34. Intel Core i7 Die
The dimensions are 18.9 mm by 13.6 mm (257 mm2) in a 45 nm process. (Courtesy Intel.)

35. Floorplan of Intel Core i7

36. Integrated Circuit Cost
The University of Adelaide, School of Computer Science
19 May 2018
Integrated Circuit Cost
Integrated circuit
Bose-Einstein formula:
Defects per unit area = defects per cm2 (2010)
N = process-complexity factor = (40 nm, 2010)
Chapter 2 — Instructions: Language of the Computer

37. Fundamentals of Computer Design
Introduction
Classes of Computers
Defining Computer Architecture
Trends in Technology
Trends in Power and Energy in Integrated Circuits
Tends in Cost
Dependability
Measuring and Reporting Performance
Quantitative Principles of Computer Design
Conclusion

38. Define and Quantify Dependability
How to decide when a system is operating properly?
Infrastructure providers now offer Service Level Agreements (SLA) to guarantee that their networking or power service would be dependable
Systems alternate between 2 states of service with respect to an SLA:
State 1: Service accomplishment, where the service is delivered as specified in SLA
State 2: Service interruption, where the delivered service is different from the SLA
Failure = transition from state 1 to state 2
Restoration = transition from state 2 to state 1

39. Dependability
Module reliability = measure of continuous service accomplishment (or time to failure)
Two metrics:
Mean Time To Failure (MTTF) – measures Reliability
Failures In Time (FIT) = 1/MTTF, the rate of failures
Traditionally reported as failures per billion hours of operation
Mean Time To Repair (MTTR) measures Service Interruption
Mean Time Between Failures (MTBF) = MTTF+MTTR
Module availability measures service as alternate between the 2 states of accomplishment and interruption (number between 0 and 1, e.g. 0.9)
Module availability = MTTF / ( MTTF + MTTR)

40. Example
If modules have exponentially distributed lifetimes (age of module does not affect probability of failure), overall failure rate is the sum of failure rates of the modules
Calculate FIT and MTTF for 10 disks (1M hour MTTF per disk), 1 disk controller (0.5M hour MTTF), and 1 power supply (0.2M hour MTTF): ( )

41. Fundamentals of Computer Design
Introduction
Classes of Computers
Defining Computer Architecture
Trends in Technology
Trends in Power and Energy in Integrated Circuits
Tends in Cost
Dependability
Measuring and Reporting Performance
Quantitative Principles of Computer Design
Conclusion

42. Performance Measurement
Performance metrics  execution time
Increasing performance decreases execution time
Other metrics
Wall-clock time, response time, elapsed time
CPU time: user or system
We will focus on CPU performance, i.e., user CPU time on unloaded system

43. Choosing Programs to Evaluate Performance
Real applications
For example: gcc compiler, Microsoft Word
Modified (or scripted) applications
For example: remove I/O, script to simulate interactive behavior.
Kernels
For example: Livermore loops, Linpack
Toy benchmarks
For example: sieve of eratosthenes, quicksort
Synthetic benchmarks
For example: wheatstone, dhrystone
Lower Accuracy

44. Benchmark Suites Desktop Server Embedded Processor New SPEC CPU2006
SPEC CPU2000: 11 integer, 14 floating-point
SPECviewperf, SPECapc: graphics benchmarks
Server
SPEC CPU2000: running multiple copies
SPECSFS: for NFS performance
SPECWeb: Web server benchmark
TPC-x: measure transaction-processing, queries, and decision making database applications
Embedded Processor
EEMBC: EDN Embedded Microprocessor Benchmark Consortium

45. SPEC CPU Benchmarks

46. Reporting Performance
Performance should be reproducible
Description of the machine and compiler flags
Report for both baseline and optimized version
Source code modifications
Not allowed in SPEC benchmarks
Allowed but difficult or impossible
TPC-C using Oracle or SQL database
Allowed in supercomputer benchmarks
Modify or re-write algorithms
Hand-coding in assembly for EEMBC benchmark

47. Comparing Performance
Arithmetic Mean:
What is the mixture of programs in the workload?
Arithmetic Mean:

48. Comparing Performance
Weighted Arithmetic Mean:
What if programs are fixed and inputs are not?

49. Comparing Performance
Geometric Mean:
Execution time ratio is normalized to a base machine.
Reference machine is not important.
The arithmetic means are different depending on which machine is used as basis, but geometric means are same.
Geometric mean does not predict execution time

50. Normalized Execution Times (SPECRatio)
Geometric mean does not predict execution time
Performance of machines A and B are same only if program P1 is executed 100 times for every occurrence of program P2
Rewards easy enhancements
Improving program P3 (2 to 1) is same as improving program P4 (1000 to 500).

51. Fundamentals of Computer Design
Introduction
Classes of Computers
Defining Computer Architecture
Trends in Technology
Trends in Power and Energy in Integrated Circuits
Tends in Cost
Dependability
Measuring and Reporting Performance
Quantitative Principles of Computer Design
Conclusion

52. Amdahl’s Law Make the common case fast
Performance improvement to be gained from using some faster mode of execution is limited by the fraction of the time the faster mode can be used.
Where:
f is a fraction of the execution time that can be enhanced
n is the enhancement factor
Example: f = 0.1, n = 10  Speedup = 1.1

53. Application of Amdahl’s Law
Amdahl’s law is useful for comparing overall performance of two design alternatives.
Example:
Floating-point (FP) operations consume 50% of the execution time of a graphics application. FP square root (FPSQRT) is used 20% of the time.
Improve FPSQRT operation execution by 10 times
Speedup = 1 / ((1-0.2) + 0.2/10) = 1.22
Improve all FP operations by 1.6 times
Speedup = 1 / ((1-0.5) + 0.5/1.6) = 1.23
Due to higher frequency of FP operations, the performance gain is more (case 2) compared to drastic improvement of FPSQRT (case 1).

54. Measuring the Performance
Performance Equation
CPU time = Instruction Count x Clock cycle time x CPI
How to compute these parameters
Known for existing processors
Clock cycle time
Use of counters in new processors
CPI, Instruction count
Simulation for performance analysis
Profile based
Trace-driven
Execution-driven

55. CPU Performance Equation
The parameters are dependent
Instruction Count: ISA and compiler technology
CPI: Organization and ISA
Cycle Time: Hardware technology and organization
Many performance enhancing techniques improves one with small/predictable impacts on the other two.

56. Example Parameters: Compare 2 designs:
Frequency of FP operations (incl. FPSQR) = 25%
CPI for FP operations = 4; CPI for others = 1.33
Frequency of FPSQR = 2%; CPI of FPSQR = 20
Compare 2 designs:
Decrease CPI of FPSQR to 2
CPI of all FP to 2.5

57. Fundamentals of Computer Design
Introduction
Classes of Computers
Defining Computer Architecture
Trends in Technology
Trends in Power and Energy in Integrated Circuits
Tends in Cost
Dependability
Measuring and Reporting Performance
Quantitative Principles of Computer Design
Conclusion

58. Fallacies and Pitfalls
The relative performance of two processors with the same ISA can be judged by clock rate or by the performance of a single benchmark suite.
1.7 GHz Pentium 4 relative to 1.0 GHz Pentium III
© 2003 Elsevier Science (USA). All rights reserved.

59. Fallacies and Pitfalls
Benchmarks remain valid indefinitely.
One line in matrix300(SPEC89) executes 99% of the time
Peak performance tracks observed performance.
The best design is the one that optimizes the primary objective without considering design costs.
Synthetic benchmarks predict performance for real programs.
Compiler/hardware optimizations can inflate performance
MIPS is an accurate measure for comparing performance among computers
Consider using FP hardware instead of FP routines.