1. COSC 3406: Computer Organization
Lecture 3: Computer Abstraction
Kalpdrum Passi
Fall 2016
( )

2. Morgan Kaufmann Publishers Computer Abstractions and Technology
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
Chapter 1
Computer Abstractions and Technology

3. Instruction Count and CPI
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
Instruction Count and CPI
Instruction Count for a program
Determined by program, ISA and compiler
Average cycles per instruction
Determined by CPU hardware
If different instructions have different CPI
Average CPI affected by instruction mix

4. Morgan Kaufmann Publishers
September 6, 2018
Chapter 1 — Computer Abstractions and Technology
CPI Example
Computer A: Cycle Time = 250ps, CPI = 2.0
Computer B: Cycle Time = 500ps, CPI = 1.2
Same ISA
Which is faster, and by how much?
A is faster…
…by this much

5. Morgan Kaufmann Publishers
September 6, 2018
Chapter 1 — Computer Abstractions and Technology
CPI in More Detail
If different instruction classes take different numbers of cycles
Weighted average CPI
Relative frequency

6. Morgan Kaufmann Publishers
Chapter 1 — Computer Abstractions and Technology
Morgan Kaufmann Publishers
September 6, 2018
CPI Example
Alternative compiled code sequences using instructions in classes A, B, C
Class A B C
CPI for class 1 2 3
IC in sequence 1
IC in sequence 2 4
Sequence 1: IC = 5
Clock Cycles = 2×1 + 1×2 + 2×3 = 10
Avg. CPI = 10/5 = 2.0
Sequence 2: IC = 6
Clock Cycles = 4×1 + 1×2 + 1×3 = 9
Avg. CPI = 9/6 = 1.5

7. Morgan Kaufmann Publishers
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Performance Summary
The BIG Picture
Performance depends on
Algorithm: affects IC, possibly CPI
Programming language: affects IC, CPI
Compiler: affects IC, CPI
Instruction set architecture: affects IC, CPI, Tc

8. Morgan Kaufmann Publishers
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Power Trends
§1.7 The Power Wall
Power provides a limit to what we can cool,
In the post-PC era the really valuable resource is energy.
Dominant technology for IC is CMOS (complementary metal oxide semiconductor)

9. Power Trends
The primary source of energy consumption is so-called dynamic energy—that is, energy that is consumed when transistors switch states from 0 to 1 and vice versa.
The dynamic energy depends on the capacitive loading of each transistor and the voltage applied:
This equation is the energy of a pulse during the logic transition of 0 → 1 → 0 or 1 → 0 → 1.
The power required per transistor is just the product of energy of a transition and the frequency of transitions:
×30
5V → 1V
×1000

10. Morgan Kaufmann Publishers
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
Reducing Power
Suppose a new CPU has
85% of capacitive load of old CPU
15% voltage and 15% frequency reduction
The power wall
We can’t reduce voltage further
We can’t remove more heat
How else can we improve performance?

11. Uniprocessor Performance
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
Uniprocessor Performance
§1.8 The Sea Change: The Switch to Multiprocessors
Constrained by power, instruction-level parallelism, memory latency

12. Morgan Kaufmann Publishers
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
Multiprocessors
Multicore microprocessors
More than one processor per chip
Requires explicitly parallel programming
Compare with instruction level parallelism
Hardware executes multiple instructions at once
Hidden from the programmer
Hard to do
Programming for performance
Load balancing
Optimizing communication and synchronization

13. Morgan Kaufmann Publishers
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
SPEC CPU Benchmark
Programs used to measure performance
Supposedly typical of actual workload
System Performance Evaluation Cooperative (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
Dividing the execution time of a reference processor by the execution time of the evaluated computer normalizes the execution time measurements;
This normalization yields a measure, called the SPECratio
SPECratio is the inverse of execution time.

14. Morgan Kaufmann Publishers
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
CINT2006 for Intel Core i7 920
A CINT2006 (integer) or CFP2006 (floating-point) Summary measurement is obtained by taking the geometric mean of SPECratios

15. Morgan Kaufmann Publishers
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
SPEC Power Benchmark
Power consumption of servers at different workload levels, divided into 10% increments, over a period of time.
SPECpower started with another SPEC benchmark for Java business applications (SPECJBB2005),
It exercises the processors, caches, and main memory as well as the Java virtual machine, compiler, garbage collector, and pieces of the operating system.
Performance is measured in throughput, and the units are business operations per second.
where ssj_opsi is performance at each 10% increment and poweri is power consumed at each performance level
Power: Watts (Joules/sec)

16. SPECpower_ssj2008 for Xeon X5650
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
SPECpower_ssj2008 for Xeon X5650

17. Morgan Kaufmann Publishers
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
Pitfall: Amdahl’s Law
Improving an aspect of a computer and expecting a proportional improvement in overall performance
§1.10 Fallacies and Pitfalls
Example: multiply accounts for 80s/100s
How much improvement in multiply performance to get 5× overall?
Can’t be done!
Corollary: make the common case fast

18. Fallacy: Low Power at Idle
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
Fallacy: Low Power at Idle
Look back at i7 power benchmark
At 100% load: 258W
At 50% load: 170W (66%)
At 10% load: 121W (47%)
Google data center
Mostly operates at 10% – 50% load
At 100% load less than 1% of the time
Consider designing processors to make power proportional to load
If future servers used, say, 10% of peak power at 10% workload, we could reduce the electricity bill of datacenters

19. Fallacy: Designing for performance and designing for energy efficiency unrelated
Energy is power over time
Hardware or software optimizations that take less time save energy overall even if the optimization takes a bit more energy when it is used.

20. Pitfall: MIPS as a Performance Metric
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
Pitfall: MIPS as a Performance Metric
MIPS: Millions of Instructions Per Second
Doesn’t account for
Differences in ISAs between computers
Differences in complexity between instructions
CPI varies between programs on a given CPU and so does MIPS

21. Morgan Kaufmann Publishers
Chapter 1 — Computer Abstractions and Technology
September 6, 2018
Morgan Kaufmann Publishers
Concluding Remarks
Cost/performance is improving
Due to underlying technology development
Hierarchical layers of abstraction
In both hardware and software
Instruction set architecture
The hardware/software interface
Execution time: the best performance measure
Individually the factors do not determine performance: only the product, is a reliable measure of performance.
§1.9 Concluding Remarks

22. Concluding Remarks Two of the key ideas are Power is a limiting factor
exploiting parallelism in the program, via multiple processors, and
exploiting locality of accesses to a memory hierarchy, typically via caches.
Power is a limiting factor
Use parallelism to improve performance
Computer designs measured by cost and performance, as well as
energy, dependability, cost of ownership, and scalability.

23. The University of Adelaide, School of Computer Science
Chapter 2 — Instructions: Language of the Computer
6 September 2018
The University of Adelaide, School of Computer Science
Chapter 2
Instructions: Language of the Computer

24. The University of Adelaide, School of Computer Science
Chapter 2 — Instructions: Language of the Computer
6 September 2018
Instruction Set
§2.1 Introduction
The repertoire of instructions of a computer
Different computers have different instruction sets
But with many aspects in common
Early computers had very simple instruction sets
Simplified implementation
Many modern computers also have simple instruction sets

25. The ARMv8 Instruction Set
Chapter 2 — Instructions: Language of the Computer
6 September 2018
The University of Adelaide, School of Computer Science
The ARMv8 Instruction Set
A subset, called LEGv8, used as the example throughout the book
Commercialized by ARM Holdings (
Large share of embedded core market
Applications in consumer electronics, network/storage equipment, cameras, printers, …
Typical of many modern ISAs
See ARM Reference Data tear-out card

26. Arithmetic Operations
Chapter 2 — Instructions: Language of the Computer
6 September 2018
The University of Adelaide, School of Computer Science
Arithmetic Operations
Add and subtract, three operands
Two sources and one destination
ADD a, b, c // a gets b + c
All arithmetic operations have this form
Each LEGv8 arithmetic instruction performs only one operation and must always have exactly three variables
Design Principle 1: Simplicity favours regularity
Regularity makes implementation simpler
Simplicity enables higher performance at lower cost
§2.2 Operations of the Computer Hardware

27. The University of Adelaide, School of Computer Science
Chapter 2 — Instructions: Language of the Computer
6 September 2018
The University of Adelaide, School of Computer Science
Arithmetic Example
Compiling Two C Assignment Statements into LEGv8:
a = b +c ;
d = a – e ;
Compiled LEGv8 code:
ADD a, b, c
SUB d, a, e
Compiling a Complex C Assignment into LEGv8:
f = (g + h) - (i + j);
ADD t0, g, h // temp t0 = g + h ADD t1, i, j // temp t1 = i + j SUB f, t0, t1 // f = t0 - t1