1. Samira Khan University of Virginia Jan 16, 2019
ADVANCED
COMPUTER ARCHITECTURE
Fundamental Concepts:
Computing Models
Samira Khan
University of Virginia
Jan 16, 2019
The content and concept of this course are adapted from CMU ECE 740

2. AGENDA Review from last lecture Why study computer architecture?
Fundamental concepts
Computing models

3. LAST LECTURE RECAP
What it means/takes to be a good (computer) architect
Roles of a computer architect (look everywhere!)
Levels of transformation
Abstraction layers, their benefits, and the benefits of comfortably crossing them
An example problem and solution ideas
Designing a system with processing-in-memory technologies
Course Logistics
Assignments: HW (today), Review Set 1 (Next Wednesday)

4. REVIEW: KEY TAKEAWAY
Breaking the abstraction layers (between components and transformation hierarchy levels) and knowing what is underneath enables you to solve problems and design better future systems
Cooperation between multiple components and layers can enable more effective solutions and systems

5. HOW TO DO THE PAPER REVIEWS
1: Brief summary
What is the problem the paper is trying to solve?
What are the key ideas of the paper? Key insights?
What is the key contribution to literature at the time it was written?
What are the most important things you take out from it?
2: Strengths (most important ones)
Does the paper solve the problem well?
3: Weaknesses (most important ones)
This is where you should think critically. Every paper/idea has a weakness. This does not mean the paper is necessarily bad. It means there is room for improvement and future research can accomplish this.
4: Can you do (much) better? Present your thoughts/ideas.
5: What have you learned/enjoyed/disliked in the paper? Why?
Review should be short and concise (~half a page to a page)

6. AGENDA Review from last lecture Why study computer architecture?
Fundamental concepts
Computing models

7. AN ENABLER: MOORE’S LAW
Moore, “Cramming more components onto integrated circuits,”
Electronics Magazine, Component counts double every other year
Image source: Intel

8. Number of transistors on an integrated circuit doubles ~ every two years
Image source: Wikipedia

9. RECOMMENDED READING
Moore, “Cramming more components onto integrated circuits,” Electronics Magazine, 1965.
Only 3 pages
A quote:
“With unit cost falling as the number of components per circuit rises, by 1975 economics may dictate squeezing as many as components on a single silicon chip.”
Another quote:
“Will it be possible to remove the heat generated by tens of thousands of components in a single silicon chip?”

10. WHAT DO WE USE THESE TRANSISTORS FOR?

11. WHY STUDY COMPUTER ARCHITECTURE?
Enable better systems: make computers faster, cheaper, smaller, more reliable, …
By exploiting advances and changes in underlying technology/circuits
Enable new applications
Life-like 3D visualization 20 years ago?
Virtual reality?
Personalized genomics? Personalized medicine?
Enable better solutions to problems
Software innovation is built into trends and changes in computer architecture
> 50% performance improvement per year has enabled this innovation
Understand why computers work the way they do

12. COMPUTER ARCHITECTURE TODAY (I)
Today is a very exciting time to study computer architecture
Industry is in a large paradigm shift (to multi-core and beyond: accelerators, FPGAs, processing-in-memory) – many different potential system designs possible
Many difficult problems motivating and caused by the shift
Power/energy constraints  multi-core?
Complexity of design  multi-core?
Difficulties in technology scaling  new technologies?
Memory wall/gap
Reliability wall/issues
Programmability wall/problem
Huge hunger for data and new data-intensive applications
No clear, definitive answers to these problems

13. COMPUTER ARCHITECTURE TODAY (II)
These problems affect all parts of the computing stack – if we do not change the way we design systems
No clear, definitive answers to these problems
Problem
Many new demands
from the top
(Look Up)
Algorithm
Program/Language
User
Fast changing
demands and
personalities
of users
(Look Up)
Runtime System
(VM, OS, MM)
ISA
Microarchitecture
Many new issues
at the bottom
(Look Down)
Logic
Circuits
Electrons

14. COMPUTER ARCHITECTURE TODAY (III)
Computing landscape is very different from years ago
Both UP (software and humanity trends) and DOWN (technologies and their issues), FORWARD and BACKWARD, and the resulting requirements and constraints
Hybrid Main Memory
Persistent Memory/Storage
Microsoft Catapult (FPGA)
General Purpose GPUs
Heterogeneous
Processors
Every component and its interfaces, as well as entire system designs are being re-examined

15. COMPUTER ARCHITECTURE TODAY (IV)
You can revolutionize the way computers are built, if you understand both the hardware and the software (and change each accordingly)
You can invent new paradigms for computation, communication, and storage
Recommended book: Thomas Kuhn, “The Structure of Scientific Revolutions” (1962)
Pre-paradigm science: no clear consensus in the field
Normal science: dominant theory used to explain/improve things (business as usual); exceptions considered anomalies
Revolutionary science: underlying assumptions re-examined

16. Thomas S Kuhn PhD in Physics from Harvard in 1949
During his PhD switched from physics to the History and Philosophy of Science
Joined University of California Berkeley as a professor of the History of Science in 1961
Wrote the book “Structure of the Scientific Revolutions” in 1962

17. COMPUTER ARCHITECTURE TODAY (IV)
You can revolutionize the way computers are built, if you understand both the hardware and the software (and change each accordingly)
You can invent new paradigms for computation, communication, and storage
Recommended book: Thomas Kuhn, “The Structure of Scientific Revolutions” (1962)
Pre-paradigm science: no clear consensus in the field
Normal science: dominant theory used to explain/improve things (business as usual); exceptions considered anomalies
Revolutionary science: underlying assumptions re-examined

18. So What is the Structure of Scientific Revolutions?
Step 2:
Normal Science
Step 4:
Crisis and Emergence
of Scientific Theory
Step 5:
Scientific
Revolution
Step 3:
Anomaly
Step 1:
Pre-paradigm
History of Science

19. COMPUTER ARCHITECTURE TODAY (IV)
Thomas Kuhn, “The Structure of Scientific Revolutions” (1962)

20. … BUT, FIRST … Let’s understand the fundamentals…
You can change the world only if you understand it well enough…
Especially the past and present dominant paradigms
And, their advantages and shortcomings – tradeoffs
And, what remains fundamental across generations
And, what techniques you can use and develop to solve problems

21. AGENDA Review from last lecture Why study computer architecture?
Fundamental concepts
Computing models

22. WHAT IS A COMPUTER? Three key components Computation Communication
Storage (memory)

23. WHAT IS A COMPUTER? Processing Memory I/O (program and data) control
(sequencing)
datapath

24. THE VON NEUMANN MODEL/ARCHITECTURE
Also called stored program computer (instructions in memory). Two key properties:
Stored program
Instructions stored in a linear memory array
Memory is unified between instructions and data
The interpretation of a stored value depends on the control signals
Sequential instruction processing
One instruction processed (fetched, executed, and completed) at a time
Program counter (instruction pointer) identifies the current instr.
Program counter is advanced sequentially except for control transfer instructions
When is a value interpreted as an instruction?

25. THE VON NEUMANN MODEL/ARCHITECTURE
Recommended reading
Burks, Goldstein, Von Neumann, “Preliminary discussion of the logical design of an electronic computing instrument,” 1946.
Stored program
Sequential instruction processing

26. THE VON NEUMANN MODEL (OF A COMPUTER)
MEMORY
Mem Addr Reg
Mem Data Reg
PROCESSING UNIT
INPUT
OUTPUT
ALU
TEMP
CONTROL UNIT IP
Inst Register

27. THE VON NEUMANN MODEL (OF A COMPUTER)
Q: Is this the only way that a computer can operate?
A: No.
Qualified Answer: But, it has been the dominant way
i.e., the dominant paradigm for computing
for N decades

28. THE DATA FLOW MODEL (OF A COMPUTER)
Von Neumann model: An instruction is fetched and executed in control flow order
As specified by the instruction pointer
Sequential unless explicit control flow instruction
Dataflow model: An instruction is fetched and executed in data flow order
i.e., when its operands are ready
i.e., there is no instruction pointer
Instruction ordering specified by data flow dependence
Each instruction specifies “who” should receive the result
An instruction can “fire” whenever all operands are received
Potentially many instructions can execute at the same time
Inherently more parallel

29. VON NEUMANN VS DATAFLOW
Consider a Von Neumann program
What is the significance of the program order?
What is the significance of the storage locations?
Which model is more natural to you as a programmer? a b
v <= a + b;
w <= b * 2;
x <= v - w
y <= v + w
z <= x * y + *2 - +
Sequential *
Dataflow z

30. MORE ON DATA FLOW
In a data flow machine, a program consists of data flow nodes
A data flow node fires (fetched and executed) when all it inputs are ready
i.e. when all inputs have tokens
Data flow node and its ISA representation

31. Samira Khan University of Virginia Jan 16, 2019
ADVANCED
COMPUTER ARCHITECTURE
Fundamental Concepts:
Computing Models
Samira Khan
University of Virginia
Jan 16, 2019
The content and concept of this course are adapted from CMU ECE 740