1. Samira Khan University of Virginia Jan 23, 2019
ADVANCED
COMPUTER ARCHITECTURE
Fundamental Concepts:
Computing Models
Samira Khan
University of Virginia
Jan 23, 2019
The content and concept of this course are adapted from CMU ECE 740

2. AGENDA Review from last lecture Fundamental concepts
Computing models
Data flow architecture

3. 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?

4. 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

5. 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

6. 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

7. DATA FLOW NODES

8. An Example

9. What does this model perform?
val = a ^ b

10. What does this model perform?
val = a ^ b
val =! 0

11. What does this model perform?
val = a ^ b
val =! 0
val &= val - 1

12. What does this model perform?
val = a ^ b
val =! 0
val &= val - 1;
dist = 0
dist++;

13. Hamming Distance
int hamming_distance (unsigned a, unsigned b) { int dist = 0; unsigned val = a ^ b; // Count the number of bits set while (val != 0) { // A bit is set, so increment the count and clear the bit dist++; val &= val - 1; } // Return the number of differing bits return dist;

14. Hamming Distance
 Number of positions at which the corresponding symbols are different.
The Hamming distance between:
"karolin" and "kathrin" is 3
 and   is 2
 and   is 3

15. RICHARD HAMMING Best known for Hamming Code Won Turing Award in 1968
Was part of the Manhattan Project
Worked in Bell Labs for 30 years
You and Your Research is mainly his advice to other researchers
Had given the talk many times during his life time

16. HOW TO BUILD
A DATAFLOW MACHINE?

17. Monsoon Dataflow Processor 1990

18. Review Set 2 Due Jan 30 Choose 2 from a set of four
Dennis and Misunas, “A Preliminary Architecture for a Basic Data Flow Processor,” ISCA 1974.
Arvind and Nikhil,“Executing a Program on the MIT Tagged-Token Dataflow Architecture”, IEEE TC 1990.
H. T. Kung, “Why Systolic Architectures?,” IEEE Computer 1982.
Annaratone et al., “Warp Architecture and Implementation,” ISCA 1986.

19. Dataflow Graphs a b x y < ip , p , v >
{x = a + b;
y = b * 7 in
(x-y) * (x+y)} a b + *7 - * y x 1 2 3 4 5
Values in dataflow graphs are represented as tokens
An operator executes when all its input tokens are present; copies of the result token are distributed to the destination operators
ip = 3
p = L
token
< ip , p , v >
instruction ptr
port
data
no separate control flow

20. Control Flow vs. Data Flow

21. Static Dataflow
Allows only one instance of a node to be enabled for firing
A dataflow node is fired only when all of the tokens are available on its input arcs and no tokens exist on any of its its output arcs
Dennis and Misunas, “A Preliminary Architecture for a Basic Data Flow Processor,” ISCA 1974.

22. Static Dataflow Machine: Instruction Templatesb + *7 - * y x 1 2 3 4 5
Destination 1
Destination 2
Operand 1
Operand 2
Opcode
+ 3L 4L 1 2 3 4 5
* 3R 4R
- 5L
+ 5R
* out
Presence bits
Each arc in the graph has an operand slot in the program

23. Static Dataflow Machine (Dennis+, ISCA 1974)
Receive
Instruction Templates 1 2 .
Op dest1 dest2 p1 src1 p2 src2 FU FU FU FU FU
Send
<s1, p1, v1>, <s2, p2, v2>
Many such processors can be connected together
Programs can be statically divided among the processors

24. Static Data Flow Machines
Mismatch between the model and the implementation
The model requires unbounded FIFO token queues per arc but the architecture provides storage for one token per arc
The architecture does not ensure FIFO order in the reuse of an operand slot
The static model does not support
Reentrant code
Function calls
Loops
Data Structures

25. Problems with Re-entrancy
Assume this was in a loop
Or in a function
And operations took variable time to execute
How do you ensure the tokens that match are of the same invocation?

26. Dynamic Dataflow Architectures
Allocate instruction templates, i.e., a frame, dynamically to support each loop iteration and procedure call
termination detection needed to deallocate frames
The code can be shared if we separate the code and the operand storage
a token
<fp, ip, port, data>
frame
pointer
instruction pointer

27. A Frame in Dynamic Dataflow1 2 3 4 5 + 1 2 4 5
3L, 4L
Program a b + *7 - * y x 1 2 3 4 5 *
3R, 4R
Need to provide storage for only one operand/operator - 3 5L + 5R *
out
<fp, ip, p , v> 1 2 4 5 3 L 7
Frame

28. Monsoon Processor (ISCA 1990)op r
d1,d2
Code
Instruction Fetch ip
Operand Fetch
fp+r
Token
Queue
Frames
ALU
Form Token
Network
Network

29. Concept of Tagging
Each invocation receives a separate tag

30. Procedure Linkage Operatorsf a1 an
...
get frame
extract tag
change Tag 0
change Tag 1
change Tag n
Like standard call/return but caller & callee can be active simultaneously 1: n:
Fork
Graph for f
token in frame 0
token in frame 1
change Tag 0
change Tag 1

31. Samira Khan University of Virginia Jan 23, 2019
ADVANCED
COMPUTER ARCHITECTURE
Fundamental Concepts:
Computing Models
Samira Khan
University of Virginia
Jan 23, 2019
The content and concept of this course are adapted from CMU ECE 740