Samira Khan University of Virginia Jan 23, 2019

1. 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 inthamming_distance (unsigned a, unsigned b) { intdist=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 returndist; }

14. Hamming Distance •  Number of positions at which the corresponding symbols are different. • The Hamming distance between: • "karolin" and "kathrin" is 3 • 1011101 and 1001001 is 2 • 2173896 and 2233796 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 • http://www.cs.virginia.edu/~robins/YouAndYourResearch.html

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,“Executinga 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. b a + *7 1 2 x y ip = 3 p = L token - + 4 3 * 5 < ip , p , v > port data instruction ptr Dataflow Graphs {x = a + b; y = b * 7 in (x-y) * (x+y)} • 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 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. b a 1 + *7 1 2 2 x 3 y 4 - + 4 3 5 * 5 Static Dataflow Machine:Instruction Templates Destination 2 Destination 1 Operand 1 Operand 2 Opcode + 3L 4L *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 • Many such processors can be connected together • Programs can be statically divided among the processors Instruction Templates Op dest1 dest2 p1 src1 p2 src2 1 2 . . . FU FU FU FU FU Send <s1, p1, v1>, <s2, p2, v2>

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 <fp, ip, port, data> a token instruction pointer frame pointer

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

28. op r d1,d2 Code Monsoon Processor (ISCA 1990) InstructionFetch ip OperandFetch fp+r Token Queue Frames ALU FormToken Network Network

29. Concept of Tagging • Each invocation receives a separate tag

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

31. 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