saman amarasinghe 16.035 ©mit fall 1998 simple machine model instructions are executed in sequence...

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Saman Amarasinghe 1 6.035 ©MIT Simple Machine Model • Instructions are executed in sequence – Fetch, decode, execute, store results – One instruction at a time • For branch instructions, start fetching from a different location if needed – Check branch condition – Next instruction may come from a new location given by the branch instruction

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Saman Amarasinghe 1 6.035 ©MIT Fall 1998

Simple Machine Model

• Instructions are executed in sequence– Fetch, decode, execute, store results– One instruction at a time

• For branch instructions, start fetching from a different location if needed– Check branch condition– Next instruction may come from a new location

given by the branch instruction

Saman Amarasinghe 2 6.035 ©MIT Fall 1998

Simple Execution Model

• 5 Stage pipe-line

• Fetch: get the next instruction• Decode: figure-out what that instruction is• Execute: Perform ALU operation

– address calculation in a memory op

• Memory: Do the memory access in a mem. Op.• Write Back: write the results back

fetch decode execute memory writeback

Saman Amarasinghe 3 6.035 ©MIT Fall 1998

Simple Execution Model

IF DE EXE MEM WB

IF DE EXE MEM WB

Inst 1

Inst 2

time

Saman Amarasinghe 4 6.035 ©MIT Fall 1998

Simple Execution Model

IF DE EXE MEM WB

IF DE EXE MEM WB

Inst 1

Inst 2

time

IF DE EXE MEM WB

IF DE EXE MEM WB

IF DE EXE MEM WB

IF DE EXE MEM WB

IF DE EXE MEM WB

Inst 1

Inst 2

Inst 3

Inst 4

Inst 5

Saman Amarasinghe 5 6.035 ©MIT Fall 1998

From a Simple Machine Model to a Real Machine Model

• Many pipeline stages– MIPS R4000 has 8 stages

• Different instructions taking different amount of time to execute– mult 10 cycles– div 69 cycles– ddiv 133 cycles

• Hardware to stall the pipeline if an instruction uses a result that is not ready

Saman Amarasinghe 6 6.035 ©MIT Fall 1998

Real Machine Model cont.

• Most modern processors have multiple execution units (superscalar)– If the instruction sequence is correct, multiple

operations will happen in the same cycles– Even more important to have the right instruction

sequence

Saman Amarasinghe 7 6.035 ©MIT Fall 1998

Instruction Scheduling

• Reorder instructions so that pipeline stalls are minimized

Saman Amarasinghe 8 6.035 ©MIT Fall 1998

Constraints On Scheduling

• Data dependencies

• Control dependencies

• Resource Constraints

Saman Amarasinghe 9 6.035 ©MIT Fall 1998

Data Dependency between Instructions

• If two instructions access the same variable, they can be dependent

• Kind of dependencies – True: write read– Anti: read write– Output: write write

• What to do if two instructions are dependent.– The order of execution cannot be reversed – Reduce the possibilities for scheduling

Saman Amarasinghe 10 6.035 ©MIT Fall 1998

Computing Dependencies

• For basic blocks, compute dependencies by walking through the instructions

• Identifying register dependencies is simple– is it the same register?

• For memory accesses– simple: base + offset1 != base + offset2– data dependence analysis: a[2i] != a[2i+1]– interprocedural analysis: global != parameter– pointer alias analysis: p1 != p

Saman Amarasinghe 11 6.035 ©MIT Fall 1998

Representing Dependencies

• Using a dependence DAG, one per basic block

• Nodes are instructions, edges represent dependencies

Saman Amarasinghe 12 6.035 ©MIT Fall 1998

Representing Dependencies

• Using a dependence DAG, one per basic block

• Nodes are instructions, edges represent dependencies

1: r2 = *(r1 + 4)

2: r3 = *(r1 + 8)

3: r4 = r2 + r3

4: r5 = r2 - 1

Saman Amarasinghe 13 6.035 ©MIT Fall 1998

• Using a dependence DAG, one per basic block

• Nodes are instructions, edges represent dependencies

1: r2 = *(r1 + 4)

2: r3 = *(r1 + 8)

3: r4 = r2 + r3

4: r5 = r2 - 1 3

1

Representing Dependencies

2

4

Saman Amarasinghe 14 6.035 ©MIT Fall 1998

• Using a dependence DAG, one per basic block

• Nodes are instructions, edges represent dependencies

1: r2 = *(r1 + 4)

2: r3 = *(r1 + 8)

3: r4 = r2 + r3

4: r5 = r2 - 1

• Edge is labeled with Latency:– v(i j) = delay required between initiation times of i

and j minus the execution time required by i

3

1

Representing Dependencies

2

4

2 22

Saman Amarasinghe 15 6.035 ©MIT Fall 1998

Example

1: r2 = *(r1 + 4)

2: r3 = *(r2 + 4)

3: r4 = r2 + r3

4: r5 = r2 - 1

3

1 2

4

2 22

3

Saman Amarasinghe 16 6.035 ©MIT Fall 1998

Another Example

1: r2 = *(r1 + 4)

2: *(r1 + 4) = r3

3: r3 = r2 + r3

4: r5 = r2 - 1

3

1 2

4

Saman Amarasinghe 17 6.035 ©MIT Fall 1998

Another Example

1: r2 = *(r1 + 4)

2: *(r1 + 4) = r3

3: r3 = r2 + r3

4: r5 = r2 - 1

3

1 2

4

2 21

1

Saman Amarasinghe 18 6.035 ©MIT Fall 1998

Control Dependencies and Resource Constraints

• For now, lets only worry about basic blocks

• For now, lets look at simple pipelines

Saman Amarasinghe 19 6.035 ©MIT Fall 1998

Example

1: LA r1,array 2: LD r2,4(r1) 3: AND r3,r3,0x00FF 4: MULC r6,r6,100 5: ST r7,4(r6)6: DIVC r5,r5,1007: ADD r4,r2,r58: MUL r5,r2,r49: ST r4,0(r1)

Saman Amarasinghe 20 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

Saman Amarasinghe 21 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1

Saman Amarasinghe 22 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1

Saman Amarasinghe 23 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2

Saman Amarasinghe 24 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3

Saman Amarasinghe 25 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3 4

Saman Amarasinghe 26 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3 4

Saman Amarasinghe 27 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3 4 st st 5

Saman Amarasinghe 28 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3 4 st st 5 6

Saman Amarasinghe 29 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3 4 st st 5 6

Saman Amarasinghe 30 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3 4 st st 5 6 st st st 7

Saman Amarasinghe 31 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3 4 st st 5 6 st st st 7

Saman Amarasinghe 32 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3 4 st st 5 6 st st st 7 8

Saman Amarasinghe 33 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3 4 st st 5 6 st st st 7 8 9

Saman Amarasinghe 34 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3 4 st st 5 6 st st st 7 8 9

14 cycles!

Saman Amarasinghe 35 6.035 ©MIT Fall 1998

List Scheduling Algorithm

• Idea– Do a topological sort of the dependence DAG– Consider when an instruction can be scheduled

without causing a stall– Schedule the instruction if it causes no stall and all

its predecessors are already scheduled

• Optimal list scheduling is NP-complete– Use heuristics when necessary

Saman Amarasinghe 36 6.035 ©MIT Fall 1998

List Scheduling Algorithm

• Create a dependence DAG of a basic block

• Topological SortREADY = nodes with no predecessors

Loop until READY is empty

Schedule each node in READY when no stalling

Update READY

Saman Amarasinghe 37 6.035 ©MIT Fall 1998

Heuristics for selection

• Heuristics for selecting from the READY list– pick the node with the longest path to a leaf in the

dependence graph– pick a node with most immediate successors – pick a node that can go to a less busy pipeline (in a

superscalar)

Saman Amarasinghe 38 6.035 ©MIT Fall 1998

Heuristics for selection

• pick the node with the longest path to a leaf in the dependence graph

• Algorithm (for node x)– If no successors dx = 0

– dx = MAX( dy + cxy) for all successors y of x

– reverse breadth-first visitation order

Saman Amarasinghe 39 6.035 ©MIT Fall 1998

Heuristics for selection

• pick a node with most immediate successors

• Algorithm (for node x):– fx = number of successors of x

Saman Amarasinghe 40 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

Saman Amarasinghe 41 6.035 ©MIT Fall 1998

Example1

6

8

2

7

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1

1

3

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1

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5

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31: LA r1,array2: LD r2,4(r1)3: AND r3,r3,0x00FF4: MULC r6,r6,1005: ST r7,4(r6)6: DIVC r5,r5,1007: ADD r4,r2,r58: MUL r5,r2,r49: ST r4,0(r1)

Saman Amarasinghe 42 6.035 ©MIT Fall 1998

Example1

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Saman Amarasinghe 43 6.035 ©MIT Fall 1998

Example1

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1

1

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1

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3

3

d=0 d=0

d=0

d=0

Saman Amarasinghe 44 6.035 ©MIT Fall 1998

Example1

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2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

Saman Amarasinghe 45 6.035 ©MIT Fall 1998

Example1

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2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

Saman Amarasinghe 46 6.035 ©MIT Fall 1998

Example1

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2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7

Saman Amarasinghe 47 6.035 ©MIT Fall 1998

Example1

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1

1

3

4

1

4

5

3

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d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

Saman Amarasinghe 48 6.035 ©MIT Fall 1998

Example1

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1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5

Saman Amarasinghe 49 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

Saman Amarasinghe 50 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { }

Saman Amarasinghe 51 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { }1, 3, 4, 6

Saman Amarasinghe 52 6.035 ©MIT Fall 1998

Example1

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2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 6, 1, 4, 3 }1, 3, 4, 6

Saman Amarasinghe 53 6.035 ©MIT Fall 1998

Example1

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2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 6, 1, 4, 3 }

Saman Amarasinghe 54 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 6, 1, 4, 3 }

6

Saman Amarasinghe 55 6.035 ©MIT Fall 1998

Example1

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2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 1, 4, 3 }

6

Saman Amarasinghe 56 6.035 ©MIT Fall 1998

Example1

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2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 1, 4, 3 }

6

Saman Amarasinghe 57 6.035 ©MIT Fall 1998

Example1

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2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 1, 4, 3 }

6 1

Saman Amarasinghe 58 6.035 ©MIT Fall 1998

Example1

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8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 4, 3 }

6 1

2

Saman Amarasinghe 59 6.035 ©MIT Fall 1998

Example1

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2

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9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 2, 4, 3 }

6 1

Saman Amarasinghe 60 6.035 ©MIT Fall 1998

Example1

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2

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1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 2, 4, 3 }

6 1

Saman Amarasinghe 61 6.035 ©MIT Fall 1998

Example1

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2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 2, 4, 3 }

6 1

Saman Amarasinghe 62 6.035 ©MIT Fall 1998

Example1

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8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 2, 4, 3 }

6 1 2

Saman Amarasinghe 63 6.035 ©MIT Fall 1998

Example1

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8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 4, 3 }

6 1 2

7

Saman Amarasinghe 64 6.035 ©MIT Fall 1998

Example1

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2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 7, 4, 3 }

6 1 2

Saman Amarasinghe 65 6.035 ©MIT Fall 1998

Example1

6

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2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 7, 4, 3 }

6 1 2

Saman Amarasinghe 66 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 7, 4, 3 }

6 1 2

Saman Amarasinghe 67 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 7, 4, 3 }

6 1 2

Saman Amarasinghe 68 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 7, 4, 3 }

6 1 2 4

Saman Amarasinghe 69 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 7, 3 }

6 1 2 4

5

Saman Amarasinghe 70 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 7, 3, 5 }

6 1 2 4

Saman Amarasinghe 71 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 7, 3, 5 }

6 1 2 4

Saman Amarasinghe 72 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 7, 3, 5 }

6 1 2 4

Saman Amarasinghe 73 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 7, 3, 5 }

6 1 2 4 7

Saman Amarasinghe 74 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 3, 5 }

6 1 2 4 7

8, 9

Saman Amarasinghe 75 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 3, 5, 8, 9 }

6 1 2 4 7

Saman Amarasinghe 76 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 3, 5, 8, 9 }

6 1 2 4 7

Saman Amarasinghe 77 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 3, 5, 8, 9 }

6 1 2 4 7 3

Saman Amarasinghe 78 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 5, 8, 9 }

6 1 2 4 7 3

Saman Amarasinghe 79 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 5, 8, 9 }

6 1 2 4 7 3

Saman Amarasinghe 80 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 5, 8, 9 }

6 1 2 4 7 3

Saman Amarasinghe 81 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 5, 8, 9 }

6 1 2 4 7 3 5

Saman Amarasinghe 82 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 8, 9 }

6 1 2 4 7 3 5

Saman Amarasinghe 83 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 8, 9 }

6 1 2 4 7 3 5

Saman Amarasinghe 84 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 8, 9 }

6 1 2 4 7 3 5

Saman Amarasinghe 85 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 8, 9 }

6 1 2 4 7 3 5 8

Saman Amarasinghe 86 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 9 }

6 1 2 4 7 3 5 8

Saman Amarasinghe 87 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 9 }

6 1 2 4 7 3 5 8

Saman Amarasinghe 88 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 9 }

6 1 2 4 7 3 5 8

Saman Amarasinghe 89 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { 9 }

6 1 2 4 7 3 5 8 9

Saman Amarasinghe 90 6.035 ©MIT Fall 1998

Example1

6

8

2

7

9

1

1

3

4

1

4

5

3

3

d=0 d=0

d=0

d=0 d=3

d=3

d=7d=4

d=5f=1f=0

f=0f=1f=1

f=1

f=2

f=0 f=0

READY = { }

6 1 2 4 7 3 5 8 9

Saman Amarasinghe 91 6.035 ©MIT Fall 1998

Example

6 1 2 4 7 3 5 8 9

Results In1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

9 cycles

Saman Amarasinghe 92 6.035 ©MIT Fall 1998

ExampleResults In

1: LA r1,array 1 cycle2: LD r2,4(r1) 1 cycle3: AND r3,r3,0x00FF 1 cycle4: MULC r6,r6,100 3 cycles5: ST r7,4(r6)6: DIVC r5,r5,100 4 cycles7: ADD r4,r2,r5 1 cycle8: MUL r5,r2,r4 3 cycles9: ST r4,0(r1)

1 2 3 4 st st 5 6 st st st 7 8 914 cycles

Vs9 cycles

6 1 2 4 7 3 5 8 9