Chapter 3 instruction level parallelism and its exploitation

Outline
2.1 Instruction-Level Parallelism: Concepts and Challenges
Computer Architecture 2.2 Basic Compiler Techniques for Exposing ILP
計算機結構 2.3 Reducing Branch Costs with Prediction
2.4 Overcoming Data Hazards with Dynamic Scheduling
Lecture 3 2.5 Dynamic Scheduling: Examples and the Algorithm
Instruction-Level Parallelism 2.6
26 Hardware-Based Speculation
H d B dS l ti
2.7 Exploiting ILP Using Multiple Issue and Static Scheduling
and Its Exploitation
p 2.8
28 Exploiting ILP Using Dynamic Scheduling, Multiple Issue
Scheduling
(Chapter 2 in textbook) and Speculation

Ping-Liang Lai (賴秉樑)

Computer Architecture Ch3-1 Computer Architecture Ch3-2

2.1 ILP: Concepts and Challenges Instruction-Level Parallelism (ILP)

Instruction-Level Parallelism (ILP): overlap the execution of Basic Block (BB) ILP is quite small
instructions to improve performance. BB: a straight-line code sequence with no branches in except to the entry
2 approaches to exploit ILP and, no branches out except at the exit;
1) Rely on hardware to help discover and exploit the parallelism dynamically Average d
A dynamic branch frequency 15% to 25%;
i b hf t 25%
(e.g., Pentium 4, AMD Opteron, IBM Power), and » 3 to 6 instructions execute between a pair of branches.
2) Rely on software technology to find parallelism statically at compile-time
parallelism, Plus instructions in BB likely to depend on each other.
(e.g., Itanium 2) To obtain substantial performance enhancements, we must
Pipelining Review exploit ILP across multiple basic blocks. (ILP → LLP)
p p ( )
Pipeline CPI = Ideal pipeline CPI + Structural Stalls + Data Hazard Stalls Loop-Level Parallelism: to exploit parallelism among iterations of a loop.
+ Control Stalls E.g., add two matrixes.

for (i=1; i<=1000; i=i+1)
x[i] = x[i] + y[ ]
[] [ ] y[i];


Data Dependence and Hazards Data Dependence

Three data dependence: data dependences (true data Floating-point data part
dependences), name dependences, and control dependences.
1. Instruction i produces a result that may be used by instruction j (i → j), or Loop: L.D F0, 0(R1) ;F0=array element
2.
2 Instruction j i data dependent on instruction k and i
i is d d d i i k, d instruction k i data
i is d ADD.D
ADD D F4, F0,
F4 F0 F2 ;add scalar in
dd l i
dependent on instruction i (i → k → j, dependence chain). S.D F4, 0(R1) ;store result
For example, a MIPS code sequence
example
Integer data part
Loop: L.D F0, 0(R1) ;F0=array element
ADD.D F4, F0, F2 ;add scalar in DADDUI R1, R1, #-8 ;decrement pointer
S.D F4, 0(R1) ;store result
;8 bytes (per DW)
DADDUI R1, R1 # 8
R1 R1, #-8 ;decrement pointer 8 bytes
d t i t b t
BNE R1, R2, Loop ;branch R1!=R2 BNE R1, R2, Loop ;branch R1!=R2

† This type is called a Write After Read (WAR) hazard.

Data Dependence and Hazards ILP and Data Dependencies, Hazards

InstrJ is data dependent (aka true dependence) on InstrI: HW/SW must preserve program order: order instructions would
1) InstrJ tries to read operand before InstrI writes it; execute in if executed sequentially as determined by original
source program.
I: add r1 r2 r3
r1,r2,r3 Dependences are a property of programs.
d f
J: sub r4,r1,r3
Presence of dependence indicates potential for a hazard, but
2) Or InstrJ is data dependent on InstrK which is dependent on InstrI. actual hazard and length of any stall is property of the pipeline
pipeline.
If two instructions are data dependent, they cannot execute Importance of the data dependencies.
simultaneously or be completely overlapped.
y p y pp 1) Indicates the possibility of a hazard;
Data dependence in instruction sequence → data dependence in 2) Determines order in which results must be calculated;
source code → effect of original data dependence must be 3) Sets an upper bound on how much parallelism can possibly be exploited.
preserved. HW/SW goal: exploit parallelism by preserving program order
If data dependence caused a hazard in pipeline, called a Read only where it affects the outcome of the program.
After Write (RAW) hazard.

Name Dependence #1: Anti-dependence Name Dependence #2: Output dependence

Name dependence: when 2 instructions use same register or InstrJ writes operand before InstrI writes it.
memory location, called a name, but no flow of data between the
I: sub r1,r4,r3
instructions associated with that name; 2 versions of name
J: add r1,r2,r3
, ,3
dependence (WAR and WAW).
d d d WAW)
K: mul r6,r1,r7
InstrJ writes operand before InstrI reads it
Called an “output dependence by compiler writers This also
output dependence” writers.
I: sub r4,r1,r3 results from the reuse of name “r1”
J: add r1,r2,r3
K:
K mul r6,r1,r7
l 6 1 7 If anti dependence caused a hazard in the pipeline, called a
anti-dependence pipeline
Write After Write (WAW) hazard.
Called an “anti-dependence” by compiler writers. This results from reuse
Instructions involved in a name dependence can execute
of the name “r1”.
f h
simultaneously if name used in instructions is changed so
If anti-dependence caused a hazard in the pipeline, called a instructions do not conflict.
Write After Read (WAR) hazard.
hazard Register renaming resolves name dependence for regs;
Either by compiler or by HW.

Control Dependencies Control Dependence

Every instruction is control dependent on some set of branches, Two constrains imposed by control dependence
and, in general, these control dependencies must be preserved to 1. An instruction that is dependent on a branch cannot be moved before the
preserve program order. branch so that its execution is no longer controlled by the branch;
2.
2 An instruction that is control dependent on a branch cannot be moved
if p1 { after the branch so that its execution is controlled by the branch.
S1;
; Control dependence need not be preserved
p p
}; Willing to execute instructions that should not have been executed,
if p2 { thereby violating the control dependences, if can do so without affecting
S2;
S2 correctness of the program .
} Instead, 2 properties critical to program correctness are
1.
1 Exception behavior and,
and
S1 is control dependent on p1, and S2 is control dependent on p2 2. Data flow
but not on p1.
p


Exception Behavior Data Flow

Preserving exception behavior Data flow: actual flow of data values among instructions that
Any changes in instruction execution order must not change how produce results and those that consume them.
exceptions are raised in program (⇒ no new exceptions). Branches make flow dynamic, determine which instruction is supplier of
Example
E l data.
data
Example
DADDU R2,R3,R4
BEQZ R2,L1 DADDU R1, R2, R3
LW R1,0(R2) BEQZ R4, L
L1: DSUBU R1 R5 R6
R1, R5,
L: …
† Assume branches not delayed. OR R7, R1, R8

Problem with moving LW before BEQZ? R1 of OR depends on DADDU or DSUBU?
Must preserve data flow on execution.
execution


2.2 Basic Compiler Techniques
Outline for Exposing ILP
2.1 Instruction-Level Parallelism: Concepts and Challenges This code, add a scalar to a vector
2.2 Basic Compiler Techniques for Exposing ILP for (i=1000; i>0; i=i–1)
2.3 Reducing Branch Costs with Prediction x[i] = x[i] + s;
2.4 Overcoming Data Hazards with Dynamic Scheduling
Assume following latencies for all examples
2.5 Dynamic Scheduling: Examples and the Algorithm Ignore delayed branch in these examples
2.6
H d B dS l ti
2.7 Exploiting ILP Using Multiple Issue and Static Scheduling
Instruction producing result Instruction using result Latency in cycles
2.8
Scheduling FP ALU op Another FP ALU op 3
and Speculation
FP ALU op Store double 2
Load d bl
L d double FP ALU op 1
Load double Store double 0

Figure 2.2 Latencies of FP operations used in this chapter.


FP Loop: Where are the Hazards? FP Loop Showing Stalls

First translate into MIPS code Example 3-1 (p.76): Show how the loop would look on MIPS, both scheduled
To simplify, assume 8 is lowest address and unscheduled i l di any stalls or idle clock cycles. Schedule for delays
d h d l d including ll idl l k l h d l f d l
from floating-point operations, but remember that we are ignoring delayed
branches.
Loop:
L L.D
LD F0,0(R1)
F0 0(R1) ;F0=vector element
F0 l
Answer
ADD.D F4,F0,F2 ;add scalar from F2
1. Loop: L.D F0,0(R1) ;F0=vector element
S.D
SD 0(R1),F4
0(R1) F4 ;store result
2. stall
DADDUI R1,R1,-8 ;decrement pointer 8B (DW)
3. ADD.D F4,F0,F2 ;add scalar in F2
BNEZ R1,Loop ;branch R1!=zero
4.
4 stall
5. stall
6. S.D 0(R1),F4
( ), ;
;store result
7. DADDUI R1,R1,-8 ;decrement pointer 8B (DW)
8. stall ;assumes can’t forward to branch
9. BNEZ R1,Loop ;branch R1!=zero

Computer Architecture Ch3-17
† 9 clock cycles: Rewrite code to minimize stalls? Computer Architecture Ch3-18

Revised FP Loop Minimizing Stalls Unroll Loop Four Times
1. Loop: L.D F0,0(R1)
1.
1 Loop: L.D
LD F0,0(R1)
F0 0(R1)
2. DADDUI R1,R1,-8 3. ADD.D F4,F0,F2
3. ADD.D F4,F0,F2 6. S.D 0(R1),F4 ;drop DSUBUI & BNEZ
4.
4 stall .
7. L.D
. F6,-8(R1)
6, 8( )
9. ADD.D F8,F6,F2
5. stall 12. S.D -8(R1),F8 ;drop DSUBUI & BNEZ
6. S.D 8(R1),F4
( ), ;
;altered offset when move DSUBUI 13. L.D ( )
F10,-16(R1)
7. BNEZ R1,Loop 15. ADD.D F12,F10,F2
18. S.D -16(R1),F12 ;drop DSUBUI & BNEZ
Swap DADDUI and S.D by changing address of S.D 19. L.D F14,-24(R1)
21. ADD.D F16,F14,F2
Instruction producing result Instruction using result Latency in cycles
24. S.D -24(R1),F16
FP ALU op Another FP ALU op 3 25. DADDUI R1,R1,#-32 ;alter to 4*8
FP ALU op Store double
S d bl 2 26. BNEZ R1,LOOP
Load double FP ALU op 1
Load double Store double 0
27 clock cycles, or 6.75 per iteration (Assumes R1 is multiple of
4).
4)
† 7 clock cycles, but just 3 for execution (L.D, ADD.D,S.D), 4 for loop overhead;
How make faster? Computer Architecture Ch3-19 Computer Architecture Ch3-20

Unrolled Loop Detail Unrolled Loop That Minimizes Stalls

Do not usually know upper bound of loop.
1. Loop:
1 L L.D
LD F0,
F0 0(R1)
Suppose it is n, and we would like to unroll the loop to make k 2. L.D F6, -8(R1)
copies of the body. 3. L.D F10, -16(R1)
Instead of a single unrolled loop, we generate a pair of 4. L.D F14, -24(R1)
5. ADD.D F4 ,F0, F2
consecutive loops: 6. ADD.D F8, F6, F2
, ,
1st executes (n mod k) times and has a body that is the original loop; 7. ADD.D F12, F10, F2
2nd is the unrolled body surrounded by an outer loop that iterates (n/k) 8. ADD.D F16, F14, F2
times. 9. S.D 0(R1), F4
For large values of n, most of the execution time will be spent in 10. S.D -8(R1), F8
11. S.D -16(R1), F12
the unrolled loop.
p 12.
12 DSUBUI R1, R1
R1 R1, #32
13. S.D 8(R1), F16 ; 8-32 = -24
14. BNEZ R1, LOOP


5 Loop Unrolling Decisions Limits to Loop Unrolling

Requires understanding how one instruction depends on another 3 Limits to Loop Unrolling
and how the instructions can be changed or reordered given the 1. Decrease in amount of overhead amortized with each extra unrolling.
dependences: Amdahl’s Law.
1.
1 Determine loop unrolling useful by finding that l
i l lli f l b fi di h loop iterations were
i i 2.
2 Growth i code size.
G th in d i
independent (except for maintenance code); For larger loops, concern it increases the instruction cache miss rate.
2. Use different registers to avoid unnecessary constraints forced by using
g y y g 3. Register p
g pressure: potential shortfall in registers created by aggressive
p g y gg
same registers for different computations; unrolling and scheduling.
3. Eliminate the extra test and branch instructions and adjust the loop If not be possible to allocate all live values to registers, may lose some or all
termination and iteration code; of its advantage
advantage.
4. Determine that loads and stores in unrolled loop can be interchanged by Loop unrolling reduces impact of branches on pipeline; another
observing that loads and stores from different iterations are independent; way is branch prediction.
y p
» Transformation requires analyzing memory addresses and finding that they do We discuss it in section 2.3: Reducing Branch Costs with Prediction.
not refer to the same address.
5.
5 Schedule the code, preserving any dependences needed to yield the same
code
result as the original code.

Outline 2.3 Reducing Branch Costs with Prediction

2.1 Instruction-Level Parallelism: Concepts and Challenges Because of the need to enforce control dependences through
2.2 Basic Compiler Techniques for Exposing ILP branch hazards and stall, branches will hurt pipeline performance.
2.3 Reducing Branch Costs with Prediction Solution 1: loop unrolling;
2.4 Overcoming Data Hazards with Dynamic Scheduling Solution 2 b
S l i 2: by predicting how they will behave.
di i h h ill b h
2.5 Dynamic Scheduling: Examples and the Algorithm SW/HW technology
2.6
H d B dS l ti SW: St ti B
SW Static Branch Prediction, statically at compile time;
h P di ti t ti ll t il ti
2.7 Exploiting ILP Using Multiple Issue and Static Scheduling HW: Dynamic Branch Prediction, dynamically by the hardware at
execution time.
2.8
Scheduling
and Speculation


Static Branch Prediction Dynamic Branch Prediction
Appendix A showed scheduling code around delayed branch. Why does prediction work?
Reorder code around branches, need to predict branch statically when compile. Underlying algorithm has regularities;
Simplest scheme is to predict a branch as taken. Data that is being operated on has regularities;
Average misprediction = untaken branch frequency = 34% SPEC. Unfortunately
SPEC Unfortunately, Instruction sequence h redundancies that are artifacts of way that
has d d i h if f h
from very accurate (59%) to highly accurate (9%).
humans/compilers think about problems.
25% 22% Is dynamic branch prediction better than static branch prediction?
Misprediction Rate

18%
More accurate
20%
15%
Seems to be;
scheme predicts 15% 12% 11% 12% There are a small number of important branches in programs which have
branches using 9% 10%
10%
6%
dynamic behavior.
profile information 5%
4%
collected from earlier
runs, and modify
d dif 0%

prediction based on
last run.
Integer (ave. 15%) Floating Point (ave. 9%)
Figure 2.3 The result of predict-taken inSPEC92

Dynamic Branch Prediction Basic Branch Prediction Buffers

Performance = ƒ(accuracy, cost of misprediction) a.k.a. Branch History Table (BHT) - Small direct-mapped cache
Branch History Table (also called Branch Prediction Buffer): of T/NT bits.
lower bits of PC address index table of 1-bit values.
Says whether the branch was recently taken or not;
No address check.
Problem: i a loop, 1-bit BHT will cause two mispredictions
P bl in l 1 bit ill t i di ti
(average is 9 in 10 iterations before exit).
End of loop case, when it exits instead of looping as before;
case
First time through loop on next time through code, when it predicts exit
instead of looping.


Dynamic Branch Prediction 2-bit Scheme Accuracy

Solution: 2-bit scheme where change prediction only if get Mispredict because either:
misprediction twice. Wrong guess for that branch;
T Got branch history of wrong branch when index the table.
4,096 entry table
NT 20% 18%
Predict Taken 11 10 Predict Taken 18%

iction Rate
e
T 16%
T NT 14% 12%
Predict Not NT Predict Not 12% 10%
01 00 9% 9% 9%
Taken Taken 10%
T

Mispredi
8%
6% 5% 5%
4%
1%
NT 2% 0%
0%
Red: stop, not taken;
Blue: go, taken;
t

c

c

0
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p

7
ot

ice

ice
so

gc

du

pp

30

sa
nt

es

sp

sp
do

ri x
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at
Adds hysteresis to decision making process. es

m
Integer Floating Point
Computer Architecture Ch3-31 Figure 2.5 The result of 2-bit scheme inSPEC89 Computer Architecture Ch3-32

2-bit Scheme Accuracy Improve Prediction Strategy By Correlating Branches

In figure 2.5, the accuracy of the predictors for integer programs, Consider the worst case for the 2-bit predictor
which typically also have higher branch frequencies, is lower DSUBUI R3, R1, #2
if (aa==2) if the first BNEZ R3, L1
than for the loop-intensive scientific programs. 2 fail then DADD R1, R0, R0
aa=0;
the 3rd will
Two ways to attack this problem if (bb==2) always be
L1:
DSUBUI R3, R2, #2
Large buffer size; bb=0; taken.
BNEZ R3, L2
Increasing the accuracy of the scheme we use for each prediction.
I i th f th h f h di ti if (aa != bb) {
DADD R2, R0, R0
However, simply increasing the number of bits per predictor † Single level predictors can never get this case. L2:
without changing the predictor structure also has little impact.
impact DSUBU R3, R1, R2
BEQZ R3, L3
Single branch predictor V.S. correlating branch predictors. Correlating predictors or 2-level predictors
Correlation = what happened on the last branch
pp This branch is based on
the Outcome of the
» Note that the last correlator branch may not always be the same. previous 2 branches.
Predictor = which way to go
» 4 possibilities: which way the last one went chooses the prediction.
prediction
− (Last-taken, last-not-taken) × (predict-taken, predict-not-taken)


Correlated Branch Prediction Correlating Branches
Idea: record m most recently executed branches as taken or not taken, and use (2, 2) predictor
that pattern to select the proper n-bit branch history table.
h l h bi b h hi bl Behavior of recent 2 branches selects between four predictions of next
In general, (m, n) predictor means record last m branches to select between 2m branch, updating just that prediction.
history tables, each with n-bit counters.
y ,
Thus, old 2-bit BHT is a (0, 2 ) predictor. Branch address
Global Branch History: m-bit shift register keeping T/NT status of last m 4
branches.
b h
2-bits per branch predictor
Each entry in table has m n-bit predictors.
Total bits for the (m, n) BHT prediction buffer:

Total _ memory _ bits = 2 m × n × 2 p Prediction

2m banks of memory selected by the global branch history (which is just a shift
register) - e.g. a column address;
Use p bits of the branch address to select row;
Get the n predictor bits in the entry to make the decision.
2-bit global branch history

Example of Correlating Branch Predictors Example: Multiple Consequent Branches

if(d == 0) ;not taken
d=1;
else ;taken
if(d==1) ;not taken
else ;taken
if (d==0) BNEZ R1,
R1 L1 ;branch b1 (d!=0)
If b1 is not taken, then b2 will be not taken
d = 1; DADDIU R1, R0, #1 ;d==0, so d=1
if (d==1)
( ) L1: DADDIU R3, R1, #-1
… BNEZ R3, L2 ;branch b2 (d!=1)
…
L2:
1-bit predictor: consider d alternates between 2 and 0. All branches are mispredicted


Example: Multiple Consequent Branches Accuracy of Different Schemes

if(d == 0) ;not taken
d=1; 20%
else ;taken 18% 4,096 Entries 2-bit BHT

ctions
if(d==1) ;not taken
else ;taken 16% Unlimited Entries 2-bit BHT
1,024
1 024 Entries (2 2) BHT
(2,

ency of Mispredic
14%
2-bits prediction : prediction if last branch not taken/ and prediction if last branch taken
12%
11%
10%

M
8%
6% 6% 6%
6%
5% 5%
4%
4%
Freque 2%
(1,1) predictor - 1-bit predictor with 1 bit of correlation: last branch (either taken or 1% 1%
0% 0%
not taken) decides which prediction bit will be considered or updated

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4,096 entries: 2-bits per entry
, p y Unlimited entries: 2-bits/entry
/ y 1,024 entries (2,2)
, ( , )


Outline Advantages of Dynamic Scheduling
2.1 Instruction-Level Parallelism: Concepts and Challenges Dynamic scheduling: hardware rearranges the instruction
2.2 Basic Compiler Techniques for Exposing ILP execution to reduce stalls while maintaining data flow and
2.3 Reducing Branch Costs with Prediction exception behavior.
2.4 Overcoming Data Hazards with Dynamic Scheduling It handles cases when dependences unknown at compile time.
2.5 Dynamic Scheduling: Examples and the Algorithm It allows the processor to tolerate unpredictable delays such as cache
2.6
H d B dS l ti misses,
misses by executing other code while waiting for the miss to resolve.
resolve

2.7 Exploiting ILP Using Multiple Issue and Static Scheduling It allows code that compiled for one pipeline to run efficiently on
2.8
Scheduling a different pipeline.
pp
and Speculation It simplifies the compiler.
Hardware speculation: a technique with significant performance
advantages, builds on dynamic scheduling (next lecture).


HW Schemes: Instruction Parallelism Dynamic Scheduling Step 1

Key idea: allow instructions behind stall to proceed. Simple pipeline had 1 stage to check both structural and data
DIVD F0, F2, F4
hazards: Instruction Decode (ID), also called Instruction Issue.
ADDD F10, F0, F8 Split the ID pipe stage of simple 5-stage pipeline into 2 stages
SUBD F12, F8, F14 Issue: decode instructions, check for structural hazards.
Enables out-of-order execution and allows out-of-order Read operands: wait until no data hazards, then read operands.
completion (e.g., SUBD).
In a dynamically scheduled pipeline, all instructions still pass through
issue stage in order (in-order issue).
(in order issue)
Will distinguish when an instruction begins execution and when
it completes execution; between 2 times the instruction is in
times,
execution.
Note: Dynamic execution creates WAR and WAW hazards and
y
makes exceptions harder.

Tomasulo Algorithm Tomasulo Organization
Control & buffers distributed with Function Units (FU) FP Registers
g
From Mem FP Op
FU buffers called “reservation stations”; have pending operands Queue
Registers in instructions replaced by values or pointers to reservation Load Buffers
stations(RS); called register renaming; Load1
Renaming avoids WAR, WAW hazards Load2
Load3
More reservation stations than registers, so can do optimizations compilers can’t Load4
Load5 Store
Results to FU from RS, not through registers, over Common Data Bus that Load6
Buffers
broadcasts results to all FUs
Avoids RAW hazards by executing an instruction only when its operands are
y g y p Add1
available Add2 Mult1
Add3 Mult2
Load and Stores treated as FUs with RSs as well
Reservation To Mem
Integer instructions can go past branches (predict taken), allowing FP ops
taken) Stations
beyond basic block in FP queue. FP adders FP multipliers


Reservation Station Components Three Stages of Tomasulo Algorithm

Op: operation to perform in the unit (e.g., + or –) 1. Issue: get instruction from FP Op Queue
If reservation station free (no structural hazard), control issues instr & sends
Vj, Vk: value of Source operands operands (renames registers).
Store buffers has V field, result to be stored 2. Execute: operate on operands (EX)
p p ( )
Qj, Qk: reservation stations producing source registers (value to When both operands ready then execute; if not ready, watch Common Data Bus for
be written) result.
Note: Qj,Qk=0 => ready 3.
3 Write result: finish execution (WB)
Write on Common Data Bus to all awaiting units; mark reservation station
Store buffers only have Qi for RS producing result
available
Busy: indicates reservation station or FU is busy Normal data bus: data + destination (“go to” bus)
Register result status: Indicates which functional unit will write Common data bus: data + source (“come from” bus)
each register if one exists Blank when no pending instructions
register, exists. 64 bits of data + 4 bits of Functional Unit source address
that will write that register. Write if matches expected Functional Unit (produces result)
Does the broadcast
Example speed:
3 clocks for Fl .pt. +,-; 10 for * ; 40 clks for /

Outline Instruction stream Tomasulo Example
p
Instruction status: Exec Write
Instruction j k Issue Comp Result Busy Address
2.1 Instruction-Level Parallelism: Concepts and Challenges LD F6 34+ R2 Load1 No
2.2 Basic Compiler Techniques for Exposing ILP LD
MULTD F0
F2 45+
F2
R3
F4
Load2
Load3
No
No
2.3 Reducing Branch Costs with Prediction SUBD F8 F6 F2
V
DIVD F10 F0 F6
2.4 Overcoming Data Hazards with Dynamic Scheduling ADDD F6 F8 F2
3 Load/Buffers
L d/B ff

2.5 Dynamic Scheduling: Examples and the Algorithm Reservation Stations: S1 S2 RS RS
Time Name Busy
y Op
p Vj
j Vk Qj Q
Qk
2.6
H d B dS l ti Add1 No

2.7 Exploiting ILP Using Multiple Issue and Static Scheduling FU count Add2 No
3 FP Adder R.S.
down Add3 No
2 FP Mult R.S.
2.8
Scheduling Mult1 No
Mult2 No
and Speculation
Register result status:
Clock F0 F2 F4 F6 F8 F10 F12 ... F30
0 FU

Clock cycle
y
counter

Computer Architecture Ch3-49

Tomasulo Example Cycle 1
p y Tomasulo Example Cycle 2
p y
Instruction status: Exec Write Instruction status: Exec Write
Instruction j k Issue Comp Result Busy Address Instruction j k Issue Comp Result Busy Address
LD F6 34+ R2 1 Load1 Yes 34+R2 LD F6 34+ R2 1 Load1 Yes 34+R2
LD F2 45+ R3 Load2 No LD F2 45+ R3 2 Load2 Yes 45+R3
MULTD F0 F2 F4 Load3 No MULTD F0 F2 F4 Load3 No
SUBD F8 F6 F2 SUBD F8 F6 F2
V
DIVD F10 F0 F6 V
DIVD F10 F0 F6
ADDD F6 F8 F2 ADDD F6 F8 F2

Reservation Stations: S1 S2 RS RS Reservation Stations: S1 S2 RS RS
Time Name Busy
y Op
p Vj
j Vk Qj Q
Qk Time Name Busy
y Op
p Vj
j Vk Qj Q
Qk
Add1 No Add1 No
Add2 No Add2 No
Add3 No Add3 No
Mult1 No Mult1 No
Mult2 No Mult2 No

Register result status: Register result status:
Clock F0 F2 F4 F6 F8 F10 F12 ... F30 Clock F0 F2 F4 F6 F8 F10 F12 ... F30
1 FU Load1 2 FU Load2 Load1

p y
LD F6 34+ R2 1 3 Load1 Yes 34+R2 LD F6 34+ R2 1 3 4 Load1 No
LD F2 45+ R3 2 Load2 Yes 45+R3 LD F2 45+ R3 2 4 Load2 Yes 45+R3
MULTD F0 F2 F4 3 Load3 No MULTD F0 F2 F4 3 Load3 No
SUBD F8 F6 F2 SUBD F8 F6 F2 4
V
DIVD F10 F0 F6 V
DIVD F10 F0 F6
ADDD F6 F8 F2 ADDD F6 F8 F2

Time Name Busy Op
y p Vj
j Vk Qj Q
Qk Time Name Busy Op
y p Vj
j Vk Qj Q
Qk
Add1 No Add1 Yes SUBD M(A1) Load2
Add2 No Add2 No
Add3 No Add3 No
Mult1 Yes MULTD R(F4) Load2 Mult1 Yes MULTD R(F4) Load2
Mult2 No Mult2 No

3 FU Mult1 Load2 Load1 4 FU Mult1 Load2 M(A1) Add1

• Note: registers names are removed (“renamed”) in Reservation Stations;
g ( ) ;
MULT issued • Load2 completing; what is waiting for Load2?
• Load1 completing; what is waiting for Load1?

p y
LD F6 34+ R2 1 3 4 Load1 No LD F6 34+ R2 1 3 4 Load1 No
LD F2 45+ R3 2 4 5 Load2 No LD F2 45+ R3 2 4 5 Load2 No
MULTD F0 F2 F4 3 Load3 No MULTD F0 F2 F4 3 Load3 No
SUBD F8 F6 F2 4 SUBD F8 F6 F2 4
V
DIVD F10 F0 F6 5 V
DIVD F10 F0 F6 5
ADDD F6 F8 F2 ADDD F6 F8 F2 6

Time Name Busy Op
y p Vj
j Vk Qj Q
Qk Time Name Busy Op
y p Vj
j Vk Qj Q
Qk
2 Add1 Yes SUBD M(A1) M(A2) 1 Add1 Yes SUBD M(A1) M(A2)
Add2 No Add2 Yes ADDD M(A2) Add1
Add3 No Add3 No
10 Mult1 Yes MULTD M(A2) R(F4) 9 Mult1 Yes MULTD M(A2) R(F4)
Mult2 Yes DIVD M(A1) Mult1 Mult2 Yes DIVD M(A1) Mult1

5 FU Mult1 M(A2) M(A1) Add1 Mult2 6 FU Mult1 M(A2) Add2 Add1 Mult2

• Timer starts down for Add1 Mult1
Add1, • Issue ADDD here despite name dependency on F6?

Chapter 3 instruction level parallelism and its exploitation

Chapter 3 instruction level parallelism and its exploitation

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