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Dependence-Based Value Prediction
Yiannakis SazeidesUniversity of Cyprus
yanos@ucy.ac.cy
UPC-Barcelona 17/5/2001
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Motivation
• Improve Performance (reduce complexity, save power...)
• What limits performance? Dependences
• Break dependences: Prediction• Exploits regularity and/or non-
uniformity in predicted information
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Predicted Information
• Architectural– dependences: control, address, value, memory
• Non-Architectural– structural constrains: cache misses,
bank conflicts– reduce hardware complexity: cache
sets, shared patterns
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Use of Predicted Information• Speculative/Non-Speculative
– Depends on whether predicted information can directly/indirectly cause an incorrect update of architected state• non-speculative: branch prediction for
instruction prefetching• speculative: branch prediction for
instruction execution
• Hardware/Software
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Trends• Identify and eliminate predictability at
computational levels below programming• Predictors in almost all high performance
processors and many compilers• Technological evolution and limitations
will increase latency and make predictive techniques more important– deeper pipelines– fast processor/slow memory– distributed microarchitectures
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Our motivation
• Can value prediction help?• How to use value predictability?• Wholistic approach: all types not
just vp and coarser grain• Absolutely curious to discover,
understand and use the esoteric program properties causing the predictability and non-uniformity observed in program information
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
This talk...
• Hypothesis: dependence information influences the predictability of program values
• Propose and evaluate various dependence-based value predictors
• Approach: theoretical and practical• Work in progress...
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Outline
• Introduction• Background on value prediction• Dependence Info and Predictability• Dependence-based Value
Predictors• Results• Future work
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Value Sequences Produced by Instructions• Basic Sequences
– Constant: 0 0 0 0 0– Stride: 4 3 2 1 0– Non-Stride: -1 23 10 94
• Repeating Sequences (composition of basic sequences):
– Repeated Stride: 4 3 2 1 0 4 3 2 1 0– Repeated Non-Stride: -1 23 10 94 -1 23
10 94
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Local History Value Predictors• Computational-Based
– compute next value by performing a computation on previous value(s)
• Context-Based– learn the value that follows a finite
number of previous values (context) and predict that value when context repeats
– need to observe a context-value before predicting correctly
• Hybrid: Delta-Predictor
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Last Value Predictor
Las t Value
V a lu e P re d ic t io n T a b le (V P T )
P C P re d ic t io n
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Stride Predictor
Las t Value
V a lu e P re d ic t io n T a b le (V P T )
P C
P re d ic t io n
S tr ide
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Context-Based Predictor
Value His tory Las t value
C o n te x t T a b le (C T ) V a lu e P re d ic t io n T a b le (V P T )
P CP re d ic t io n
V P TI n d e x
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Hybrid Predictor
D eltas His tory s tr ide
C o n te x t T a b le (C T ) S trid e T a b le (S T )
P CP re d ic t io n
S TI n d e x
Las t value
P re d ic t io n
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Causes of Predictability
• Model Based on Dependence Graph• Robustness: predictability due to
program control structure and immediate values not input data
• Predictability generation-propagation termination– need to consider info beyond local scope– dependence information holds potential
to increase accuracy of value prediction
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Hypothesis/Fact
• The predictability of an instruction is determined by the information on the dependence path that leads to it
• Predict the value produced by an instruction based on the values and/or information of its predecessors
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Outline
• Introduction• Background on value prediction• Dependence Info and Predictability• Dependence-based Value
Predictors• Results• Future work
Example
A
C
B(s everal bbs )
Contro l F low
D ata F low
O pco de O pe ra n ds G R O u tpu t V a lu e s
A1 s r l $2 ,$6 ,5 6 (0) 3 2 (1) 3 2
2 s ll $2 ,$2,2 6 (0) 3 2 (4) 3 2
3 addu $2 ,$2 ,19 6 ,19 (0x1002f8b0) 3 2 (0x1002f8b4) 3 2
4 lw $2 ,($2) 6 ,19 (0x8000bfff) 3 2 (0xffffffff ) 3 2
5 andi $3 ,$6 ,31 6 (0 ,1 , . . ,31) 2
6 s r lv $2 ,$2,$3 6 ,19 v 0 ,v 1 , . . . ,v 6 2 ,v 3 1
7 andi $2 ,$2,1 6 ,19 (1) 1 401(0) 1 5 (1) 3 3
8 beq $2 ,0 , C 6 ,19
C9 addiu $6 ,$6 ,1 6 1 ,2 , . . , 64
10 s lti $2 ,$6,64 6 (0) 6 31
11 bne $2 ,0 , A 6
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Observations
• All instructions can trace dependence back to $6
• $6 used as an induction variable• Use information about $6 to get
predictions
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Example
• Use value in $6 to predict instruction 7:
Value of $6 Output of Instr. 7 0,..,13, 15, 31..63 1
14, 16..30 0
• Local history with <47 previous values mispredicts
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Dependence-Based Prediction• Prediction based on information from
the component of the predicted instruction i
Output = PF(Componenti)
• Component:– backward dynamic data dependence slice– node info: pcs, optypes, immed., outcomes– livein values (values not produced by
component, register and memory)
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Dependence-Based Prediction• In general, use information from
components of previous instructions
Output = PF(Ci-n..Ci-1,Ci)
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Perspective
• Virtually all predictors are functions of component approximations to values
• “Practical Limitation”: predictors can rely on a subset of components and information
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Component Approximation
• Approximation accuracy depends on information used from component
• What limits approximation accuracy:– finite resources and aliasing– amount of information to be stored– how soon a prediction can be made
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Existing Predictors
• “Interesting” with how little information predictors work well
• Global History Branch Predictors– use information from multiple previous
branch components Output=PF(Ci-n..Ci-1,Ci)
– Note components may be unrelated (longer learning and destructive aliasing)
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Dependence Information
• A lot of information to choose from– Which predecessors:
• recent, recurrent, earliest, all
– Information• values• register, memory names• pc, optypes, immediates• dependence distance
– Propagation through registers or memory
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Ideas… predict based on:
– Values of recurrent predecessors• values indicating location in program
Based on Recurrent Predecessors$6 $19
1
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5
6
7
8
9
9
10
9
11
9
1
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5
6
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9
9
99
9
9
9
8
9
9
10
9
11
9
99
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Ideas… predict based on:
– Values of recurrent predecessors• values indicating location in program• no sp• sp with last SP value and PC selection of
stride
+ 24
+ 32
-32
+ 128
S P
S P
S P
S P
S P
Created with the Trial Edition of SmartDraw 5.
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Dependence-Based Stack Pointer Predictor
Las t Value
S trid e T a b le (V P T )
P C
P re d ic t io n
S tr ideLas t Value
V a lu e P re d ic t io n T a b le (V P T )
P C
P re d ic t io n
S tr ide
+ 24
+ 32
-32
+ 128
S P
S P
S P
S P
S P
Created with the Trial Edition of SmartDraw 5.
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Ideas… predict based on:
– Values of recurrent predecessors• values indicating location in program• no sp• sp with last SP value and PC selection of
stride
– Distance from predecessors • distance indicates coordinates in
program • predict values not based on values• no sp
Based on Dependence Distance$6 $19
1
2
3
4
5
6
7
11
2
3
42
1
5
6
7
8
9
2
10
3
11
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1
1
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5
6
7
2
3
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53
6
7
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8
9
3
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4
11
5
22
1
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Ideas… predict based on:
– Values of recurrent predecessors• values indicating location in program• no sp• sp with last SP value and PC selection of
stride
– Distance from predecessors • distance indicates coordinates in program • predict values not based on values• no sp
– Most recently known processor state• isolate non-determinism
$6 $19
1
2
3
4
5
6
7
66
6
6
6,196
19
6,19
6,19
6,19
8
9
6
10
6
11
6
6
1
2
3
4
5
6
7
6
6
6
6,196
6,19
6,19
6,19
8
9
6
10
6
11
6
66
1 9
Based on Recent Predecessors
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Outline
• Introduction• Background on value prediction• Dependence Info and Predictability• Dependence-based Value
Predictors• Results• Future work
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Prediction Process
• To predict an instruction– Construction of Dependence Record
(DR)• approximation of the information on the
component of the instruction
– Use DR to obtain a prediction (directly or indirectly)
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DBVP Predictor with CT
D e p e n d e n c e R e c o rd
L a s t V a lu e
C o n te x t T a b le (C T )
P re d ic t io n T a b le (P T )
P re d ic t io nP TI n d e x
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DBVP Register (DBVP-R)
• Component subset: instructions those fetched but have not updated yet the architectural state– isolates the non-determinism in execution
• Information used: – Generate Registers(GR):livein registers
(most recently known architected state) – Dependence Path Id(DPI):pcs, optypes,
immediates (info about unknown state)
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Construction of DR for an instruction (GR)
1 s rl $ 2 ,$ 6 ,5
2 s ll $ 2 ,$ 2 ,2
3 a d d u $ 2 ,$ 2 ,1 9
4 lw $ 2 ,($ 2 )
5 a nd i $ 3 ,$ 6 ,3 1
6 s rlv $ 2 ,$ 2 ,$ 3
7 a nd i $ 2 ,$ 2 ,1
8 b e q $ 2 ,0 ,C
9 a d d iu $ 6 ,$ 6 ,1
1 0 s lti $ 2 ,$ 6 ,6 4
1 1 b ne $ 2 ,0 ,A
$6 $19
1
2
3
4
5
6
7
66
6
6
6,196
19
6,19
6,19
6,19
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Construction of DR for an instruction
1 s rl $ 2 ,$ 6 ,5
2 s ll $ 2 ,$ 2 ,2
3 a d d u $ 2 ,$ 2 ,1 9
4 lw $ 2 ,($ 2 )
5 a nd i $ 3 ,$ 6 ,3 1
6 s rlv $ 2 ,$ 2 ,$ 3
7 a nd i $ 2 ,$ 2 ,1
8 b e q $ 2 ,0 ,C
9 a d d iu $ 6 ,$ 6 ,1
1 0 s lti $ 2 ,$ 6 ,6 4
1 1 b ne $ 2 ,0 ,A
$6 $19
1
2
3
4
5
6
7
66
6
6
6,196
19
6,19
6,19
6,19
- Many instructions same GRs- Differentiate with DPI
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DR Construction
• Information encoded in instructions• May be done incrementally off-line
and stored in a table where it can be retrieved (imprecise?)
• No investigation of implementation specifics of DR construction
• Memory instructions propagate the dependence info through address operands (can be problematic - later)
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DBVP-R Predictor I n s tru c t io n D e p e n d e n c e R e c o rd
D e p e n d e n c e P a th I dG e n e ra te R e g is te rs
R e g is t e r H is to ry T a b le (R H T )
V a lu e P re d ic t io n T a b le (V P T )(p c s , o p ty p e s , im m e d ia te s e t c )
R e g N u m
R e g N u m
P re d ic t io nV P TI n d e x
History Table indexed with register names contains architected state
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DBVP Memory (DBVP-M)
• DBVP-R ignores memory dependences– often no correlation between load address
and value• DBVP-M: same as DBVP-R with
additional functionality and information: – propagation of DR through memory
• convert def-store-load-use dependence in a component to def-use
– livein memory locations(generate locations GLs): • converts values from memory to liveins
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DR Propagation Through Memory (spilled variable)
O p T y p e O p e ra n d s G R re g G R re g +m e m O u tp u tV a lu e s
1 add $ 2 ,$ 2 ,1 2 2 1 ,2 ,..,6 4
2 s t $ 2 ,($ 9 ) 2 ,9 2
- - - - - - - - - - - inte rve ning ins truc tio ns - - - - - - - - - - - - -
3 ld $ 2 ,($ 9 ) 9 2 1 ,2 ,..,6 4
4 s lt i $ 2 ,$ 2 ,6 4 9 2 (0 ) 631
5 bne $ 2 ,$ 0 ,... 9 2
$2
1
2s t
3 ld
4
5
2
9
9
2
$9
9
9
$2
1
2s t
3 ld
4
5
2
9
2
2
2
$9
2
p r o p ag at i o nt h r o u g hm e m o r y
G R reg G R reg +mem
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Livein Memory LocationsO pT ype O pe ra nds G R re g + m e m O utput V a lue s1 ld $2,($9) 9 0,1,2,..,63
2 ad d $2,$2,1 9 1,2,3,..,64
3 s t $2,($9) 9
4 s lti $2,$2,64 9 (0)631
5 b ne $2,$0,... 9
$9
1
2
3
4
5
9
9
9
9
9
G L i
1
2
3
4
5
G L i
G L i
G L i
G L i
G L i
C o n v e rt in g s to red e p e n d e n c e to a liv e in
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Generate Locations (GL) and Memory History Table(MHT)• GL is an index into a table (MHT)
with values written to memory• Store-load dependent pairs are
assigned MHT location
• Memory Dependence Prediction mechanisms can provide the extra functionality required by DBVP-M
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DBVP-M Predictor
I n s tru c t io n D e p e n d e n c e R e c o rd
D e p e n d e n c e P a th I dG e n e ra te R e g s /L o c s R e g is te r H is to ry T a b le (R H T )
V a lu e P re d ic t io n T a b le (V P T )
(p c s , o p ty p e s ,im m e d ia te s e tc )
M e m N u m
R e g N u m
P re d ic t io nV P TI n d e x
M e m o ry H is to ry T a b le (M H T )
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Differences DBVP and CB
• Information used to access tables and table sizes– History table is register indexed (vs
PC indexed)+smaller+may be easier to manage speculative
updates-possibly provide predictions later
– Smaller context is required to capture repeated behavior
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Outline
• Introduction• Background on value prediction• Dependence Info and Predictability• Dependence-based Value
Predictors• Results• Future work
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Experimental Framework
• SPECINT95 Benchmarks• Study predictors with simple delay
model:– predict d instructions (or up to first
misprediction)– update
• d is also maximum component size• DPI: pc, optypes and immediates
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DBVP-R: DPI Information d=1, 64K entry VPT
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DBVP-R: VPT Sized=1
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DBVP-R: Sensitivity to Delay64K VPT size
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Problems with increasing d
• Capacity: instruction in different components requires more information to be learned
$6 $19
1
2
3
4
5
6
7
66
6
6
6,196
19
6,19
6,19
6,19
$2 $19
2
3
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5
6
7
26
2
2,19
6
19
2,19
2,6,19
2,6,19
$6
Component Redundancy
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Problems with increasing d
• Capacity: instruction in different components requires more information to be learned
• Destr. Aliasing: multiple instances of same instruction same prediction
$6 $19
1
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5
6
7
66
6
6
6,196
19
6,19
6,19
6,19
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6,196
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6,19
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66
1 9
Multiple instances same GR and DPI
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Problems with increasing d
• Capacity: instruction in different components requires more information to be learned (destructive aliasing)
• Destr. Aliasing: multiple instances of same instruction same prediction
• Capacity: increasing d means more information needs to be learned
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Information to be learned
1,2,3,..,64
9
0/1
10
0/1
Two combinations
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Information to be learned
1,2,3,..,64
9
0/1
10
0/1
64 combinations
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Solutions for increasing d
• Careful partitioning• Transform different structure to same• Better DPI:
– position– count of outstanding instances of an
instruction– predict only liveouts
• Predict based on other information– recurrence, dependence distance...
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DBVP-M: MHT Sized=1, 64K VPT size
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DBVP-M: Sensitivity to Delay
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
DBVP vs CB
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Conclusions
• Dependence-based prediction holds potential to improve accuracy
• Use dependence information for prediction
• Propagation of dependence information through memory useful for increased accuracy
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Current and Future Work
• Refinement and evaluation of dependence-based prediction methods
• Evaluation in a processor • DR construction• Characterize program full
components– size, uniqueness, correlation to
predictability
Yanos SazeidesUniversity of Cyprus
UPC 17/5/01
Other Work/Interests
• Prediction and Speculation• Program Behavior• Distributed Microarchitectures• SMT• Benchmark characterization
I had a great time - thank you all!
Hasta luego amigas y amigos!
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