Analysis of the OGEHL branch predictor

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Analysis of the O-GEHL branch predictor

• Optimized GEometric History Length
• André Seznec
• IRISA/INRIA/HIPEAC

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Objectives

• State of the art accuracy
• Any gain in branch prediction accuracy results in
  performance gain and power consumption gain
• Keep the design implementable
• Rely on a single prediction scheme
• Use only global history information
• Designers hate maintaining speculative local
  history

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The basis A Multiple history length predictor
TO
T1
T2
?
L(0)
T3
L(1)
L(2)
T4
L(3)
L(4)
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Selecting between multiple predictions

• Classic solution
• Use of a meta predictor
• wasting storage !?!
• chosing among 5 or 10 predictions ??
• Neural inspired predictors
• Use an adder tree instead of a meta-predictor

Lets use the adder tree
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Final computation through a sum
L(0)
PredictionSign
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From old experience on 2bcgskew

• Some applications benefit from 100 bits
  histories
• Some dont !!

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GEometric History Length predictor
The set of history lengths forms a geometric
series
0, 2, 4, 8, 16, 32, 64, 128
What is important L(i)-L(i-1) is drastically
increasing
Spends most of the storage for short history !!
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Update policy

• Perceptron inspired threshold based
• Perceptron-like threshold did not work
• Reasonable fixed threshold Number of tables

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Dynamic update threshold fitting

• On an O-GEHL predictor, best threshold depends
  on
• the application ?
• the predictor size ?
• the counter width ?
• By chance, on most applications, for the best
  fixed threshold,
• updates on mispredictions updates on correct
  predictions
• Monitor the difference
• and adapt the update threshold

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Adaptative history length fitting (inspired by
Juan et al 98)

• (½ applications L(7) lt 50)
• ?
• (½ applications L(7) gt 150)
• Let us adapt some history lengths to the behavior
  of each application
• 8 tables
• T2 L(2) and L(8)
• T4 L(4) and L(9)
• T6 L(6) and L(10)

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Adaptative history length fitting (2)

• Intuition
• if high degree of conflicts on T7, stick with
  short history
• Implementation
• monitoring of aliasing on updates on T7 through a
  tag bit and a counter
• Simple is sufficient
• Flipping from short to long histories and
  vice-versa

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Evaluation framework

• 1st Championship Branch Prediction traces
• 20 traces including system activity
• Floating point apps loop dominated
• Integer apps usual SPECINT
• Multimedia apps
• Server workload apps very large footprint

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Reference configurationpresented for
Championship Branch Prediction

• 8 tables
• 2 Kentries except T1, 1Kentries
• 5 bit counters for T0 and T1, 4 bit counters
  otherwise
• 1 Kbits of one bit tags associated with T7
• 10K 5K 6x8K 1K 64K
• L(1) 3 and L(10) 200
• 0,3,5,8,12,19,31,49,75,125,200

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A case for the OGEHL predictor

• 2nd at CBP 2.82 misp/KI
• Best practice award
• The predictor the closest to a possible hardware
  implementation
• Does not use exotic features
• Various prime numbers, etc
• Strange initial state
• Very short warming intervals
• Chaining all simulations 2.84 misp/KI

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A case for the OGEHL predictor (2)

• High accuracy
• 32Kbits (3,150) 3.41 misp/KI
• better than any implementable 128 Kbits
  predictor before CBP
• 128 Kbits 2bcgkew (6,6,24,48) 3.55 misp/KI
• 176 Kbits PBNP (43) 3.67 misp/KI
• 1Mbits (5,300) 2.27 misp/KI
• 1Mbit 2bcgskew (9,9,36,72) 3.19 misp/KI
• 1888 Kbits PBNP (58) 3.23 misp/KI

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A case for the OGEHL predictor (3)

• Robustness to variations of history lengths
  choices
• L(1) in 2,6, L(10) in 125,300
• misp. rate lt 2.96 misp/KI
• Geometric series not a bad formula !!
• best geometric L(1)3, L(10)223, 2.80 misp/KI
• best overall 0, 2, 4, 9, 12, 18, 31, 54, 114,
  145, 266 2.78 misp/KI

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Impact of the number of components

• 4 components 8 components
• 64 Kbits 3.02 -- 2.84 misp/KI
• 256Kbits 2.59 -- 2.44 misp/KI
• 1Mbit 2.40 -- 2.27 misp/KI
• 6 components 12 components
• 48 Kbits 3.02 3.03 misp/KI
• 768Kbits 2.35 2.25 misp/KI
• 4 to 12 components bring high accuracy ?

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Impact of counter width

• Robustness to counter width variations
• 3-bit counter, 49 Kbits 3.09 misp/KI
• Dynamic update threshold fitting helps a lot
• 5-bit counter 79 Kbits 2.79 misp/KI
• 4-bit is the best tradeoff

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Prediction computation time

• 3 successive steps
• Index computation a 3-entry XOR gate
• Table read
• Adder tree
• May not fit on a single cycle
• But can be ahead pipelined !

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Ahead pipelining a global history branch
predictor (principle)

• Initiate branch prediction X1 cycles in advance
  to provide the prediction in time
• Use information available
• X-block ahead instruction address
• X-block ahead history
• To ensure accuracy
• Use intermediate path information

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Practice
Ahead OGEHL 8 // prediction computations
Ha A
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Ahead Pipelined 64 Kbits OGEHL

• 3-block 2.94 misp/KI
• 4-block 2.99 misp/KI
• 5-block 3.04 misp/KI

Not such a huge accuracy loss ?
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A final case for the O-GEHL predictor

• delivers state-of-the-art accuracy
• uses only global information
• Very long history 200 bits !!
• can be ahead pipelined
• many effective design points
• Nb of tables, counter width, history lengths
• prediction computation logic complexity is low
• (compared with concurrent predictors?)

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The End ?