Improving Branch Prediction by Dynamic Dataflowbased Identification of Correlation Branches from a L

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Improving Branch Prediction by Dynamic
Dataflow-based Identification of Correlation
Branches from a Larger Global History

• CSE 340 Project Presentation
• Baha Guclu Dundar

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Outline

• Branch prediction overview
• Primitive techniques
• -Long global history
• -Selecting Correlated Branches
• Identifying Affector Branches in Run-time
• Implementing affector branches
• Building predictors that use affector information
• Experimental Evaluation

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Branch Prediction Overview

• Branch mis-prediction is the single most
  significant performance limiter for improving
  processor performance using deeper pipelining
  18.

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Branch Prediction Overview

• Trivial prediction
• Static prediction
• Line prediction
• Bimodal branch prediction
• Local branch prediction
• Global branch prediction
• Overriding branch prediction
• Other types

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Branch Prediction Overview

• Use multiple predictors with progressively
  increasing latencies and prediction accuracies in
  an overriding fashion.
• Each of these predictors provide its predictions
  at different stage. The ones at initial stages
  are less accurate. Predictions in further stage
  are expected to be more accurate.

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Selected predictors in this paper

• A simple 1 cycle line predictor provides
  predictions in first stage, followed by a more
  accurate global branch predictor, and finally a
  highly accurate corrector predictor. It corrects
  global predictor.

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Selected predictors in this paper Cnt

• Corrector predictor must have high accuracy
  comparing with other predictors.
• Focus on this paper is to propose and evaluate a
  new high-accuracy corrector predictor.
• Techniques are long global history and
  identifying correlated branches in this history
  by using runtime dataflow.

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Long Global history

• What if there is a large distance between two
  correlated branches?
• Use longer history
• How far in the past we should go? What is a good
  way to use longer history?

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Long History Cnt

• Length of global history lt log2 (branch
  prediction table size)
• Moreover, recent papers 910 show that looking
  longer histories (at least 64 branches) is
  necessary.

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Selecting Correlated Branches

• Throughout history, which branches we should
  select in long history? Brute force? No, this
  will increase second-level table size.
• Using hash table? No too much aliasing. We need
  to be careful.
• Not all branches in long history are correlated
  under branch prediction.
• Perceptron predictors gives weight for each
  branch in long history.

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Identifying Affector Branches in Run-time

• A branch becomes affector for a future branch if
  it can affect the outcome of future branchs
  source operands.

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Identifying Affector Branches in Run-time
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Identifying Affector Branches in Run-time

• We need to both keep both conventional global
  history and track run-time dataflow of the
  program and determine affector branches of last
  register.
• When a prediction is made for a branch, the
  affector information is used by source registers
  on the fly.
• Affector information on each node is determined
  by bitmap in dataflow.
• LSB of bit map denote the most recent dynamical
  branch.

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How to implement affector branches

• We keep a separate record of affector information
  corresponding to each architectural registers as
  entries in Affector Register File(ARF)

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How to implement affector branches

• Classifying three dynamic instructions
• Conditional branch instructions
• Register-writing instructions
• Non-register-writing instructions jumps,
  returns, system calls, stores etc.

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How to implement affector branches
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Back to the Example
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(No Transcript)
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Building predictors that use affector information

• There are two proposed predictors
• Zeroing scheme
• Packing scheme

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Building predictors that use affector information

• Resulting register bits are hashed down to the
  required number of bits for predictor index.

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In a big picture
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Experimental Evaluation

• For the simulation SimpleScalar v3.0 using Alpha
  ISA was used for 12 benchmarks from SPEC95 and
  SPEC2000 integer benchmark suites.

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Misprediction Results
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Experimental Evaluation Cnt
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Conclusion

• We used long global history and identifying
  correlated branches by using runtime dataflow
  information.
• We examined hardware structures, ARF to identify
  affector branches for each dynamical branch.
• Hardware overhead for identifying affectors from
  64 branch global history is 312 bytes.
• We go through two prediction schemes.
• Experimental studies show that adding 8 KB
  corrector predictor to a 16 KB perception
  predictor reduces average misprediction rate from
  6.3 to 5.7 which is achieved by 64 KB
  perception predictor.

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Appendix A Global Branch Predictors

• What if we came across the same branch after
  executing several branch instructions?
• We can use Global branch predictors to deal with
  this problem. They correlate branchs outcome
  with the history preceding dynamic branches.
• Global branch predictors have good accuracy
  percentage. However, it may take 2-3 cycles to
  execute it.