CS 7810 Lecture 8

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CS 7810 Lecture 8
Memory Dependence Prediction using Store
Sets G.Z. Chrysos and J.S. Emer Proceedings of
ISCA-25 1998
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Lifetime of a Load
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LSQ Basics
Ld/St Address Data Completed
Store Unknown 1000 --
Load x40000000 -- --
Store x50000000 -- --
Load x50000000 -- --
Load x30000000 -- --

• An incomplete store stalls all future loads No
• Speculation the paper is overly conservative
• because it also waits for store values
• Most of these stalls are unnecessary
  artificial
• dependences

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Aggressive Approach

• Assume that loads do not conflict with earlier
• stores all loads and stores execute out of
  order
• -- Naive Speculation
• When there is a conflict, the load behaves like
  a
• branch mispredict all subsequent instructions
• are squashed and re-fetched
• Expensive 30-cycle penalty
• Rename checkpoints for all instructions
• Re-execute only the dependent instructions?
• more complex, better performance

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Ideal Model

• In the perfect model, loads only wait for
  conflicting
• stores no artificial dependences and no
• memory-order violations

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False Dependences and Violations
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Store Sets Concept

• For every load, keep track of all stores that it
• has conflicted with in the past
• A load does not issue if members of its store
• set have not finished (dependences are
  introduced
• at the time of dispatch)
• The implementation is easy if
• a load depends on only one store
• a store is present in only one store set

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Trivial Implementations

• Execution time normalized to an ideal store set
• implementation

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Ideal Store Set Predictor

• An occasional memory-order violation can
• introduce many false dependencies hence,
• use saturating counters

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Implementation Overview

• Every ld/st depends on the last store in its set
• Causes serialized stores and false dependences

st
st
st
st
st
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Store Set Implementation

• Every load and store belong to one color keep
  track of the
• last writer for each color mpreds can pose
  problems
• Colors are merged as you discover m-o violations

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Store Set Merging

• Store set merging improves performance by 12
• Note that merging happens gradually no need to
• instantly correct all entries in the table

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Design Details

• Merging store sets
• To deal with occasional dependences and
  conflicts
• clear the table every million cycles
• use saturating counters for each entry
• The SSIT needs 4K entries and the LFST needs
• 128 entries

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Results
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Related Work

• Store barrier cache identify stores that are
  likely
• to pose conflicts
• Keep track of all store-load conflict pairs and
• associatively check for dependences while
• dispatching instructions

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Next Weeks Paper

• Effective Hardware-Based Prefetching for
• High-Performance Microprocessors, T.F. Chen
• and J.L. Baer, IEEE Transactions on Computers,
• May 1995

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Title

• Bullet