Branch
Classification:
a
New
Mechanism
for
Improving
Branch
Predictor
Performance
Po-Yung
Chang
Eric
Department
of
Electrical
Engineering
The
Ann
Tse-Yu
Hao
University
Arbor,
and
of
Clara,
48109-2122
CA
Abstract
95051
predictor
minimizes
dicting
is wide
agreement
impediments
to the
pipelined
tive
branches
execution
one of the most
performance
superscalar
ditional
that
in
the
seems
branch
problem,
branch
is mispredicted.
is the
instruction
speculative
Therefore,
predictor;
9570 accuracy
This
paper
proposes
branch
prove
the
accuracy
allows
associated
dict
its
with
predictor
predictor
is best
suited.
scheme,
the
predictor
this
predicts
This
a hybrid
several
reported
Keywords:
speculative
in the
such
each
that
branches
suggests
any
for
one
that
im-
cycles
clas-
branch
classification,
ipc denotes
are
executed
of branches
branch
can
one method
branch
predictors
achieve
higher
have
This
super-
paper
sults.
of instructions
if they
to the
reduce
interrupt
instruction
the performance
the
pipeline
steady
[4].
Section
num-
that
r * ipc = 0.9,
a
a prediction
as a technique
of branch
that
predictors.
Using
we analyze
hybrid
several
predictors
any branch
that
predictors
that
as follows:
model,
schemes,
analyzes
and
some
2 presents
Section
proposes
schemes,
4 provides
Section
classification.
3 describes
previously
several
pro-
new
hybrid
presents
simulation
concluding
remarks.
re-
Branch
2
Classification
A branch
Branch
to copy
of the
instructions,
of
supply
into
fee all or part of this material is
granted provided that the copies are not made or distributed for
direct commercial advantage, the ACM copyright notice and the
title of the publication and its date appear, and notice is given
that copying is by permission of the Association of Computing
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MICRO 27- 11/94 San Jose CA USA
0 1994 ACM 0-89791 -707-3/94/001 1..$3.50
Permission
ratio
reported.
classification
prediction
of
p denotes
97.770.
than
of branch
prediction
as
number
10~0 requires
propose
accuracy
the
and
classification,
and
and
of instructions
C=5
classification
is organized
concept
branch
Introduction
can significantly
algo-
defined
of total
number
than
accuracy
been
the
number
than
of branch
previously
a branch
processors
branch
misses
is
misprediction,
For
branch
previ-
cache
T denotes
cycle.
the
as
C’ denotes
of less
higher
posed
Branches
if that
microproces-
where
the
p of greater
improve
the
pipelined
in-
work
prediction
penalty
the average
per
We introduce
pro-
such
to a branch
over
penalty
accuracy
scalar.
1
branch
branch
accuracy,
and
predictor,
performance,
due
to pre-
predictor
branch
processor
wasted
the prediction
it
stalls
* r * ipc),
branch
and
the
all speculative
away
accurate
the
ber
literature.
execution,
ignore
to be
which
Because
fetching
to a high-performance
confiicts,
C * ((l-p)
classification
achieves
branch
we
bus
component
predictors,
predictor
than
suited
a hybrid
branch
branch
accuracy
best
approach,
those
paper
Branch
instruction
st ails by pre-
and
be thrown
a very
is important
If
accu-
help
path.
must
of pipeline
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sor,
if a
enough.
to
predictors.
branch
branch
Using
analyzes
prediction
ously
of branch
can be constructed
branch
poses
classification
an individual
direction.
is not good
of the
that
a branch
rithm
to the
a very
from
beyond
the number
direction
is mispredicted,
Specula-
is discarded
we need
branch
future
of choice
work
the
structions
of con-
stream.
rate
sification
and
presence
to be one solution
but
important
of current
processors
Science
Corporation*
Santa
There
Computer
Patt
Michigan
Michigan
Intel
Yale
Yeh*
without
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sets
or
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7 shows
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Table
4: Summary
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that
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[6]
Lee
S.
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idea
of branch
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sity
of Michigan.
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entific
and
of our
for
We would
ful
HPS
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provide,
his comments
also like
at
the
of our industrial
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June
the
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in particular,
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Branch
accu-
With
PG+gshare
information
execution
combination
96.47~o
by
branch
Thus,
branch
a fixed
and
each
statically.
to more
as compared
rate
[2] J.A.
can
Furthermore,
branches.
bytes,
classification,
for
and/or
With
of 32K
miss
hardware
be optimized
accuracy,
the
branch
predictor
can
more
predictor.
mixed-direction
prediction
With
pile
dynamic
predictor
predict
a 96.0%
branches,
the
Comparison
use
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Computer
Two
20th
of DyLevels
Annual
Architecture,
of
In-