PL-4089, Accelerating and Evaluating OpenCL Graph Applications, by Shuai Che, Bradford Bechmann, Steve Reinhardt and Kevin Skadron at the AMD Developer Summit (APU13) November 11-13, 2014.

1. ACCELERATING
AND
EVALUATING
OPENCL
GRAPH
APPLICATIONS
SHUAI
CHE
,
BRAD
BECKMANN,
STEVE
REINHARDT
AND
KEVIN
SKADRON

2. AGENDA
Background
and
Graph
Applica8ons
Panno8a
OpenCL™
Graph
Applica8ons
Performance
Evalua8on
and
Discussion
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3. GRAPH
APPLICATIONS
! Intelligence
‒ Business
analy8cs,
security
and
scien8fic
discovery
! Social
networks
‒ Facebook,
TwiVer,
LinkedIn,
Weibo,
etc.
! Life
science
and
healthcare
‒ Disease
and
drug
research,
life
system
research
! Infrastructure
‒ Transporta8on,
power
grid,
energy
and
water
supply
! Scien8fic
and
engineering
simula8ons
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4. GRAPH
APPLICATIONS
! Low
arithme8c
intensity
and
data
reuse
! Not
floa8ng-­‐point
intensive
! Branch
divergence
‒ Part
of
threads
in
a
wavefront
are
ac8ve
! Memory
divergence
‒ Data
distributed
in
different
regions
of
memory
‒ A
challenge
to
op8mize
data
layouts
and
memory
accesses
! Load
imbalance
‒ Uneven
work
distribu8on
across
different
threads
‒ Short-­‐running
threads
wait
for
long-­‐running
threads
! Parallelism
‒ Changing
degree
of
parallelism
across
itera8ons
‒ Underu8liza8on
of
compute
units
for
certain
phases
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5. PANNOTIA
! A
graph
applica8on
suite
for
GPGPU
! Eight
diverse
graph
algorithms,
e.g.,
shortest
path,
graph
par88oning,
web
analysis
and
social
network
! Implemented
in
C
+
OpenCL™
! Some
are
OpenCL
implementa8ons
based
on
algorithms
of
prior
work
! Ini8al
implementa8on
is
for
a
single
GPU
node
! Further
algorithmic
and
hardware-­‐specific
op8miza8ons
are
in
progress
! Details
of
Panno8a
can
be
found
in
our
paper
published
in
2013
IEEE
Interna8onal
Symposium
on
Workload
Characteriza8on
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6. PANNOTIA
Applica7ons
Domains
Single-­‐Source
Shortest
Path
Shortest
Path
Connected
Component
Labeling
Graph
Clustering
Graph
Coloring
Graph
Par88oning
Floyd-­‐Warshall
Shortest
Path
Maximal
Independent
Set
Graph
Par88oning
Betweeness
Centrality
Social
Network
Friend
Recommenda8on
Social
Network
Page
Rank
Web
Analysis
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7. GRAPH
INPUT
AND
DATA
STRUCTURE
! Real-­‐world
graphs
‒ The
University
of
Florida
Sparse
Matrix
Collec8on
‒ The
9th
DIMACS
Implementa8on
Challenges
‒ The10th
DIMACS
Implementa8on
Challenges
!
Synthe8c
graphs
‒
Random-­‐graph
generator
from
Georgia
Tech
!
Graph
input
formats
‒
Coordinate
Format
‒
METIS
‒
Matrix
Market
!
Data
structure
representa8on
‒
CSR,
COO,
ELL
…
‒
2D
adjacency
matrix
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8. SINGLE
SOURCE
SHORTEST
PATH
! Finds
the
path
with
the
shortest
path
between
the
source
node
and
all
the
other
nodes
in
the
graph
Vid
Dist
7
0
15
2
13
6
23
18
4
1
5
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0
1
3
2
1
3
8
4
16
5
2
1
0
8
3
19
6
16

9. CONNECTED
COMPONENT
LABELING
! Detect
connected
regions
in
graphs
and
images
! Connected
components
are
the
nodes
in
a
graph
that
point
to
the
same
root
q
p
s
r
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10. GRAPH
COLORING
! Assign
colors
(integers)
to
ver8ces
with
no
two
adjacent
ver8ces
with
the
same
color
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11. FLOYD-­‐WARSHALL
! Solves
the
all-­‐pairs
shortest
path
(APSP)
problem
–
finding
the
shortest
path
from
every
possible
source
to
every
possible
des8na8on
!
A
dynamic
programming
approach
shortestPath(i,
j,
k)
returns
the
shortest
path
from
i
to
j
with
ver8ces
from
{1,2,...,k}
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12. MAXIMAL
INDEPENDENT
SET
! Independent
set:
no
two
ver8ces
are
neighbors
! Maximal
Independent
set:
impossible
to
add
another
vertex
to
s8ll
keep
independent
0
2
3
5
4
1
6
7
S
=
{0,
4,
6}
is
an
Maximal
Independent
Set
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13. BETWEENNESS
CENTRALITY
! Centrality
determines
the
rela8ve
importance
of
a
vertex
within
the
graph
(e.g.
degree,
betweenness,
closeness
…)
! Betweenness
Centrality
quan8fies
the
number
of
8mes
a
node
acts
as
a
bridge
along
the
shortest
path
between
two
other
nodes.
σ st (v)
BC (v) = ∑
s ≠ v ≠ t σ st
σ st
σ st (v)
no.
of
shortest
paths
between
nodes
s
and
t
no.
of
shortest
paths
between
nodes
s
and
t
passing
through
v
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14. FRIEND
RECOMMENDATION
!
Recommend
friend
connec8ons
–
a
common
feature
in
social
websites
!
A
simple
Map-­‐Reduce
like
algorithm
“Andy” =
[
“Brad”,
“Derek”,
“Shuai”,
…]
Andy
!
<“Brad”,
“Derek”,
“Andy”>
<“Brad”,
“Shuai”,
“Andy”>
<“Derek”,
“Brad”,
“Andy”>
<“Derek”,
“Shuai”,
“Andy”>
<“Shuai”,
“Derek”,
“Andy”>
<“Shuai”,
“Brad”,
“Andy”>
Andy
recommends
Brad
to
Shuai
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15. PAGERANK
!
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16. PERFORMANCE
BENEFITS
! Speedups
are
up
to
11x
(an
AMD
“Tahi8”
discrete
GPU
v.s.
4
CPU
cores
on
A8)
! PCI-­‐E
overhead
is
included
! Performance
benefits
depend
on
graph
input
datasets
15
Parallel Speedup
10
5
0
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17. EXECUTION
TIME
BREAKDOWN
(D-­‐GPU)
! The
por8on
of
GPU
execu8on:
8%
-­‐
99%
! Some
further
GPU
offload
can
be
done
(e.g.
FRD
and
MIS)
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and
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Graph
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20,
2013
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CONFIDENTIAL

18. PARALLELISM
(ACTIVE
VERTICES
OVER
TIME)
Single-­‐Source
Shortest
Path
(Road
Network
-­‐
NY)
120000
0
400000
0
Time
Graph
Coloring
(G3
Circuit)
Time
18
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and
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19. LOAD
IMBALANCE
(DEGREE
DISTRIBUTION)
Single-­‐Source
Shortest
Path
(Road
Network)
1
2
3
4
5
6
100%
0%
Time
Graph
Coloring
(G3
Circuit)
1
2
3
4
100%
0%
Time
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5
7
8

20. HIERARCHICAL
CLUSTERING
!
Different
program-­‐input
pairs
may
have
vastly
different
characteris8cs!
BC-­‐2k
BC-­‐1k
MIS-­‐US-­‐NW
PRK-­‐2k
CLR-­‐G3-­‐circuit
CLR-­‐ecology
MIS-­‐ecology
FW-­‐512-­‐64k
FW-­‐256-­‐16k
CCL-­‐lena
CCL-­‐deposit
DJK-­‐US-­‐NW
DJK-­‐US-­‐CA
MIS-­‐shell
CLR-­‐shell
PRK-­‐flicker
FRD-­‐coAuthor
0.0
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4.6

21. L2
HIT
RATE
OVER
TIME
(SSSP)
! The
cache
hit
rate
first
improves,
then
degrades,
improves
again
and
finally
degrades
with
some
fluctua8ons
in
the
middle
60
Hit
Rate
50
40
30
20
10
0
Time
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and
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CONFIDENTIAL

22. ARCHITECTURAL
IMPLICATIONS
(SCALAR
UNIT)
Scalar
SIMD
SIMD
Scalar
SIMD
Time
A
B
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Graph
Traversal

23. ARCHITECTURAL
IMPLICATIONS
! Possibly
include
narrower
SIMD
units
or
heterogeneous
SIMD
units
Scalar
Narrow
SIMD
Wide
SIMD
! Resource
management
and
scheduling
‒ Switch
the
task
between
the
CPU
and
the
GPU
based
on
parallelism
‒ Use
only
“enough”
SIMD
engines
and
save
power
CPU
120000
0
GPU
GPU
Time
A
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B

24. CONCLUSION
AND
FUTURE
WORK
! Graph
applica8ons
are
an
emerging
workload
domain
! Panno8a
is
a
first-­‐step
aVempt
to
evaluate
diverse
graph
building
blocks
on
GPUs
Next-­‐Step
Goals:
! Add
more
applica8ons
(e.g.
matching,
spanning
tree,
flow)
! Op8mize
Panno8a
applica8ons
! Extend
to
mul8ple
GPU
nodes
and
across
CPU
and
GPU
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25. DISCLAIMER
&
ATTRIBUTION
The
informa8on
presented
in
this
document
is
for
informa8onal
purposes
only
and
may
contain
technical
inaccuracies,
omissions
and
typographical
errors.
The
informa8on
contained
herein
is
subject
to
change
and
may
be
rendered
inaccurate
for
many
reasons,
including
but
not
limited
to
product
and
roadmap
changes,
component
and
motherboard
version
changes,
new
model
and/or
product
releases,
product
differences
between
differing
manufacturers,
so{ware
changes,
BIOS
flashes,
firmware
upgrades,
or
the
like.
AMD
assumes
no
obliga8on
to
update
or
otherwise
correct
or
revise
this
informa8on.
However,
AMD
reserves
the
right
to
revise
this
informa8on
and
to
make
changes
from
8me
to
8me
to
the
content
hereof
without
obliga8on
of
AMD
to
no8fy
any
person
of
such
revisions
or
changes.
AMD
MAKES
NO
REPRESENTATIONS
OR
WARRANTIES
WITH
RESPECT
TO
THE
CONTENTS
HEREOF
AND
ASSUMES
NO
RESPONSIBILITY
FOR
ANY
INACCURACIES,
ERRORS
OR
OMISSIONS
THAT
MAY
APPEAR
IN
THIS
INFORMATION.
AMD
SPECIFICALLY
DISCLAIMS
ANY
IMPLIED
WARRANTIES
OF
MERCHANTABILITY
OR
FITNESS
FOR
ANY
PARTICULAR
PURPOSE.
IN
NO
EVENT
WILL
AMD
BE
LIABLE
TO
ANY
PERSON
FOR
ANY
DIRECT,
INDIRECT,
SPECIAL
OR
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CONSEQUENTIAL
DAMAGES
ARISING
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HEREIN,
EVEN
IF
AMD
IS
EXPRESSLY
ADVISED
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POSSIBILITY
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SUCH
DAMAGES.
ATTRIBUTION
©
2013
Advanced
Micro
Devices,
Inc.
All
rights
reserved.
AMD,
the
AMD
Arrow
logo
and
combina8ons
thereof
are
trademarks
of
Advanced
Micro
Devices,
Inc.
in
the
United
States
and/or
other
jurisdic8ons.
OpenCL
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Apple
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Other
names
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their
respec8ve
owners.
25
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20,
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