Shanghai Many-Core Workshop March 27-28 2008
Judy Qiu
xqiu@indiana.edu, http://www.infomall.org/salsa
Research Computing UITS, Indiana University Bloomington IN
Geoffrey Fox, Huapeng Yuan, Seung-Hee Bae
Community Grids Laboratory, Indiana University Bloomington IN
George Chrysanthakopoulos, Henrik Frystyk Nielsen
Microsoft Research, Redmond WA
 What applications can use the 128 cores expected in 2013?
 Over same time period real-time and archival data will increase as fast
as or faster than computing
 Internet data fetched to local PC or stored in “cloud”
 Surveillance
 Environmental monitors, Instruments such as LHC at CERN, High throughput
screening in bio- and chemo-informatics
 Results of Simulations
 Intel RMS analysis suggests Gaming and Generalized decision support
(data mining) are ways of using these cycles
 SALSA is developing a suite of parallel data-mining capabilities:
currently




Clustering with deterministic annealing (DA)
Mixture Models (Expectation Maximization) with DA
Metric Space Mapping for visualization and analysis
Matrix algebra as needed
Service Aggregated Linked Sequential Activities
 Link parallel and distributed (Grid) computing by developing parallel modules as
services and not as programs or libraries
 e.g. clustering algorithm is a service running on multiple cores
 We can divide problem into two parts:
 “Micro-parallelism” : High Performance scalable (in number of cores) parallel kernels or
libraries
 “Macro-parallelism” : Composition of kernels into complete applications
 Two styles of “micro-parallelism”
 Dynamic search as in scheduling algorithms, Hidden Markov Methods (speech
recognition), and computer chess (pruned tree search); irregular synchronization with
dynamic threads
 “MPI Style” i.e. several threads running typically in SPMD (Single Program Multiple
Data); collective synchronization of all threads together
 Most data-mining algorithms (in INTEL RMS) are “MPI Style” and very close to
scientific algorithms
 Status: is developing a suite of parallel data-mining capabilities: currently
Clustering with deterministic annealing (DA)
Mixture Models (Expectation Maximization) with DA
Metric Space Mapping for visualization and analysis
Matrix algebra as needed
 Results: currently
Microsoft CCR supports MPI, dynamic threading and via DSS a service model of computing;
Detailed performance measurements with Speedups of 7.5 or above on 8-core systems for “large
problems” using deterministic annealed (avoid local minima) algorithms for clustering, Gaussian
Mixtures, GTM (dimensional reduction) etc.
 Collaboration:
SALSA Team
Geoffrey Fox
Xiaohong Qiu
Seung-Hee Bae
HuapengYuan
Indiana University
 Technology Collaboration
 Application Collaboration
George Chrysanthakopoulos
Henrik Frystyk Nielsen
Microsoft
Cheminformatics
Rajarshi Guha
David Wild
Bioinformatics
Haiku Tang
Demographics (GIS)
Neil Devadasan
IU Bloomington and IUPUI
 We implement micro-parallelism using Microsoft CCR
(Concurrency and Coordination Runtime) as it supports both MPI rendezvous and
dynamic (spawned) threading style of parallelism http://msdn.microsoft.com/robotics/
 CCR Supports exchange of messages between threads using named ports and has
primitives like:
 FromHandler: Spawn threads without reading ports
 Receive: Each handler reads one item from a single port
 MultipleItemReceive: Each handler reads a prescribed number of items of a given type from
a given port. Note items in a port can be general structures but all must have same type.
 MultiplePortReceive: Each handler reads a one item of a given type from multiple ports.
 CCR has fewer primitives than MPI but can implement MPI collectives efficiently
 Use DSS (Decentralized System Services) built in terms of CCR for service model
 DSS has ~35 µs and CCR a few µs overhead (latency, details later)
N data points E(x) in D dimensions space and minimize F by EM
N
F  T  p( x) ln{ k 1 exp[( E ( x)  Y ( k )) 2 / T ]
K
x 1
Deterministic Annealing Clustering (DAC)
• F is Free Energy
• EM is well known expectation maximization method
•p(x) with  p(x) =1
•T is annealing temperature varied down from  with
final value of 1
• Determine cluster centerY(k) by EM method
• K (number of clusters) starts at 1 and is incremented by
algorithm
Decrease temperature (distance scale) to discover more clusters
Total
Asian
Hispanic
Renters
30 Clusters
GIS30Clustering
Clusters
10 Clusters
F({Y}, T)
Solve Linear
Equations for
each temperature
Nonlinearity
removed by
approximating
with solution at
previous higher
temperature
Configuration {Y}
Minimum evolving as temperature decreases
Movement at fixed temperature going to local minima if not
initialized “correctly”
N data points E(x) in D dim. space and Minimize F by EM
N
F  T  a ( x) ln{ k 1 g (k ) exp[ 0.5( E ( x)  Y (k )) 2 / (Ts(k ))]
K
x 1
Deterministic
Generative
Traditional
Topographic
Gaussian
Annealing
Clustering
Mapping
(GTM)
(DAC)
Deterministic
Annealing
Gaussian
mixture
models
GM
Mixture
models
(DAGM)
• a(x)
= 1/N or
generally
p(x)
D/2 with  p(x) =1
• a(x) = 1 and g(k) = (1/K)(/2)
•and
As s(k)=0.5
DAGM but set T=1 and fix K
•• g(k)=1
a(x)
=
1
• s(k) = 1/  and T = 1
• T is annealing
temperature
2)D/2}1/T
varied down from 
M W/(2(k)
•Y(k) •= g(k)={P
m=1DAGTM:

(X(k))
km m
Deterministic
Annealed
with
final
value
of
1
2
2/2 Gaussian)
• s(k)=
(k)
(taking
case
of(X-
spherical
• Choose
fixed

(X)
=
exp(
0.5
)
)
m
m
Generative
Topographic
Mapping
• Vary
cluster centerY(k)
but can
calculate
weight
T misand
annealing
temperature
varied
down
from

• Vary•W

but
fix
values
of
M
and
K
a
priori
2
• GTM
has several
natural
annealing
P
and
correlation
matrix
s(k)
=
(k)
(even
for space
k
with
final
value
of
1
•Y(k) E(x)versions
Wm are2 vectors
in
original
high
D
dimension
based
on either DAC
or DAGM:
matrix
(k)
)
using
IDENTICAL
formulae
for space
•
Vary
Y(k)
P
and
(k)
• X(k) andunder
m areinvestigation
vectors
in
2
dimensional
mapped
k
Gaussian
mixtures
• K starts at 1 and is incremented by algorithm
•K starts at 1 and is incremented by algorithm
SALSA
“Main Thread” and Memory M
MPI/CCR/DSS
From other nodes
MPI/CCR/DSS
From other nodes
0
m0
1
m1
2
m2
3
m3
4
m4
5
m5
6
m6
7
m7
Subsidiary threads t with memory mt
 Use Data Decomposition as in classic distributed memory but use
shared memory for read variables. Each thread uses a “local” array
for written variables to get good cache performance
 Multicore and Cluster use same parallel algorithms but different
runtime implementations; algorithms are



Accumulate matrix and vector elements in each process/thread
At iteration barrier, combine contributions (MPI_Reduce)
Linear Algebra (multiplication, equation solving, SVD)
Parallel Overhead
on 8 Threads Intel 8b
0.45
10 Clusters
0.4
Speedup = 8/(1+Overhead)
0.35
Overhead = Constant1 + Constant2/n
Constant1 = 0.05 to 0.1 (Client Windows) due to
thread runtime fluctuations
0.3
0.25
20 Clusters
0.2
0.15
0.1
0.05
10000/(Grain Size n = points per core)
0
0
0.5
1
1.5
2
2.5
3
3.5
4
Speedup = Number of cores/(1+f)
f = (Sum of Overheads)/(Computation per
core)
Computation  Grain Size n . # Clusters K
Overheads are
Synchronization: small with CCR
Load Balance: good
Memory Bandwidth Limit:  0 as K  
Cache Use/Interference: Important
Runtime Fluctuations: Dominant large n, K
All our “real” problems have f ≤ 0.05 and
speedups on 8 core systems greater than 7.6
Multicore Matrix Multiplication
(dominant linear algebra in GTM)
10,000.00
Execution Time
Seconds 4096X4096 matrices
1 Core
1,000.00
Parallel Overhead
 1%
8 Cores
100.00
Block Size
10.00
1
0.14
10
100
1000
10000
Parallel GTM Performance
0.12
Fractional
Overhead
f
0.1
0.08
0.06
4096 Interpolating Clusters
0.04
0.02
1/(Grain Size n)
0
0
0.002
n = 500
0.004
0.006
0.008
0.01
100
0.012
0.014
0.016
0.018
0.0
SALSA50
 Deterministic Annealing
for Clustering of 335
compounds
 Method works on much
larger sets but choose
this as answer known
 GTM (Generative
Topographic Mapping)
used for mapping 155D
to 2D latent space
 Much better than PCA
(Principal Component
Analysis) or SOM (Self
Organizing Maps)
Parallel Generative Topographic Mapping GTM
Reduce dimensionality preserving
topology and perhaps distances
Here project to 2D
GTM Projection of PubChem:
10,926,94 compounds in 166
dimension binary property space takes
4 days on 8 cores. 64X64 mesh of GTM
clusters interpolates PubChem. Could
usefully use 1024 cores! David Wild will
use for GIS style 2D browsing interface
to chemistry
PCA
GTM
Linear PCA v. nonlinear GTM on 6 Gaussians in 3D
PCA is Principal Component Analysis
GTM Projection of 2 clusters
of 335 compounds in 155
SALSA
dimensions
 Micro-parallelism uses low latency CCR threads or MPI processes
 Services can be used where loose coupling natural
 Input data
 Algorithms
PCA
 DAC GTM GM DAGM DAGTM – both for complete algorithm and for
each iteration
 Linear Algebra used inside or outside above
 Metric embedding MDS, Bourgain, Quadratic Programming ….
 HMM, SVM ….
 User interface: GIS (Web map Service) or equivalent

Average run time (microseconds)
350
DSS Service Measurements
300
250
200
150
100
50
0
1
10
100
1000
10000
Round
trips of simultaneous two-way service
Timing of HP Opteron Multicore as a function
of number
messages processed (November 2006 DSS Release)
 Measurements of Axis 2 shows about 500 microseconds – DSS is 10 times better
Machine
Intel8c:gf12
(8 core 2.33 Ghz)
(in 2 chips)
Intel8c:gf20
(8 core 2.33 Ghz)
Intel8b
(8 core 2.66 Ghz)
AMD4
(4 core 2.19 Ghz)
Intel4
(4 core 2.8 Ghz)
OS
Runtime
Grains
Parallelism
MPI Exchange Latency (µs)
MPJE (Java)
Process
8
181
MPICH2 (C)
Process
8
40.0
MPICH2: Fast
Process
8
39.3
Nemesis
Process
8
4.21
MPJE
Process
8
157
mpiJava
Process
8
111
MPICH2
Process
8
64.2
Vista
MPJE
Process
8
170
Fedora
MPJE
Process
8
142
Fedora
mpiJava
Process
8
100
Vista
CCR (C#)
Thread
8
20.2
XP
MPJE
Process
4
185
MPJE
Process
4
152
mpiJava
Process
4
99.4
MPICH2
Process
4
39.3
XP
CCR
Thread
4
16.3
XP
CCR
Thread
4
25.8
Redhat
Fedora
Redhat
Intel8b: 8 Core
(μs)
1
2
3
4
7
8
1.58
2.44
3
2.94
4.5
5.06
Shift
2.42
3.2
3.38
5.26
5.14
Two Shifts
4.94
5.9
6.84
14.32
19.44
3.96
4.52
5.78
6.82
7.18
Pipeline
Spawned
Number of Parallel Computations
Pipeline
2.48
Rendezvous
Shift
4.46
6.42
5.86
10.86
11.74
MPI
Exchange As
Two Shifts
7.4
11.64
14.16
31.86
35.62
Exchange
6.94
11.22
13.3
18.78
20.16
30
Time Microseconds
AMD Exch
25
AMD Exch as 2 Shifts
AMD Shift
20
15
10
5
Stages (millions)
0
0
2
4
6
8
10
Overhead (latency) of AMD4 PC with 4 execution threads on MPI style Rendezvous
Messaging for Shift and Exchange implemented either as two shifts or as custom CCR
pattern
70
Time Microseconds
60
Intel Exch
50
Intel Exch as 2 Shifts
Intel Shift
40
30
20
10
Stages (millions)
0
0
2
4
6
8
10
Overhead (latency) of Intel8b PC with 8 execution threads on MPI style Rendezvous
Messaging for Shift and Exchange implemented either as two shifts or as custom
CCR pattern
1.6
Scaled
Intel 8b Vista C# CCR 1 Cluster
1.5
10,000
Runtime
1.4
500,000
1.3
Divide runtime
by
Grain Size n
. # Clusters K
1.2
50,000
Datapoints
per thread
1.1
1
a)
1
2
3
4
5
6
Number of Threads (one per core)
7
8
1
Scaled
Runtime
Intel 8b Vista C# CCR 80 Clusters
50,000
10,000
0.95
500,000
0.9
Datapoints
per thread
0.85
0.8
b)
1
2
3
4
5
8 cores (threads)
and 1 cluster show
memory bandwidth
effect
6
Number of Threads (one per core)
7
8
80 clusters show
cache/memory
bandwidth effect
0.1
Std Dev Intel 8a XP C# CCR
Runtime 80 Clusters
0.075
500,000
10,000
0.05
50,000
0.025
Datapoints
per thread
0
b)
0
1
2
3
4
5
6
7
Number of Threads (one per core)
0.006
10,000
0.004
50,000
500,000
0.002
Datapoints
per thread
0
b)
1
2
3
4
5
6
Number of Threads (one per core)
7
8
synchronization
points
8
Std Dev Intel 8c Redhat C Locks
Runtime 80 Clusters
This is average of
standard
deviation of run
time of the 8
threads between
messaging
 Early implementations of our clustering algorithm showed large




fluctuations due to the cache line interference effect (false sharing)
We have one thread on each core each calculating a sum of same
complexity storing result in a common array A with different cores using
different array locations
Thread i stores sum in A(i) is separation 1 – no memory access
interference but cache line interference
Thread i stores sum in A(X*i) is separation X
Serious degradation if X < 8 (64 bytes) with Windows
 Note A is a double (8 bytes)
 Less interference effect with Linux – especially Red Hat
Machine
Intel8b
Intel8b
Intel8b
Intel8b
Intel8a
Intel8a
Intel8a
Intel8c
AMD4
AMD4
AMD4
AMD4
AMD4
AMD4
OS
Vista
Vista
Vista
Fedora
XP CCR
XP Locks
XP
Red Hat
WinSrvr
WinSrvr
WinSrvr
XP
XP
XP
Run
Time
Mean
C# CCR
C# Locks
C
C
C#
C#
C
C
C# CCR
C# Locks
C
C# CCR
C# Locks
C
8.03
13.0
13.4
1.50
10.6
16.6
16.9
0.441
8.58
8.72
5.65
8.05
8.21
6.10
Time µs versus Thread Array Separation (unit is 8 bytes)
1
4
8
1024
Std/
Mean
Std/
Mean Std/
Mean Std/
Mean
Mean
Mean
Mean
.029
3.04
.059
0.884 .0051
0.884 .0069
.0095 3.08
.0028
0.883 .0043
0.883 .0036
.0047 1.69
.0026
0.66 .029
0.659 .0057
.01
0.69
.21
0.307 .0045
0.307 .016
.033
4.16
.041
1.27 .051
1.43
.049
.016
4.31
.0067
1.27 .066
1.27
.054
.0016 2.27
.0042
0.946 .056
0.946 .058
.0035 0.423
.0031
0.423 .0030
0.423 .032
.0080 2.62
.081
0.839 .0031
0.838 .0031
.0036 2.42
0.01
0.836 .0016
0.836 .0013
.020
2.69
.0060
1.05 .0013
1.05
.0014
0.010 2.84
0.077
0.84
0.040
0.840 0.022
0.006 2.57
0.016
0.84
0.007
0.84
0.007
0.026 2.95
0.017
1.05
0.019
1.05
0.017
 Note measurements at a separation X of 8 and X=1024 (and values between 8 and 1024 not
shown) are essentially identical
 Measurements at 7 (not shown) are higher than that at 8 (except for Red Hat which shows
essentially no enhancement at X<8)
 As effects due to co-location of thread variables in a 64 byte cache line, align the array with
cache boundaries
This class of data mining does/will parallelize well on current/future multicore nodes
Several engineering issues for use in large applications
 How to take CCR in multicore node to cluster (MPI or cross-cluster CCR?)
 Need high performance linear algebra for C# (PLASMA from UTenn)
 Access linear algebra services in a different language?
 Need equivalent of Intel C Math Libraries for C# (vector arithmetic – level 1 BLAS)
 Service model to integrate modules
 Need access to a ~ 128 node Windows cluster
Future work is more applications; refine current algorithms such as DAGTM
New parallel algorithms
 Clustering with pairwise distances but no vector spaces
 Bourgain Random Projection for metric embedding
 MDS Dimensional Scaling with EM-like SMACOF and deterministic annealing
 Support use of Newton’s Method (Marquardt’s method) as EM alternative
 Later HMM and SVM