HiCOMB 2015 14th IEEE International Workshop on High Performance Computational Biology at IPDPS 2015 Hyderabad, India. This talk covers parallel data analytics for bioinformatics. Messages are Always run MDS. Gives insight into data and performance of machine learning Leads to a data browser as GIS gives for spatial data 3D better than 2D ~20D better than MSA? Clustering Observations Do you care about quality or are you just cutting up space into parts Deterministic Clustering always makes more robust Continuous clustering enables hierarchy Trimmed Clustering cuts off tails Distinct O(N) and O(N2) algorithms Use Conjugate Gradient

1. Visualizing and Clustering Life Science
Applications in Parallel
HiCOMB 2015 14th IEEE International Workshop on
High Performance Computational Biology at IPDPS 2015
Hyderabad, India
May 25 2015
Geoffrey Fox
gcf@indiana.edu
http://www.infomall.org
School of Informatics and Computing
Digital Science Center
Indiana University Bloomington
5/25/2015 1

2. Deterministic Annealing
Algorithms
25/25/2015

3. Some Motivation
• Big Data requires high performance – achieve with parallel
computing
• Big Data sometimes requires robust algorithms as more
opportunity to make mistakes
• Deterministic annealing (DA) is one of better approaches to
robust optimization and broadly applicable
– Started as “Elastic Net” by Durbin for Travelling Salesman
Problem TSP
– Tends to remove local optima
– Addresses overfitting
– Much Faster than simulated annealing
• Physics systems find true lowest energy state if
you anneal i.e. you equilibrate at each
temperature as you cool
• Uses mean field approximation, which is also
used in “Variational Bayes” and “Variational
inference”5/25/2015 3

4. (Deterministic) Annealing
• Find minimum at high temperature when trivial
• Small change avoiding local minima as lower temperature
• Typically gets better answers than standard libraries- R and Mahout
• And can be parallelized and put on GPU’s etc.
45/25/2015

5. General Features of DA
• In many problems, decreasing temperature is classic
multiscale – finer resolution (√T is “just” distance scale)
• In clustering √T is distance in space of points (and
centroids), for MDS scale in mapped Euclidean space
• T = ∞, all points are in same place – the center of universe
• For MDS all Euclidean points are at center and distances
are zero. For clustering, there is one cluster
• As Temperature lowered there are phase transitions in
clustering cases where clusters split
– Algorithm determines whether split needed as second derivative
matrix singular
• Note DA has similar features to hierarchical methods and
you do not have to specify a number of clusters; you need
to specify a final distance scale 55/25/2015

6. Math of Deterministic Annealing
• H() is objective function to be minimized as a function of
parameters  (as in Stress formula given earlier for MDS)
• Gibbs Distribution at Temperature T
P() = exp( - H()/T) /  d exp( - H()/T)
• Or P() = exp( - H()/T + F/T )
• Use the Free Energy combining Objective Function and Entropy
F = < H - T S(P) > =  d {P()H+ T P() lnP()}
• Simulated annealing performs these integrals by Monte Carlo
• Deterministic annealing corresponds to doing integrals analytically
(by mean field approximation) and is much much faster
• Need to introduce a modified Hamiltonian for some cases so that
integrals are tractable. Introduce extra parameters to be varied so
that modified Hamiltonian matches original
• In each case temperature is lowered slowly – say by a factor 0.95 to
0.9999 at each iteration
5/25/2015 6

7. Some Uses of Deterministic Annealing DA
• Clustering improved K-means
– Vectors: Rose (Gurewitz and Fox 1990 – 486 citations encouraged me to revisit)
– Clusters with fixed sizes and no tails (Proteomics team at Broad)
– No Vectors: Hofmann and Buhmann (Just use pairwise distances)
• Dimension Reduction for visualization and analysis
– Vectors: GTM Generative Topographic Mapping
– No vectors SMACOF: Multidimensional Scaling) MDS (Just use pairwise
distances)
• Can apply to HMM & general mixture models (less study)
– Gaussian Mixture Models
– Probabilistic Latent Semantic Analysis with Deterministic Annealing DA-PLSA
as alternative to Latent Dirichlet Allocation for finding “hidden factors”
• Have scalable parallel versions of much of above – mainly Java
• Many clustering methods – not clear what is best although DA pretty good and
improves K-means at increased computing cost which is not always useful
• DA clearly improves MDS which is ~only reliable” dimension reduction?
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8. Clusters v. Regions
• In Lymphocytes clusters are distinct; DA useful
• In Pathology, clusters divide space into regions and
sophisticated methods like deterministic annealing are
probably unnecessary
8
Pathology 54D
Lymphocytes 4D
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9. Some Problems we are working on
• Analysis of Mass Spectrometry data to find peptides by
clustering peaks (Broad Institute/Hyderabad)
– ~0.5 million points in 2 dimensions (one collection) -- ~ 50,000
clusters summed over charges
• Metagenomics – 0.5 million (increasing rapidly) points NOT
in a vector space – hundreds of clusters per sample
– Apply MDS to Phylogenetic trees
• Pathology Images >50 Dimensions
• Social image analysis is in a highish dimension vector space
– 10-50 million images; 1000 features per image; million clusters
• Finding communities from network graphs coming from
Social media contacts etc.
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10. 5/25/2015 10
MDS and
Clustering on
~60K EMR

11. 5/25/2015 11
Colored by Sample – not clustered.
Distances from vectors in word space. This 128 4mers for ~200K sequences

12. Examples of current DA
algorithms: LCMS
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13. Background on LC-MS
• Remarks of collaborators – Broad Institute/Hyderabad
• Abundance of peaks in “label-free” LC-MS enables large-scale comparison of
peptides among groups of samples.
• In fact when a group of samples in a cohort is analyzed together, not only is it
possible to “align” robustly or cluster the corresponding peaks across samples,
but it is also possible to search for patterns or fingerprints of disease states
which may not be detectable in individual samples.
• This property of the data lends itself naturally to big data analytics for
biomarker discovery and is especially useful for population-level studies with
large cohorts, as in the case of infectious diseases and epidemics.
• With increasingly large-scale studies, the need for fast yet precise cohort-wide
clustering of large numbers of peaks assumes technical importance.
• In particular, a scalable parallel implementation of a cohort-wide peak
clustering algorithm for LC-MS-based proteomic data can prove to be a critically
important tool in clinical pipelines for responding to global epidemics of
infectious diseases like tuberculosis, influenza, etc.
135/25/2015

14. Proteomics 2D DA Clustering T= 25000
with 60 Clusters (will be 30,000 at T=0.025)
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15. The brownish triangles are “sponge” (soaks up trash) peaks outside
any cluster.
The colored hexagons are peaks inside clusters with the white
hexagons being determined cluster center
15
Fragment of 30,000 Clusters
241605 Points
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16. Continuous Clustering
• This is a very useful subtlety introduced by Ken Rose but not widely known
although it greatly improves algorithm
• Take a cluster k to be split into 2 with centers Y(k)A and Y(k)B with initial
values Y(k)A = Y(k)B at original center Y(k)
• Then typically if you make this change
and perturb the Y(k)A and Y(k)B, they will
return to starting position
as F at stable minimum (positive eigenvalue)
• But instability (the negative eigenvalue) can develop and one finds
• Implement by adding arbitrary number p(k) of centers for each cluster
Zi = k=1
K p(k) exp(-i(k)/T) and M step gives p(k) = C(k)/N
• Halve p(k) at splits; can’t split easily in standard case p(k) = 1
• Show weighting in sums like Zi now equipoint not equicluster as p(k)
proportional to points C(k) in cluster 16
Free Energy F
Y(k)A and Y(k)B
Y(k)A + Y(k)B
Free Energy F Free Energy F
Y(k)A - Y(k)B
5/25/2015

17. Trimmed Clustering
• Clustering with position-specific constraints on variance: Applying
redescending M-estimators to label-free LC-MS data analysis (Rudolf
Frühwirth, D R Mani and Saumyadipta Pyne) BMC
Bioinformatics 2011, 12:358
• HTCC = k=0
K i=1
N Mi(k) f(i,k)
– f(i,k) = (X(i) - Y(k))2/2(k)2 k > 0
– f(i,0) = c2 / 2 k = 0
• The 0’th cluster captures (at zero temperature) all points outside
clusters (background)
• Clusters are trimmed
(X(i) - Y(k))2/2(k)2 < c2 / 2
• Relevant when well
defined errors
T ~ 0
T = 1
T = 5
Distance from
cluster center
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18. 0
10000
20000
30000
40000
50000
60000
1.00E-031.00E-021.00E-011.00E+001.00E+011.00E+021.00E+031.00E+041.00E+051.00E+06
ClusterCount
Temperature
DAVS(2) DA2D
Start Sponge DAVS(2)
Add Close Cluster Check
Sponge Reaches final value
Cluster Count v. Temperature for 2 Runs
• All start with one cluster at far left
• T=1 special as measurement errors divided out
• DA2D counts clusters with 1 member as clusters. DAVS(2) does not5/25/2015 18

19. Simple Parallelism as in k-means
• Decompose points i over processors
• Equations either pleasingly parallel “maps” over i
• Or “All-Reductions” summing over i for each cluster
• Parallel Algorithm:
– Each process holds all clusters and calculates
contributions to clusters from points in node
– e.g. Y(k) = i=1
N <Mi(k)> Xi / C(k)
• Runs well in MPI or MapReduce
–See all the MapReduce k-means papers
195/25/2015

20. Better Parallelism
• The previous model is correct at start but each point does
not really contribute to each cluster as damped
exponentially by exp( - (Xi- Y(k))2 /T )
• For Proteomics problem, on average only 6.45 clusters
needed per point if require (Xi- Y(k))2 /T ≤ ~40 (as exp(-40)
small)
• So only need to keep nearby clusters for each point
• As average number of Clusters ~ 20,000, this gives a factor
of ~3000 improvement
• Further communication is no longer all global; it has
nearest neighbor components and calculated by
parallelism over clusters which can be done in parallel if
separated
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21. 21
Speedups for several runs on Tempest from 8-way through 384 way MPI parallelism with
one thread per process. We look at different choices for MPI processes which are either
inside nodes or on separate nodes
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22. 22
Parallelism within a Single Node of Madrid
Cluster. A set of runs on 241605 peak data
with a single node with 16 cores with either
threads or MPI giving parallelism.
Parallelism is either number of threads or
number of MPI processes.
Parallelism (#threads or #processes)
5/25/2015

23. METAGENOMICS -- SEQUENCE
CLUSTERING
Non-metric Spaces
O(N2) Algorithms – Illustrate Phase Transitions
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24. 24
• Start at T= “” with 1
Cluster
• Decrease T, Clusters
emerge at instabilities
5/25/2015

25. 255/25/2015

26. 265/25/2015

27. 446K sequences
~100 clusters
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28. METAGENOMICS -- SEQUENCE
CLUSTERING
Non-metric Spaces
O(N2) Algorithms – Compare Other Methods
5/25/2015 28

29. “Divergent” Data
Sample
23 True Clusters
29
CDhit
UClust
Divergent Data Set UClust (Cuts 0.65 to 0.95)
DAPWC 0.65 0.75 0.85 0.95
Total # of clusters 23 4 10 36 91
Total # of clusters uniquely identified 23 0 0 13 16
(i.e. one original cluster goes to 1 uclust cluster )
Total # of shared clusters with significant sharing 0 4 10 5 0
(one uclust cluster goes to > 1 real cluster)
Total # of uclust clusters that are just part of a real cluster 0 4 10 17(11) 72(62)
(numbers in brackets only have one member)
Total # of real clusters that are 1 uclust cluster 0 14 9 5 0
but uclust cluster is spread over multiple real clusters
Total # of real clusters that have 0 9 14 5 7
significant contribution from > 1 uclust cluster
DA-PWC
5/25/2015

30. PROTEOMICS
No clear clusters
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31. Protein Universe Browser for COG Sequences with a
few illustrative biologically identified clusters
315/25/2015

32. Heatmap of biology distance (Needleman-
Wunsch) vs 3D Euclidean Distances
32
If d a distance, so is f(d) for any monotonic f. Optimize choice of f
5/25/2015

33. O(N2) ALGORITHMS?
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34. Algorithm Challenges
• See NRC Massive Data Analysis report
• O(N) algorithms for O(N2) problems
• Parallelizing Stochastic Gradient Descent
• Streaming data algorithms – balance and interplay between
batch methods (most time consuming) and interpolative
streaming methods
• Graph algorithms – need shared memory?
• Machine Learning Community uses parameter servers;
Parallel Computing (MPI) would not recommend this?
– Is classic distributed model for “parameter service” better?
• Apply best of parallel computing – communication and load
balancing – to Giraph/Hadoop/Spark
• Are data analytics sparse?; many cases are full matrices
• BTW Need Java Grande – Some C++ but Java most popular in
ABDS, with Python, Erlang, Go, Scala (compiles to JVM) …..5/25/2015 34

35. O(N2) interactions between
green and purple clusters
should be able to represent by
centroids as in Barnes-Hut.
Hard as no Gauss theorem; no
multipole expansion and points
really in 1000 dimension space
as clustered before 3D
projection
O(N2) green-green and purple-
purple interactions have value
but green-purple are “wasted”
“clean” sample of 446K
5/25/2015 35

36. 36
Use Barnes Hut OctTree,
originally developed to
make O(N2) astrophysics
O(NlogN), to give similar
speedups in machine
learning
5/25/2015

37. 37
OctTree for 100K
sample of Fungi
We use OctTree for logarithmic
interpolation (streaming data)
5/25/2015

38. Fungi Analysis
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39. Fungi Analysis
• Multiple Species from multiple places
• Several sources of sequences starting with 446K and
eventually boiled down to ~10K curated sequences with 61
species
• Original sample – clustering and MDS
• Final sample – MDS and other clustering methods
• Note MSA and SWG gives similar results
• Some species are clearly split
• Some species are diffuse; others compact making a fixed
distance cut unreliable
– Easy for humans!
• MDS very clear on structure and clustering artifacts
• Why not do “high-value” clustering as interactive iteration
driven by MDS?
5/25/2015 39

40. Fungi -- 4 Classic Clustering Methods
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41. 5/25/2015 41

42. 5/25/2015 42

43. 5/25/2015 43
Same Species

44. 5/25/2015 44
Same Species Different Locations

45. Parallel Data Mining
5/25/2015 45

46. Parallel Data Analytics
• Streaming algorithms have interesting differences but
• “Batch” Data analytics is “just classic parallel computing” with
usual features such as SPMD and BSP
• Expect similar systematics to simulations where
• Static Regular problems are straightforward but
• Dynamic Irregular Problems are technically hard and high level
approaches fail (see High Performance Fortran HPF)
– Regular meshes worked well but
– Adaptive dynamic meshes did not although “real people with MPI”
could parallelize
• However using libraries is successful at either
– Lowest: communication level
– Higher: “core analytics” level
• Data analytics does not yet have “good regular parallel libraries”
– Graph analytics has most attention
5/25/2015 46

47. Remarks on Parallelism I
• Maximum Likelihood or 2 both lead to objective functions like
• Minimize sum items=1
N (Positive nonlinear function of
unknown parameters for item i)
• Typically decompose items i and parallelize over both i and
parameters to be determined
• Solve iteratively with (clever) first or second order
approximation to shift in objective function
– Sometimes steepest descent direction; sometimes Newton
– Have classic Expectation Maximization structure
– Steepest descent shift is sum over shift calculated from each point
• Classic method – take all (millions) of items in data set and
move full distance
– Stochastic Gradient Descent SGD – take randomly a few hundred of
items in data set and calculate shifts over these and move a tiny
distance
– SGD cannot parallelize over items 475/25/2015

48. Remarks on Parallelism II
• Need to cover non vector semimetric and vector spaces for
clustering and dimension reduction (N points in space)
• Semimetric spaces just have pairwise distances defined between
points in space (i, j)
• MDS Minimizes Stress and illustrates this
(X) = i<j=1
N weight(i,j) ((i, j) - d(Xi , Xj))2
• Vector spaces have Euclidean distance and scalar products
– Algorithms can be O(N) and these are best for clustering but for MDS O(N)
methods may not be best as obvious objective function O(N2)
– Important new algorithms needed to define O(N) versions of current O(N2) –
“must” work intuitively and shown in principle
• Note matrix solvers often use conjugate gradient – converges in 5-
100 iterations – a big gain for matrix with a million rows. This
removes factor of N in time complexity
• Ratio of #clusters to #points important; new clustering ideas if ratio
>~ 0.1
485/25/2015

49. Problem Structure
• Note learning networks have huge number of parameters (11 billion
in Stanford work) so that inconceivable to look at second derivative
• Clustering and MDS have lots of parameters but can be practical to
look at second derivative and use Newton’s method to minimize
• Parameters are determined in distributed fashion but are typically
needed globally
– MPI use broadcast and “AllCollectives” implying Map-Collective is a useful
programming model
– AI community: use parameter server and access as needed. Non-optimal?
495/25/2015
(1) Map Only
(4) Point to Point or
Map-Communication
(3) Iterative Map Reduce or
Map-Collective
(2) Classic
MapReduce
Input
map
reduce
Input
map
reduce
Iterations
Input
Output
map
Local
Graph
(5) Map-Streaming
maps brokers
Events
(6) Shared memory
Map Communicates
Map & Communicate
Shared Memory

50. MDS in more detail
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51. WDA-SMACOF “Best” MDS
• Semimetric spaces just have pairwise distances defined between
points in space (i, j)
• MDS Minimizes Stress with pairwise distances (i, j)
(X) = i<j=1
N weight(i,j) ((i, j) - d(Xi , Xj))2
• SMACOF clever Expectation Maximization method choses good
steepest descent
• Improved by Deterministic Annealing reducing distance scale; DA
does not impact compute time much and gives DA-SMACOF
• Classic SMACOF is O(N2) for uniform weight and O(N3) for non trivial
weights but get nonuniform weight from
– The preferred Sammon method weight(i,j) = 1/(i, j) or
– Missing distances put in as weight(i,j) = 0
• Use conjugate gradient – converges in 5-100 iterations – a big gain
for matrix with a million rows. This removes factor of N in time
complexity and gives WDA-SMACOF 515/25/2015

52. Timing of WDA SMACOF
• 20k to 100k AM Fungal sequences on 600 cores
5/25/2015 52
1
10
100
1000
10000
100000
20k 40k 60k 80k 100k
Seconds
Data Size
Time Cost Comparison between WDA-
SMACOF with Equal Weights and Sammon's
Mapping
Equal Weights Sammon's Mapping

53. WDA-SMACOF Timing
• Input Data: 100k to 400k AM Fungal sequences
• Environment: 32 nodes (1024 cores) to 128 nodes (4096 cores) on
BigRed2.
• Using Harp plug in for Hadoop (MPI Performance)
0
500
1000
1500
2000
2500
3000
3500
4000
100k 200k 300k 400k
Seconds
Data Size
Time Cost of WDA-SMACOF over
Increasing Data Size
512 1024 2048 4096
0
0.2
0.4
0.6
0.8
1
1.2
512 1024 2048 4096
ParallelEfficiency
Number of Processors
Parallel Efficiency of WDA-SMACOF over
Increasing Number of Processors
WDA-SMACOF (Harp)
5/25/2015 53

54. Spherical Phylogram
• Take a set of sequences mapped to nD with MDS (WDA-
SMACOF) (n=3 or ~20)
– N=20 captures ~all features of dataset?
• Consider a phylogenetic tree and use neighbor joining
formulae to calculate distances of nodes to sequences (or
later other nodes) starting at bottom of tree
• Do a new MDS fixing mapping of sequences noting that
sequences + nodes have defined distances
• Use RAxML or Neighbor Joining (N=20?) to find tree
• Random note: do not need Multiple Sequence Alignment;
pairwise tools are easier to use and give reliably good results
5/25/2015 54

55. RAxML result visualized in FigTree.
Spherical Phylogram visualized in PlotViz
for MSA or SWG distances
Spherical Phylograms
MSA
SWG
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56. Quality of 3D Phylogenetic Tree
• EM-SMACOF is basic SMACOF
• LMA was previous best method using Levenberg-Marquardt
nonlinear 2 solver
• WDA-SMACOF finds best result
• 3 different distance measures
Sum of branch lengths of the Spherical Phylogram generated in 3D space
on two datasets
0
5
10
15
20
25
30
MSA SWG NW
SumofBranches
Sum of Branches on 599nts Data
WDA-SMACOF LMA EM-SMACOF
0
5
10
15
20
25
MSA SWG NW
SumofBranches
Sum of Branches on 999nts Data
WDA-SMACOF LMA EM-SMACOF
5/25/2015 56

57. Summary
• Always run MDS. Gives insight into data and performance
of machine learning
– Leads to a data browser as GIS gives for spatial data
– 3D better than 2D
– ~20D better than MSA?
• Clustering Observations
– Do you care about quality or are you just cutting up
space into parts
– Deterministic Clustering always makes more robust
– Continuous clustering enables hierarchy
– Trimmed Clustering cuts off tails
– Distinct O(N) and O(N2) algorithms
• Use Conjugate Gradient 575/25/2015

58. Java Grande
5/25/2015 58

59. Java Grande
• We once tried to encourage use of Java in HPC with Java Grande
Forum but Fortran, C and C++ remain central HPC languages.
– Not helped by .com and Sun collapse in 2000-2005
• The pure Java CartaBlanca, a 2005 R&D100 award-winning
project, was an early successful example of HPC use of Java in a
simulation tool for non-linear physics on unstructured grids.
• Of course Java is a major language in ABDS and as data analysis
and simulation are naturally linked, should consider broader use
of Java
• Using Habanero Java (from Rice University) for Threads and
mpiJava or FastMPJ for MPI, gathering collection of high
performance parallel Java analytics
– Converted from C# and sequential Java faster than sequential C#
• So will have either Hadoop+Harp or classic Threads/MPI
versions in Java Grande version of Mahout5/25/2015 59

60. Performance of MPI Kernel Operations
1
100
10000
0B
2B
8B
32B
128B
512B
2KB
8KB
32KB
128KB
512KB
Averagetime(us)
Message size (bytes)
MPI.NET C# in Tempest
FastMPJ Java in FG
OMPI-nightly Java FG
OMPI-trunk Java FG
OMPI-trunk C FG
Performance of MPI send and receive operations
5
5000
4B
16B
64B
256B
1KB
4KB
16KB
64KB
256KB
1MB
4MB
Averagetime(us)
Message size (bytes)
MPI.NET C# in Tempest
FastMPJ Java in FG
OMPI-nightly Java FG
OMPI-trunk Java FG
OMPI-trunk C FG
Performance of MPI allreduce operation
1
100
10000
1000000
4B
16B
64B
256B
1KB
4KB
16KB
64KB
256KB
1MB
4MB
AverageTime(us)
Message Size (bytes)
OMPI-trunk C Madrid
OMPI-trunk Java Madrid
OMPI-trunk C FG
OMPI-trunk Java FG
1
10
100
1000
10000
0B
2B
8B
32B
128B
512B
2KB
8KB
32KB
128KB
512KB
AverageTime(us)
Message Size (bytes)
OMPI-trunk C Madrid
OMPI-trunk Java Madrid
OMPI-trunk C FG
OMPI-trunk Java FG
Performance of MPI send and receive on
Infiniband and Ethernet
Performance of MPI allreduce on Infiniband
and Ethernet
Pure Java as
in FastMPJ
slower than
Java
interfacing
to C version
of MPI
5/25/2015 60

61. Java Grande and C# on 40K point DAPWC Clustering
Very sensitive to threads v MPI
64 Way parallel
128 Way parallel
256 Way
parallel
TXP
Nodes
Total
C#
Java
C# Hardware 0.7 performance Java Hardware
5/25/2015 61

62. Java and C# on 12.6K point DAPWC Clustering
Java
C##Threads x #Processes per node
# Nodes
Total Parallelism
Time hours
1x1 2x21x2 1x42x1 1x84x1 2x4 4x2 8x1
#Threads x #Processes per node
C# Hardware 0.7 performance Java Hardware
5/25/2015 62

63. Data Analytics in SPIDAL
5/25/2015 63

64. Analytics and the DIKW Pipeline
• Data goes through a pipeline
Raw data  Data  Information  Knowledge  Wisdom 
Decisions
• Each link enabled by a filter which is “business logic” or “analytics”
• We are interested in filters that involve “sophisticated analytics”
which require non trivial parallel algorithms
– Improve state of art in both algorithm quality and (parallel) performance
• Design and Build SPIDAL (Scalable Parallel Interoperable Data
Analytics Library)
More Analytics
KnowledgeInformation
Analytics
InformationData
5/25/2015 64

65. Strategy to Build SPIDAL
• Analyze Big Data applications to identify analytics
needed and generate benchmark applications
• Analyze existing analytics libraries (in practice limit to
some application domains) – catalog library members
available and performance
– Mahout low performance, R largely sequential and missing
key algorithms, MLlib just starting
• Identify big data computer architectures
• Identify software model to allow interoperability and
performance
• Design or identify new or existing algorithm including
parallel implementation
• Collaborate application scientists, computer systems
and statistics/algorithms communities5/25/2015 65

66. Machine Learning in Network Science, Imaging in Computer
Vision, Pathology, Polar Science, Biomolecular Simulations
66
Algorithm Applications Features Status Parallelism
Graph Analytics
Community detection Social networks, webgraph
Graph
.
P-DM GML-GrC
Subgraph/motif finding Webgraph, biological/social networks P-DM GML-GrB
Finding diameter Social networks, webgraph P-DM GML-GrB
Clustering coefficient Social networks P-DM GML-GrC
Page rank Webgraph P-DM GML-GrC
Maximal cliques Social networks, webgraph P-DM GML-GrB
Connected component Social networks, webgraph P-DM GML-GrB
Betweenness centrality Social networks Graph, Non-metric,
static
P-Shm
GML-GRA
Shortest path Social networks, webgraph P-Shm
Spatial Queries and Analytics
Spatial relationship based
queries
GIS/social networks/pathology
informatics Geometric
P-DM PP
Distance based queries P-DM PP
Spatial clustering Seq GML
Spatial modeling Seq PP
GML Global (parallel) ML
GrA Static GrB Runtime partitioning
5/25/2015

67. Some specialized data analytics in
SPIDAL
• aa
67
Algorithm Applications Features Status Parallelism
Core Image Processing
Image preprocessing
Computer vision/pathology
informatics
Metric Space Point
Sets, Neighborhood
sets & Image
features
P-DM PP
Object detection &
segmentation P-DM PP
Image/object feature
computation P-DM PP
3D image registration Seq PP
Object matching
Geometric
Todo PP
3D feature extraction Todo PP
Deep Learning
Learning Network,
Stochastic Gradient
Descent
Image Understanding,
Language Translation, Voice
Recognition, Car driving
Connections in
artificial neural net P-DM GML
PP Pleasingly Parallel (Local ML)
Seq Sequential Available
GRA Good distributed algorithm needed
Todo No prototype Available
P-DM Distributed memory Available
P-Shm Shared memory Available5/25/2015

68. Some Core Machine Learning Building Blocks
68
Algorithm Applications Features Status //ism
DA Vector Clustering Accurate Clusters Vectors P-DM GML
DA Non metric Clustering Accurate Clusters, Biology, Web Non metric, O(N2) P-DM GML
Kmeans; Basic, Fuzzy and Elkan Fast Clustering Vectors P-DM GML
Levenberg-Marquardt
Optimization
Non-linear Gauss-Newton, use
in MDS Least Squares P-DM GML
SMACOF Dimension Reduction DA- MDS with general weights Least Squares,
O(N2) P-DM GML
Vector Dimension Reduction DA-GTM and Others Vectors P-DM GML
TFIDF Search Find nearest neighbors in
document corpus
Bag of “words”
(image features)
P-DM PP
All-pairs similarity search
Find pairs of documents with
TFIDF distance below a
threshold
Todo GML
Support Vector Machine SVM Learn and Classify Vectors Seq GML
Random Forest Learn and Classify Vectors P-DM PP
Gibbs sampling (MCMC) Solve global inference problems Graph Todo GML
Latent Dirichlet Allocation LDA
with Gibbs sampling or Var.
Bayes
Topic models (Latent factors) Bag of “words” P-DM GML
Singular Value Decomposition
SVD Dimension Reduction and PCA Vectors Seq GML
Hidden Markov Models (HMM) Global inference on sequence
models Vectors Seq PP &
GML
5/25/2015

69. Some Futures
• Always run MDS. Gives insight into data
– Leads to a data browser as GIS gives for spatial data
• Claim is algorithm change gave as much performance
increase as hardware change in simulations. Will this
happen in analytics?
– Today is like parallel computing 30 years ago with regular meshs.
We will learn how to adapt methods automatically to give
“multigrid” and “fast multipole” like algorithms
• Need to start developing the libraries that support Big Data
– Understand architectures issues
– Have coupled batch and streaming versions
– Develop much better algorithms
• Please join SPIDAL (Scalable Parallel Interoperable Data
Analytics Library) community 695/25/2015