5th Multicore World 15-17 February 2016 – Shed 6, Wellington, New Zealand http://openparallel.com/multicore-world-2016/ We start by dividing applications into data plus model components and classifying each component (whether from Big Data or Big Simulations) in the same way. These leads to 64 properties divided into 4 views, which are Problem Architecture (Macro pattern); Execution Features (Micro patterns); Data Source and Style; and finally the Processing (runtime) View. We discuss convergence software built around HPC-ABDS (High Performance Computing enhanced Apache Big Data Stack) http://hpc-abds.org/kaleidoscope/ and show how one can merge Big Data and HPC (Big Simulation) concepts into a single stack. We give examples of data analytics running on HPC systems including details on persuading Java to run fast. Some details can be found at http://dsc.soic.indiana.edu/publications/HPCBigDataConvergence.pdf

1. Big Data HPC Convergence
5th Multicore World
15-17 February 2016 – Shed 6, Wellington, New Zealand
http://openparallel.com/multicore-world-2016/
1
Geoffrey Fox
February 16, 2016
gcf@indiana.edu
http://www.dsc.soic.indiana.edu/, http://spidal.org/ http://hpc-abds.org/kaleidoscope/
Department of Intelligent Systems Engineering
School of Informatics and Computing, Digital Science Center
Indiana University Bloomington
02/16/2016

2. Abstract
• Two major trends in computing systems are the growth in high performance computing
(HPC) with an international exascale initiative, and the big data phenomenon with an
accompanying cloud infrastructure of well publicized dramatic and increasing size and
sophistication.
• In studying and linking these trends one needs to consider multiple aspects: hardware,
software, applications/algorithms and even broader issues like business model and
education.
• In this talk we study in detail a convergence approach for software and applications /
algorithms and show what hardware architectures it suggests.
• We start by dividing applications into data plus model components and classifying each
component (whether from Big Data or Big Simulations) in the same way. These leads to 64
properties divided into 4 views, which are Problem Architecture (Macro pattern); Execution
Features (Micro patterns); Data Source and Style; and finally the Processing (runtime) View.
• We discuss convergence software built around HPC-ABDS (High Performance Computing
enhanced Apache Big Data Stack) http://hpc-abds.org/kaleidoscope/ and show how one can
merge Big Data and HPC (Big Simulation) concepts into a single stack.
• We give examples of data analytics running on HPC systems including details on
persuading Java to run fast.
• Some details can be found at
http://dsc.soic.indiana.edu/publications/HPCBigDataConvergence.pdf
202/16/2016

3. NIST Big Data Initiative
Led by Chaitin Baru, Bob Marcus, Wo Chang
And
Big Data Application Analysis
02/16/2016 3

4. NBD-PWG (NIST Big Data Public Working Group)
Subgroups & Co-Chairs
• There were 5 Subgroups
– Note mainly industry
• Requirements and Use Cases Sub Group
– Geoffrey Fox, Indiana U.; Joe Paiva, VA; Tsegereda Beyene, Cisco
• Definitions and Taxonomies SG
– Nancy Grady, SAIC; Natasha Balac, SDSC; Eugene Luster, R2AD
• Reference Architecture Sub Group
– Orit Levin, Microsoft; James Ketner, AT&T; Don Krapohl, Augmented
Intelligence
• Security and Privacy Sub Group
– Arnab Roy, CSA/Fujitsu Nancy Landreville, U. MD Akhil Manchanda, GE
• Technology Roadmap Sub Group
– Carl Buffington, Vistronix; Dan McClary, Oracle; David Boyd, Data
Tactics
• See http://bigdatawg.nist.gov/usecases.php
• and http://bigdatawg.nist.gov/V1_output_docs.php
402/16/2016

5. Use Case Template
• 26 fields completed for 51 apps
• Government Operation: 4
• Commercial: 8
• Defense: 3
• Healthcare and Life Sciences:
10
• Deep Learning and Social
Media: 6
• The Ecosystem for Research:
4
• Astronomy and Physics: 5
• Earth, Environmental and
Polar Science: 10
• Energy: 1
• Now an online form
502/16/2016

6. 602/16/2016
http://hpc-abds.org/kaleidoscope/survey/
Online Use Case
Form

7. 702/16/2016
http://hpc-
abds.org/kaleidoscope/survey/

8. 51 Detailed Use Cases: Contributed July-September 2013
Covers goals, data features such as 3 V’s, software, hardware
• http://bigdatawg.nist.gov/usecases.php
• https://bigdatacoursespring2014.appspot.com/course (Section 5)
• Government Operation(4): National Archives and Records Administration, Census Bureau
• Commercial(8): Finance in Cloud, Cloud Backup, Mendeley (Citations), Netflix, Web Search,
Digital Materials, Cargo shipping (as in UPS)
• Defense(3): Sensors, Image surveillance, Situation Assessment
• Healthcare and Life Sciences(10): Medical records, Graph and Probabilistic analysis,
Pathology, Bioimaging, Genomics, Epidemiology, People Activity models, Biodiversity
• Deep Learning and Social Media(6): Driving Car, Geolocate images/cameras, Twitter, Crowd
Sourcing, Network Science, NIST benchmark datasets
• The Ecosystem for Research(4): Metadata, Collaboration, Language Translation, Light source
experiments
• Astronomy and Physics(5): Sky Surveys including comparison to simulation, Large Hadron
Collider at CERN, Belle Accelerator II in Japan
• Earth, Environmental and Polar Science(10): Radar Scattering in Atmosphere, Earthquake,
Ocean, Earth Observation, Ice sheet Radar scattering, Earth radar mapping, Climate simulation
datasets, Atmospheric turbulence identification, Subsurface Biogeochemistry (microbes to
watersheds), AmeriFlux and FLUXNET gas sensors
• Energy(1): Smart grid
8
26 Features for each use case
Biased to science
02/16/2016

9. Features and Examples
02/16/2016 9

10. 51 Use Cases: What is Parallelism Over?
• People: either the users (but see below) or subjects of application and often both
• Decision makers like researchers or doctors (users of application)
• Items such as Images, EMR, Sequences below; observations or contents of online
store
– Images or “Electronic Information nuggets”
– EMR: Electronic Medical Records (often similar to people parallelism)
– Protein or Gene Sequences;
– Material properties, Manufactured Object specifications, etc., in custom dataset
– Modelled entities like vehicles and people
• Sensors – Internet of Things
• Events such as detected anomalies in telescope or credit card data or atmosphere
• (Complex) Nodes in RDF Graph
• Simple nodes as in a learning network
• Tweets, Blogs, Documents, Web Pages, etc.
– And characters/words in them
• Files or data to be backed up, moved or assigned metadata
• Particles/cells/mesh points as in parallel simulations
10
02/16/2016

11. Features of 51 Use Cases I
• PP (26) “All” Pleasingly Parallel or Map Only
• MR (18) Classic MapReduce MR (add MRStat below for full count)
• MRStat (7) Simple version of MR where key computations are simple
reduction as found in statistical averages such as histograms and
averages
• MRIter (23) Iterative MapReduce or MPI (Spark, Twister)
• Graph (9) Complex graph data structure needed in analysis
• Fusion (11) Integrate diverse data to aid discovery/decision making;
could involve sophisticated algorithms or could just be a portal
• Streaming (41) Some data comes in incrementally and is processed
this way
• Classify (30) Classification: divide data into categories
• S/Q (12) Index, Search and Query
1102/16/2016

12. Features of 51 Use Cases II
• CF (4) Collaborative Filtering for recommender engines
• LML (36) Local Machine Learning (Independent for each parallel entity) –
application could have GML as well
• GML (23) Global Machine Learning: Deep Learning, Clustering, LDA, PLSI,
MDS,
– Large Scale Optimizations as in Variational Bayes, MCMC, Lifted Belief
Propagation, Stochastic Gradient Descent, L-BFGS, Levenberg-Marquardt . Can
call EGO or Exascale Global Optimization with scalable parallel algorithm
• Workflow (51) Universal
• GIS (16) Geotagged data and often displayed in ESRI, Microsoft Virtual
Earth, Google Earth, GeoServer etc.
• HPC(5) Classic large-scale simulation of cosmos, materials, etc. generating
(visualization) data
• Agent (2) Simulations of models of data-defined macroscopic entities
represented as agents
1202/16/2016

13. Local and Global Machine Learning
• Many applications use LML or Local machine Learning where machine
learning (often from R) is run separately on every data item such as on every
image
• But others are GML Global Machine Learning where machine learning is a
single algorithm run over all data items (over all nodes in computer)
– maximum likelihood or 2 with a sum over the N data items –
documents, sequences, items to be sold, images etc. and often links
(point-pairs).
– Graph analytics is typically GML
• Covering clustering/community detection, mixture models, topic
determination, Multidimensional scaling, (Deep) Learning Networks
• PageRank is “just” parallel linear algebra
• Note many Mahout algorithms are sequential – partly as MapReduce
limited; partly because parallelism unclear
– MLLib (Spark based) better
• SVM and Hidden Markov Models do not use large scale parallelization in
practice?
1302/16/2016

14. 13 Image-based Use Cases
• 13-15 Military Sensor Data Analysis/ Intelligence PP, LML, GIS, MR
• 7:Pathology Imaging/ Digital Pathology: PP, LML, MR for search
becoming terabyte 3D images, Global Classification
• 18&35: Computational Bioimaging (Light Sources): PP, LML Also
materials
• 26: Large-scale Deep Learning: GML Stanford ran 10 million images and
11 billion parameters on a 64 GPU HPC; vision (drive car), speech, and
Natural Language Processing
• 27: Organizing large-scale, unstructured collections of photos: GML
Fit position and camera direction to assemble 3D photo ensemble
• 36: Catalina Real-Time Transient Synoptic Sky Survey (CRTS): PP,
LML followed by classification of events (GML)
• 43: Radar Data Analysis for CReSIS Remote Sensing of Ice Sheets: PP,
LML to identify glacier beds; GML for full ice-sheet
• 44: UAVSAR Data Processing, Data Product Delivery, and Data
Services: PP to find slippage from radar images
• 45, 46: Analysis of Simulation visualizations: PP LML ?GML find paths,
classify orbits, classify patterns that signal earthquakes, instabilities,
climate, turbulence
1402/16/2016

15. Internet of Things and Streaming Apps
• It is projected that there will be 24 (Mobile Industry Group) to 50 (Cisco)
billion devices on the Internet by 2020.
• The cloud natural controller of and resource provider for the Internet of
Things.
• Smart phones/watches, Wearable devices (Smart People), “Intelligent River”
“Smart Homes and Grid” and “Ubiquitous Cities”, Robotics.
• Majority of use cases are streaming – experimental science gathers data in
a stream – sometimes batched as in a field trip. Below is sample
• 10: Cargo Shipping Tracking as in UPS, Fedex PP GIS LML
• 13: Large Scale Geospatial Analysis and Visualization PP GIS LML
• 28: Truthy: Information diffusion research from Twitter Data PP MR for
Search, GML for community determination
• 39: Particle Physics: Analysis of LHC Large Hadron Collider Data:
Discovery of Higgs particle PP for event Processing, Global statistics
• 50: DOE-BER AmeriFlux and FLUXNET Networks PP GIS LML
• 51: Consumption forecasting in Smart Grids PP GIS LML
1502/16/2016

16. http://www.kpcb.com/internet-trends
IoT
100B
Devices
~2025

17. Big Data and Big Simulations
Patterns – the Convergence
Diamonds
02/16/2016 17

18. Big Data - Big Simulation (Exascale) Convergence
• Lets distinguish Data and Model (e.g. machine learning
analytics) in Big data problems
• Then almost always Data is large but Model varies
– e.g. LDA with many topics or deep learning has large model
– Clustering or Dimension reduction can be quite small
• Simulations can also be considered as Data and Model
– Model is solving particle dynamics or partial differential equations
– Data could be small when just boundary conditions or
– Data large with data assimilation (weather forecasting) or when data
visualizations produced by simulation
• Data often static between iterations (unless streaming), model
varies between iterations
1802/16/2016

19. Classifying Big Data and Big Simulation Applications
• “Benchmarks” “kernels” “algorithm” “mini-apps” can serve multiple
purposes
• Motivate hardware and software features
– e.g. collaborative filtering algorithm has feature of parallelizes well with
MapReduce and suggests using Hadoop on a cloud
– e.g. deep learning on images dominated by matrix operations; needs
CUDA&MPI and suggests HPC cluster
• Benchmark sets can be designed to cover key features of systems in terms
of features and sizes of “important” applications
• Take 51 uses cases  derive specific features; each use case has multiple
features
• Generalize and systematize with features termed “facets”
• 50 Facets (Big Data) or 64 Facets (Big Simulation and Data) divided
into 4 sets or views where each view has “similar” facets
– Allow one to study coverage of benchmark sets and architectures
• Discuss Data and Model together as built around problems which combine
them but we can get insight by separating and this allows better
understanding of Big Data - Big Simulation “convergence”
1902/16/2016

20. 7 Computational Giants of
NRC Massive Data Analysis Report
1) G1: Basic Statistics e.g. MRStat
2) G2: Generalized N-Body Problems
3) G3: Graph-Theoretic Computations
4) G4: Linear Algebraic Computations
5) G5: Optimizations e.g. Linear Programming
6) G6: Integration e.g. LDA and other GML
7) G7: Alignment Problems e.g. BLAST
2002/16/2016
http://www.nap.edu/catalog.php?record_id=18374 Big Data Models?

21. HPC (Simulation) Benchmark Classics
• Linpack or HPL: Parallel LU factorization
for solution of linear equations
• NPB version 1: Mainly classic HPC solver kernels
– MG: Multigrid
– CG: Conjugate Gradient
– FT: Fast Fourier Transform
– IS: Integer sort
– EP: Embarrassingly Parallel
– BT: Block Tridiagonal
– SP: Scalar Pentadiagonal
– LU: Lower-Upper symmetric Gauss Seidel
2102/16/2016
Simulation Models

22. 13 Berkeley Dwarfs
1) Dense Linear Algebra
2) Sparse Linear Algebra
3) Spectral Methods
4) N-Body Methods
5) Structured Grids
6) Unstructured Grids
7) MapReduce
8) Combinational Logic
9) Graph Traversal
10) Dynamic Programming
11) Backtrack and
Branch-and-Bound
12) Graphical Models
13) Finite State Machines
2202/16/2016
First 6 of these correspond to Colella’s
original. (Classic simulations)
Monte Carlo dropped.
N-body methods are a subset of
Particle in Colella.
Note a little inconsistent in that
MapReduce is a programming model
and spectral method is a numerical
method.
Need multiple facets!
Largely Models for Data or Simulation

23. 2302/16/2016
Pleasingly Parallel
Classic MapReduce
Map-Collective
Map Point-to-Point
Shared Memory
Single Program Multiple Data
Bulk Synchronous Parallel
Fusion
Dataflow
Agents
Workflow
Geospatial Information System
HPC Simulations
Internet of Things
Metadata/Provenance
Shared / Dedicated / Transient / Permanent
Archived/Batched/Streaming
HDFS/Lustre/GPFS
Files/Objects
Enterprise Data Model
SQL/NoSQL/NewSQL
PerformanceMetrics
FlopsperByte;MemoryI/O
ExecutionEnvironment;Corelibraries
Volume
Velocity
Variety
Veracity
CommunicationStructure
DataAbstraction
Metric=M/Non-Metric=N
=NN/=N
Regular=R/Irregular=I
Dynamic=D/Static=S
Visualization
GraphAlgorithms
LinearAlgebraKernels
Alignment
Streaming
OptimizationMethodology
Learning
Classification
Search/Query/Index
BaseStatistics
GlobalAnalytics
LocalAnalytics
Micro-benchmarks
Recommendations
Data Source and Style View
Execution View
Processing View
2
3
4
6
7
8
9
10
11
12
10
9
8
7
6
5
4
3
2
1
1 2 3 4 5 6 7 8 9 10 12 14
9 8 7 5 4 3 2 114 13 12 11 10 6
13
Map Streaming 5
4 Ogre
Views and
50 Facets
Iterative/Simple
11
1
Problem
Architecture
View

24. 2402/16/2016
Local(Analytics/Informatics/Simulations)
2
M
Data Source and Style View
Pleasingly Parallel
Classic MapReduce
Map-Collective
Map Point-to-Point
Shared Memory
Single Program Multiple Data
Bulk Synchronous Parallel
Fusion
Dataflow
Agents
Workflow
Geospatial Information System
HPC Simulations
Internet of Things
Metadata/Provenance
Shared / Dedicated / Transient / Permanent
Archived/Batched/Streaming – S1, S2, S3, S4, S5
HDFS/Lustre/GPFS
Files/Objects
Enterprise Data Model
SQL/NoSQL/NewSQL
1
M
Micro-benchmarks
Execution View
Processing View
1
2
3
4
6
7
8
9
10
11M
12
10D
9
8D
7D
6D
5D
4D
3D
2D
1D
Map Streaming 5
Convergence
Diamonds
Views and
Facets
Problem Architecture View
15
M
CoreLibraries
Visualization14
M GraphAlgorithms
13
M
LinearAlgebraKernels/Manysubclasses
12
M
Global(Analytics/Informatics/Simulations)
3
M
RecommenderEngine
5
M
4
M
BaseDataStatistics
10
M
StreamingDataAlgorithms
OptimizationMethodology
9
M
Learning
8
M
DataClassification
7
M
DataSearch/Query/Index
6
M
11
M
DataAlignment
Big Data Processing
Diamonds
MultiscaleMethod
17
M
16
M
IterativePDESolvers
22
M
Natureofmeshifused
EvolutionofDiscreteSystems
21
M
ParticlesandFields
20
M
N-bodyMethods
19
M
SpectralMethods
18
M
Simulation (Exascale)
Processing Diamonds
DataAbstraction
D
12
ModelAbstraction
M
12
DataMetric=M/Non-Metric=N
D
13
DataMetric=M/Non-Metric=N
M
13
=NN/=N
M
14
Regular=R/Irregular=IModel
M
10
Veracity
7
Iterative/Simple
M
11
CommunicationStructure
M
8
Dynamic=D/Static=S
D
9
Dynamic=D/Static=S
M
9
Regular=R/Irregular=IData
D
10
ModelVariety
M
6
DataVelocity
D
5
PerformanceMetrics
1
DataVariety
D
6
FlopsperByte/MemoryIO/Flopsperwatt
2
ExecutionEnvironment;Corelibraries
3
DataVolume
D
4
ModelSize
M
4
Simulations
Analytics
(Model for
Data)
Both
(All Model)
(Nearly all Data+Model)
(Nearly all Data)
(Mix of Data and Model)

25. Dwarfs and Ogres give
Convergence Diamonds
• Macropatterns or Problem Architecture View:
Unchanged from Ogres
• Execution View: Significant changes to separate Data
and Model and add characteristics of Simulation models
• Data Source and Style View: Same for Ogres and
Diamonds – present but less important for Simulations
compared to big data
• Processing View is a mix of Big Data Processing View
and Big Simulation Processing View and includes
some facets like “uses linear algebra” needed in both:
has specifics of key simulation kernels and in particular
includes NAS Parallel Benchmarks and Berkeley Dwarfs
2502/16/2016

26. Facets of the Convergence
Diamonds
Problem Architecture
Meta or Macro Aspects of Diamonds
Valid for Big Data or Big Simulations as describes Problem
which is Model-Data combination
02/16/2016 26

27. Problem Architecture View (Meta or MacroPatterns)
i. Pleasingly Parallel – as in BLAST, Protein docking, some (bio-)imagery including
Local Analytics or Machine Learning – ML or filtering pleasingly parallel, as in bio-
imagery, radar images (pleasingly parallel but sophisticated local analytics)
ii. Classic MapReduce: Search, Index and Query and Classification algorithms like
collaborative filtering (G1 for MRStat in Features, G7)
iii. Map-Collective: Iterative maps + communication dominated by “collective” operations
as in reduction, broadcast, gather, scatter. Common datamining pattern
iv. Map-Point to Point: Iterative maps + communication dominated by many small point to
point messages as in graph algorithms
v. Map-Streaming: Describes streaming, steering and assimilation problems
vi. Shared Memory: Some problems are asynchronous and are easier to parallelize on
shared rather than distributed memory – see some graph algorithms
vii. SPMD: Single Program Multiple Data, common parallel programming feature
viii. BSP or Bulk Synchronous Processing: well-defined compute-communication phases
ix. Fusion: Knowledge discovery often involves fusion of multiple methods.
x. Dataflow: Important application features often occurring in composite Ogres
xi. Use Agents: as in epidemiology (swarm approaches) This is Model only
xii. Workflow: All applications often involve orchestration (workflow) of multiple
components
2702/16/2016
Most (11 of total 12) are properties of Data+Model

28. Relation of Problem and Machine Architecture
• Problem is Model plus Data
• In my old papers (especially book Parallel Computing Works!), I discussed
computing as multiple complex systems mapped into each other
Problem  Numerical formulation  Software 
Hardware
• Each of these 4 systems has an architecture that can be described in
similar language
• One gets an easy programming model if architecture of problem matches
that of Software
• One gets good performance if architecture of hardware matches that of
software and problem
• So “MapReduce” can be used as architecture of software (programming
model) or “Numerical formulation of problem”
2802/16/2016

29. 6 Forms of
MapReduce
cover “all”
circumstances
Describes
- Problem (Model
reflecting data)
- Machine
- Software
Architecture
2902/16/2016

30. Data Analysis Problem Architectures
 1) Pleasingly Parallel PP or “map-only” in MapReduce
 BLAST Analysis; Local Machine Learning
 2A) Classic MapReduce MR, Map followed by reduction
 High Energy Physics (HEP) Histograms; Web search; Recommender Engines
 2B) Simple version of classic MapReduce MRStat
 Final reduction is just simple statistics
 3) Iterative MapReduce MRIter
 Expectation maximization Clustering Linear Algebra, PageRank
 4A) Map Point to Point Communication
 Classic MPI; PDE Solvers and Particle Dynamics; Graph processing Graph
 4B) GPU (Accelerator) enhanced 4A) – especially for deep learning
 5) Map + Streaming + some sort of Communication
 Images from Synchrotron sources; Telescopes; Internet of Things IoT
 Apache Storm is (Map + Dataflow) +Streaming
 Data assimilation is (Map + Point to Point Communication) + Streaming
 6) Shared memory allowing parallel threads which are tricky to program but
lower latency
 Difficult to parallelize asynchronous parallel Graph Algorithms
3002/16/2016

31. Diamond Facets
Execution Features View
Many similar Features for Big Data and
Simulations
02/16/2016 31

32. View for Micropatterns or Execution Features
i. Performance Metrics; property found by benchmarking Diamond
ii. Flops per byte; memory or I/O
iii. Execution Environment; Core libraries needed: matrix-matrix/vector algebra, conjugate
gradient, reduction, broadcast; Cloud, HPC etc.
iv. Volume: property of a Diamond instance: a) Data Volume and b) Model Size
v. Velocity: qualitative property of Diamond with value associated with instance. Only Data
vi. Variety: important property especially of composite Diamonds; Data and Model separately
vii. Veracity: important property of applications but not kernels;
viii. Model Communication Structure; Interconnect requirements; Is communication BSP,
Asynchronous, Pub-Sub, Collective, Point to Point?
ix. Is Data and/or Model (graph) static or dynamic?
x. Much Data and/or Models consist of a set of interconnected entities; is this regular as a set
of pixels or is it a complicated irregular graph?
xi. Are Models Iterative or not?
xii. Data Abstraction: key-value, pixel, graph(G3), vector, bags of words or items; Model can
have same or different abstractions e.g. mesh points, finite element, Convolutional Network
xiii. Are data points in metric or non-metric spaces? Data and Model separately?
xiv. Is Model algorithm O(N2) or O(N) (up to logs) for N points per iteration (G2)
3202/16/2016

33. Comparison of Data Analytics with Simulation I
• Simulations produce big data as visualization of results – they are data
source
– Or consume often smallish data to define a simulation problem
– HPC simulation in (weather) data assimilation is data + model
• Pleasingly parallel often important in both
• Both are often SPMD and BSP
• Non-iterative MapReduce is major big data paradigm
– not a common simulation paradigm except where “Reduce” summarizes
pleasingly parallel execution as in some Monte Carlos
• Big Data often has large collective communication
– Classic simulation has a lot of smallish point-to-point messages
– Motivates Map-Collective model
• Simulations characterized often by difference or differential operators
• Simulation dominantly sparse (nearest neighbor) data structures
– Some important data analytics involves full matrix algorithm but
– “Bag of words (users, rankings, images..)” algorithms are sparse, as is
PageRank
02/16/2016
33

34. “Force Diagrams” for macromolecules and
Facebook
02/16/2016
34

35. Comparison of Data Analytics with Simulation II
• There are similarities between some graph problems and particle
simulations with a strange cutoff force.
– Both Map-Communication
• Note many big data problems are “long range force” (as in gravitational
simulations) as all points are linked.
– Easiest to parallelize. Often full matrix algorithms
– e.g. in DNA sequence studies, distance (i, j) defined by BLAST, Smith-
Waterman, etc., between all sequences i, j.
– Opportunity for “fast multipole” ideas in big data. See NRC report
• In image-based deep learning, neural network weights are block sparse
(corresponding to links to pixel blocks) but can be formulated as full matrix
operations on GPUs and MPI in blocks.
• In HPC benchmarking, Linpack being challenged by a new sparse conjugate
gradient benchmark HPCG, while I am diligently using non- sparse
conjugate gradient solvers in clustering and Multi-dimensional scaling.
02/16/2016
35

36. Convergence Diamond Facets
Big Data and Big Simulation
Processing View
All Model Properties but differences
between Big Data and Big Simulation
02/16/2016 36

37. Diamond Facets in Processing (runtime) View I
used in Big Data and Big Simulation
• Pr-1M Micro-benchmarks ogres that exercise simple features of hardware
such as communication, disk I/O, CPU, memory performance
• Pr-2M Local Analytics executed on a single core or perhaps node
• Pr-3M Global Analytics requiring iterative programming models (G5,G6)
across multiple nodes of a parallel system
• Pr-12M Uses Linear Algebra common in Big Data and simulations
– Subclasses like Full Matrix
– Conjugate Gradient, Krylov, Arnoldi iterative subspace methods
– Structured and unstructured sparse matrix methods
• Pr-13M Graph Algorithms (G3) Clear important class of algorithms -- as
opposed to vector, grid, bag of words etc. – often hard especially in parallel
• Pr-14M Visualization is key application capability for big data and
simulations
• Pr-15M Core Libraries Functions of general value such as Sorting, Math
functions, Hashing
3702/16/2016

38. Diamond Facets in Processing (runtime) View II
used in Big Data
• Pr-4M Basic Statistics (G1): MRStat in NIST problem features
• Pr-5M Recommender Engine: core to many e-commerce, media businesses;
collaborative filtering key technology
• Pr-6M Search/Query/Index: Classic database which is well studied (Baru, Rabl
tutorial)
• Pr-7M Data Classification: assigning items to categories based on many methods
– MapReduce good in Alignment, Basic statistics, S/Q/I, Recommender, Classification
• Pr-8M Learning of growing importance due to Deep Learning success in speech
recognition etc..
• Pr-9M Optimization Methodology: overlapping categories including
– Machine Learning, Nonlinear Optimization (G6), Maximum Likelihood or 2 least
squares minimizations, Expectation Maximization (often Steepest descent),
Combinatorial Optimization, Linear/Quadratic Programming (G5), Dynamic
Programming
• Pr-10M Streaming Data or online Algorithms. Related to DDDAS (Dynamic Data-
Driven Application Systems)
• Pr-11M Data Alignment (G7) as in BLAST compares samples with repository
3802/16/2016

39. Diamond Facets in Processing (runtime) View III
used in Big Simulation
• Pr-16M Iterative PDE Solvers: Jacobi, Gauss Seidel etc.
• Pr-17M Multiscale Method? Multigrid and other variable
resolution approaches
• Pr-18M Spectral Methods as in Fast Fourier Transform
• Pr-19M N-body Methods as in Fast multipole, Barnes-Hut
• Pr-20M Both Particles and Fields as in Particle in Cell method
• Pr-21M Evolution of Discrete Systems as in simulation of
Electrical Grids, Chips, Biological Systems, Epidemiology.
Needs Ordinary Differential Equation solvers
• Pr-22M Nature of Mesh if used: Structured, Unstructured,
Adaptive
3902/16/2016
Covers NAS Parallel Benchmarks and Berkeley Dwarfs

40. Facets of the Diamonds (same as
Ogres here)
Data Source and Style Aspects
Present but often less important for
Simulations (that use and produce data)
02/16/2016 40

41. Data Source and Style Diamond View I
i. SQL NewSQL or NoSQL: NoSQL includes Document,
Column, Key-value, Graph, Triple store; NewSQL is SQL redone to exploit
NoSQL performance
ii. Other Enterprise data systems: 10 examples from NIST integrate
SQL/NoSQL
iii. Set of Files or Objects: as managed in iRODS and extremely common in
scientific research
iv. File systems, Object, Blob and Data-parallel (HDFS) raw storage:
Separated from computing or colocated? HDFS v Lustre v. Openstack
Swift v. GPFS
v. Archive/Batched/Streaming: Streaming is incremental update of datasets
with new algorithms to achieve real-time response (G7); Before data gets
to compute system, there is often an initial data gathering phase which is
characterized by a block size and timing. Block size varies from month
(Remote Sensing, Seismic) to day (genomic) to seconds or lower (Real
time control, streaming)
• Streaming divided into categories overleaf
4102/16/2016

42. Data Source and Style Diamond View II
• Streaming divided into 5 categories depending on event size and
synchronization and integration
• Set of independent events where precise time sequencing unimportant.
• Time series of connected small events where time ordering important.
• Set of independent large events where each event needs parallel processing with time sequencing not
critical
• Set of connected large events where each event needs parallel processing with time sequencing critical.
• Stream of connected small or large events to be integrated in a complex way.
vi. Shared/Dedicated/Transient/Permanent: qualitative property of data; Other
characteristics are needed for permanent auxiliary/comparison datasets and these
could be interdisciplinary, implying nontrivial data movement/replication
vii. Metadata/Provenance: Clear qualitative property but not for kernels as important
aspect of data collection process
viii. Internet of Things: 24 to 50 Billion devices on Internet by 2020
ix. HPC simulations: generate major (visualization) output that often needs to be
mined
x. Using GIS: Geographical Information Systems provide attractive access to
geospatial data
4202/16/2016

43. 2. Perform real time analytics on data source
streams and notify users when specified
events occur
4302/16/2016
Storm, Kafka, Hbase, Zookeeper
Streaming Data
Streaming Data
Streaming Data
Posted Data Identified Events
Filter Identifying
Events
Repository
Specify filter
Archive
Post Selected
Events
Fetch streamed
Data

44. 5. Perform interactive analytics on data in
analytics-optimized database
4402/16/2016
Hadoop, Spark, Giraph, Pig …
Data Storage: HDFS, Hbase
Data, Streaming, Batch …..
Mahout, R

45. 5A. Perform interactive analytics on
observational scientific data
4502/16/2016
Grid or Many Task Software, Hadoop, Spark, Giraph, Pig …
Data Storage: HDFS, Hbase, File Collection
Streaming Twitter data for
Social Networking
Science Analysis Code,
Mahout, R
Transport batch of data to primary
analysis data system
Record Scientific Data in
“field”
Local
Accumulate
and initial
computing
Direct Transfer
NIST examples include
LHC, Remote Sensing,
Astronomy and
Bioinformatics

46. HPC-ABDS
02/16/2016 46

47. Data Platforms
4702/16/2016

48. 4802/16/2016
Big Data and (Exascale) Simulation Convergence IIKaleidoscope of (Apache) Big Data Stack (ABDS) and HPC Technologies
Cross-
Cutting
Functions
1) Message
and Data
Protocols:
Avro, Thrift,
Protobuf
2) Distributed
Coordination
: Google
Chubby,
Zookeeper,
Giraffe,
JGroups
3) Security &
Privacy:
InCommon,
Eduroam
OpenStack
Keystone,
LDAP, Sentry,
Sqrrl, OpenID,
SAML OAuth
4)
Monitoring:
Ambari,
Ganglia,
Nagios, Inca
17) Workflow-Orchestration: ODE, ActiveBPEL, Airavata, Pegasus, Kepler, Swift, Taverna, Triana, Trident, BioKepler, Galaxy, IPython, Dryad,
Naiad, Oozie, Tez, Google FlumeJava, Crunch, Cascading, Scalding, e-Science Central, Azure Data Factory, Google Cloud Dataflow, NiFi (NSA),
Jitterbit, Talend, Pentaho, Apatar, Docker Compose, KeystoneML
16) Application and Analytics: Mahout , MLlib , MLbase, DataFu, R, pbdR, Bioconductor, ImageJ, OpenCV, Scalapack, PetSc, PLASMA MAGMA,
Azure Machine Learning, Google Prediction API & Translation API, mlpy, scikit-learn, PyBrain, CompLearn, DAAL(Intel), Caffe, Torch, Theano, DL4j,
H2O, IBM Watson, Oracle PGX, GraphLab, GraphX, IBM System G, GraphBuilder(Intel), TinkerPop, Parasol, Dream:Lab, Google Fusion Tables,
CINET, NWB, Elasticsearch, Kibana, Logstash, Graylog, Splunk, Tableau, D3.js, three.js, Potree, DC.js, TensorFlow, CNTK
15B) Application Hosting Frameworks: Google App Engine, AppScale, Red Hat OpenShift, Heroku, Aerobatic, AWS Elastic Beanstalk, Azure, Cloud
Foundry, Pivotal, IBM BlueMix, Ninefold, Jelastic, Stackato, appfog, CloudBees, Engine Yard, CloudControl, dotCloud, Dokku, OSGi, HUBzero, OODT,
Agave, Atmosphere
15A) High level Programming: Kite, Hive, HCatalog, Tajo, Shark, Phoenix, Impala, MRQL, SAP HANA, HadoopDB, PolyBase, Pivotal HD/Hawq,
Presto, Google Dremel, Google BigQuery, Amazon Redshift, Drill, Kyoto Cabinet, Pig, Sawzall, Google Cloud DataFlow, Summingbird
14B) Streams: Storm, S4, Samza, Granules, Neptune, Google MillWheel, Amazon Kinesis, LinkedIn, Twitter Heron, Databus, Facebook
Puma/Ptail/Scribe/ODS, Azure Stream Analytics, Floe, Spark Streaming, Flink Streaming, DataTurbine
14A) Basic Programming model and runtime, SPMD, MapReduce: Hadoop, Spark, Twister, MR-MPI, Stratosphere (Apache Flink), Reef, Disco,
Hama, Giraph, Pregel, Pegasus, Ligra, GraphChi, Galois, Medusa-GPU, MapGraph, Totem
13) Inter process communication Collectives, point-to-point, publish-subscribe: MPI, HPX-5, Argo BEAST HPX-5 BEAST PULSAR, Harp, Netty,
ZeroMQ, ActiveMQ, RabbitMQ, NaradaBrokering, QPid, Kafka, Kestrel, JMS, AMQP, Stomp, MQTT, Marionette Collective, Public Cloud: Amazon
SNS, Lambda, Google Pub Sub, Azure Queues, Event Hubs
12) In-memory databases/caches: Gora (general object from NoSQL), Memcached, Redis, LMDB (key value), Hazelcast, Ehcache, Infinispan, VoltDB,
H-Store
12) Object-relational mapping: Hibernate, OpenJPA, EclipseLink, DataNucleus, ODBC/JDBC
12) Extraction Tools: UIMA, Tika
11C) SQL(NewSQL): Oracle, DB2, SQL Server, SQLite, MySQL, PostgreSQL, CUBRID, Galera Cluster, SciDB, Rasdaman, Apache Derby, Pivotal
Greenplum, Google Cloud SQL, Azure SQL, Amazon RDS, Google F1, IBM dashDB, N1QL, BlinkDB, Spark SQL
11B) NoSQL: Lucene, Solr, Solandra, Voldemort, Riak, ZHT, Berkeley DB, Kyoto/Tokyo Cabinet, Tycoon, Tyrant, MongoDB, Espresso, CouchDB,
Couchbase, IBM Cloudant, Pivotal Gemfire, HBase, Google Bigtable, LevelDB, Megastore and Spanner, Accumulo, Cassandra, RYA, Sqrrl, Neo4J,
graphdb, Yarcdata, AllegroGraph, Blazegraph, Facebook Tao, Titan:db, Jena, Sesame
Public Cloud: Azure Table, Amazon Dynamo, Google DataStore
11A) File management: iRODS, NetCDF, CDF, HDF, OPeNDAP, FITS, RCFile, ORC, Parquet
10) Data Transport: BitTorrent, HTTP, FTP, SSH, Globus Online (GridFTP), Flume, Sqoop, Pivotal GPLOAD/GPFDIST
9) Cluster Resource Management: Mesos, Yarn, Helix, Llama, Google Omega, Facebook Corona, Celery, HTCondor, SGE, OpenPBS, Moab, Slurm,
Torque, Globus Tools, Pilot Jobs
8) File systems: HDFS, Swift, Haystack, f4, Cinder, Ceph, FUSE, Gluster, Lustre, GPFS, GFFS
Public Cloud: Amazon S3, Azure Blob, Google Cloud Storage
7) Interoperability: Libvirt, Libcloud, JClouds, TOSCA, OCCI, CDMI, Whirr, Saga, Genesis
6) DevOps: Docker (Machine, Swarm), Puppet, Chef, Ansible, SaltStack, Boto, Cobbler, Xcat, Razor, CloudMesh, Juju, Foreman, OpenStack Heat,
Sahara, Rocks, Cisco Intelligent Automation for Cloud, Ubuntu MaaS, Facebook Tupperware, AWS OpsWorks, OpenStack Ironic, Google Kubernetes,
Buildstep, Gitreceive, OpenTOSCA, Winery, CloudML, Blueprints, Terraform, DevOpSlang, Any2Api
5) IaaS Management from HPC to hypervisors: Xen, KVM, QEMU, Hyper-V, VirtualBox, OpenVZ, LXC, Linux-Vserver, OpenStack, OpenNebula,
Eucalyptus, Nimbus, CloudStack, CoreOS, rkt, VMware ESXi, vSphere and vCloud, Amazon, Azure, Google and other public Clouds
Networking: Google Cloud DNS, Amazon Route 53
21 layers
Over 350
Software
Packages
January
29
2016

49. Functionality of 21 HPC-ABDS Layers
1) Message Protocols:
2) Distributed Coordination:
3) Security & Privacy:
4) Monitoring:
5) IaaS Management from HPC to hypervisors:
6) DevOps:
7) Interoperability:
8) File systems:
9) Cluster Resource Management:
10) Data Transport:
11) A) File management
B) NoSQL
C) SQL
12) In-memory databases&caches / Object-relational mapping / Extraction Tools
13) Inter process communication Collectives, point-to-point, publish-subscribe, MPI:
14) A) Basic Programming model and runtime, SPMD, MapReduce:
B) Streaming:
15) A) High level Programming:
B) Frameworks
16) Application and Analytics:
17) Workflow-Orchestration:
49
Here are 21 functionalities.
(including 11, 14, 15 subparts)
4 Cross cutting at top
17 in order of layered diagram
starting at bottom
02/16/2016

50. 02/16/2016
50

51. Java Grande
Revisited on 3 data analytics codes
Clustering
Multidimensional Scaling
Latent Dirichlet Allocation
all sophisticated algorithms
5102/16/2016

52. Java MPI performs better than Threads I
5202/16/2016
• 48 24 core Haswell nodes 200K DA-MDS Dataset size
• Default MPI much worse than threads
• Optimized MPI using shared memory node-based messaging is much better than
threads

53. MPI versus OpenMPI Intranode 400K DA-MDS
• Note, the default Java + OMPI could NOT handle 400K data
• Java + SM + OMPI is our zero intra-node messaging
implementation using Java shared memory maps
• SPIDAL Java is above plus other optimizations
• Input 400K x 400K binary Short (2 bytes) matrix ~300GB
• Runtime 5302/16/2016
OpenMPI Intranode
OpenMP Intranode

54. Java MPI performs better than Threads II
128 24 core Haswell nodes
5402/16/2016
200K Dataset Speedup
Best Threads
Best MPI

55. DA-MDS Speedup versus Problem Sizes
• All tests were done with the SPIDAL Java version
• Cores 48 to 1152 were done on 48 nodes by varying the number of
cores used within a node from 1 to 24
• Cores 2304 and 3072 were done on 96 and 128 nodes while using 24
cores per node
5502/16/2016

56. DA-MDS Scaling on 32 36 core Haswell Nodes
• Core counts of 32 to 1152 were done on 32 nodes by varying the number of
cores used within a node from 1 to 36
• Scales up to 32 cores per node (16 cores per chip)
5602/16/2016
MPI both Internode
and Intranode

57. Java compared to Fortran and C
5702/16/2016
• NAS Java is old Java Grande benchmark
• NAS serial benchmark total times in
seconds
• SPIDAL Java optimizations were
incomplete and included Zero-Garbage
Collection and Cache Optimizations only
• This is our machine learning code
• DA-MDS block matrix multiplication
times in milliseconds
• 1x1* is the serial version and does the
computation of a single process in
24x48 pattern.
• SPIDAL Java includes all optimizations
SPIDAL Java
1. Cache and memory optimizations - these include blocked loops, 1D arrays to
represent 2D data, and loop re-ordering
2. Minimal memory and zero full Garbage Collection (GC) - these include using statically
allocated arrays to reduce GC and using the minimum number of arrays possible
3. Off-heap data structures - these were used to load initial data, intra-node messaging,
and do MPI communications.

58. 446K sequences
~100 clusters
02/16/2016
58

59. July 21 2007 Positions
End 2008 Positions
59
10 year US Stock daily price time series mapped to 3D (work
in progress)
3400 stocks
Sector Groupings
02/16/2016

60. Clueweb
6002/16/2016
enwiki
Bi-gram
Clueweb
Latent Dirichlet Allocation on 100 Haswell nodes: red is Map-Collective

61. Big Data Exascale convergence
6102/16/2016

62. Big Data and (Exascale) Simulation Convergence I
• Our approach to Convergence is built around two ideas that avoid addressing
the hardware directly as with modern DevOps technology it isn’t hard to
retarget applications between different hardware systems.
• Rather we approach Convergence through applications and software.
• Convergence Diamonds Convergence unify Big Simulation and Big Data
applications and so allow one to more easily identify good approaches to
implement Big Data and Exascale applications in a uniform fashion.
• Software convergence builds on the HPC-ABDS High Performance
Computing enhanced Apache Big Data Software Stack concept
(http://dsc.soic.indiana.edu/publications/HPC-ABDSDescribed_final.pdf,
http://hpc-abds.org/kaleidoscope/ )
– This arranges key HPC and ABDS software together in 21 layers showing
where HPC and ABDS overlap. It for example, introduces a communication
layer to allow ABDS runtime like Hadoop Storm Spark and Flink to use the
richest high performance capabilities shared with MPI Generally it
proposes how to use HPC and ABDS software together.
– Layered Architecture offers some protection to rapid ABDS technology
change
6202/16/2016

63. Dual Convergence Architecture
• Running same HPC-ABDS across all platforms but data management
machine has different balance in I/O, Network and Compute from “model”
machine
6302/16/2016
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Data Management Model for Big Data
and Big Simulation

64. Things to do for Big Data and (Exascale)
Simulation Convergence II
• Converge Applications: Separate data and model to classify
Applications and Benchmarks across Big Data and Big
Simulations to give Convergence Diamonds with many
facets
– Indicated how to extend Big Data Ogres to Big Simulations
by looking separately at model and data in Ogres
– Diamonds have four views or collections of facets: Problem
Architecture; Execution; Data Source and Style; Big Data
and Big Simulation Processing
– Facets cover data, model or their combination – the
problem or application
– Note Simulation Processing View has by construction,
similarities to old parallel computing benchmarks
6402/16/2016

65. Things to do for Big Data and (Exascale)
Simulation Convergence III
• Convergence Benchmarks: we will use benchmarks that cover the facets of the
convergence diamonds i.e. cover big data and simulations;
– As we separate data and model, compute intensive simulation benchmarks (e.g.
solve partial differential equation) will be linked with data analytics (the model in
big data)
– IU focus SPIDAL (Scalable Parallel Interoperable Data Analytics Library) with
high performance clustering, dimension reduction, graphs, image processing as
well as MLlib will be linked to core PDE solvers to explore the communication
layer of parallel middleware
– Maybe integrating data and simulation is an interesting idea in benchmark sets
• Convergence Programming Model
– Note parameter servers used in machine learning will be mimicked by collective
operators invoked on distributed parameter (model) storage
– E.g. Harp as Hadoop HPC Plug-in
– There should be interest in using Big Data software systems to support exascale
simulations
– Streaming solutions from IoT to analysis of astronomy and LHC data will drive
high performance versions of Apache streaming systems
6502/16/2016

66. Things to do for Big Data and (Exascale)
Simulation Convergence IV
• Converge Language: Make Java run as fast as C++ (Java
Grande) for computing and communication
• Surprising that so much Big Data work in industry but basic
high performance Java methodology and tools missing
– Needs some work as no agreed OpenMP for Java parallel
threads
– OpenMPI supports Java but needs enhancements to get
best performance on needed collectives (For C++ and
Java)
– Convergence Language Grande should support Python,
Java (Scala), C/C++ (Fortran)
6602/16/2016