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Scalable Algorithms in the Cloud IIMicrosoft Summer SchoolDoing Research in the CloudMoscow State UniversityAugust 4 2014Geoffrey Fox gcf@indiana.edu http://www.infomall.orgSchool of Informatics and ComputingDigital Science CenterIndiana University Bloomington1NIST Big Data Use Cases51 Detailed Use Cases: Contributed July-September 2013Covers goals, data features such as 3 Vs, software, hardwarehttp://bigdatawg.nist.gov/usecases.phphttps://bigdatacoursespring2014.appspot.com/course (Section 5)Government Operation(4): National Archives and Records Administration, Census BureauCommercial(8): Finance in Cloud, Cloud Backup, Mendeley (Citations), Netflix, Web Search, Digital Materials, Cargo shipping (as in UPS)Defense(3): Sensors, Image surveillance, Situation AssessmentHealthcare and Life Sciences(10): Medical records, Graph and Probabilistic analysis, Pathology, Bioimaging, Genomics, Epidemiology, People Activity models, BiodiversityDeep Learning and Social Media(6): Driving Car, Geolocate images/cameras, Twitter, Crowd Sourcing, Network Science, NIST benchmark datasetsThe Ecosystem for Research(4): Metadata, Collaboration, Language Translation, Light source experimentsAstronomy and Physics(5): Sky Surveys including comparison to simulation, Large Hadron Collider at CERN, Belle Accelerator II in JapanEarth, 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 sensorsEnergy(1): Smart grid

326 Features for each use case Biased to science3Examples: Especially Image and Internet of Things based Applications

http://www.kpcb.com/internet-trends10 Image-based Use Cases17:Pathology Imaging/ Digital Pathology: PP, LML, MR for search becoming terabyte 3D images, Global Classification18&35: Computational Bioimaging (Light Sources): PP, LML Also materials26: 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-sheet44: UAVSAR Data Processing, Data Product Delivery, and Data Services: PP to find slippage from radar images45, 46: Analysis of Simulation visualizations: PP LML ?GML find paths, classify orbits, classify patterns that signal earthquakes, instabilities, climate, turbulence17:Pathology Imaging/ Digital Pathology IApplication: Digital pathology imaging is an emerging field where examination of high resolution images of tissue specimens enables novel and more effective ways for disease diagnosis. Pathology image analysis segments massive (millions per image) spatial objects such as nuclei and blood vessels, represented with their boundaries, along with many extracted image features from these objects. The derived information is used for many complex queries and analytics to support biomedical research and clinical diagnosis. 7HealthcareLife Sciences

MR, MRIter, PP, ClassificationParallelism over ImagesStreaming17:Pathology Imaging/ Digital Pathology IICurrent Approach: 1GB raw image data + 1.5GB analytical results per 2D image. MPI for image analysis; MapReduce + Hive with spatial extension on supercomputers and clouds. GPUs used effectively. Figure below shows the architecture of Hadoop-GIS, a spatial data warehousing system over MapReduce to support spatial analytics for analytical pathology imaging.8HealthcareLife Sciences

Futures: Recently, 3D pathology imaging is made possible through 3D laser technologies or serially sectioning hundreds of tissue sections onto slides and scanning them into digital images. Segmenting 3D microanatomic objects from registered serial images could produce tens of millions of 3D objects from a single image. This provides a deep map of human tissues for next generation diagnosis. 1TB raw image data + 1TB analytical results per 3D image and 1PB data per moderated hospital per year.Architecture of Hadoop-GIS, a spatial data warehousing system over MapReduce to support spatial analytics for analytical pathology imaging26: Large-scale Deep LearningApplication: Large models (e.g., neural networks with more neurons and connections) combined with large datasets are increasingly the top performers in benchmark tasks for vision, speech, and Natural Language Processing. One needs to train a deep neural network from a large (>>1TB) corpus of data (typically imagery, video, audio, or text). Such training procedures often require customization of the neural network architecture, learning criteria, and dataset pre-processing. In addition to the computational expense demanded by the learning algorithms, the need for rapid prototyping and ease of development is extremely high.Current Approach: The largest applications so far are to image recognition and scientific studies of unsupervised learning with 10 million images and up to 11 billion parameters on a 64 GPU HPC Infiniband cluster. Both supervised (using existing classified images) and unsupervised applications9Deep Learning, Social Networking GML, EGO, MRIter, ClassifyFutures: Large datasets of 100TB or more may be necessary in order to exploit the representational power of the larger models. Training a self-driving car could take 100 million images at megapixel resolution. Deep Learning shares many characteristics with the broader field of machine learning. The paramount requirements are high computational throughput for mostly dense linear algebra operations, and extremely high productivity for researcher exploration. One needs integration of high performance libraries with high level (python) prototyping environments

INClassified OUT27: Organizing large-scale, unstructured collections of consumer photos IApplication: Produce 3D reconstructions of scenes using collections of millions to billions of consumer images, where neither the scene structure nor the camera positions are known a priori. Use resulting 3d models to allow efficient browsing of large-scale photo collections by geographic position. Geolocate new images by matching to 3d models. Perform object recognition on each image. 3d reconstruction posed as a robust non-linear least squares optimization problem where observed relations between images are constraints and unknowns are 6-d camera pose of each image and 3-d position of each point in the scene.Current Approach: Hadoop cluster with 480 cores processing data of initial applications. Note over 500 billion images on Facebook and over 5 billion on Flickr with over 500 million images added to social media sites each day.10Deep LearningSocial NetworkingEGO, GIS, MR, ClassificationParallelism over Photos27: Organizing large-scale, unstructured collections of consumer photos IIFutures: Need many analytics including feature extraction, feature matching, and large-scale probabilistic inference, which appear in many or most computer vision and image processing problems, including recognition, stereo resolution, and image denoising. Need to visualize large-scale 3-d reconstructions, and navigate large-scale collections of images that have been aligned to maps.11Deep LearningSocial Networking

36: Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey IApplication: The survey explores the variable universe in the visible light regime, on time scales ranging from minutes to years, by searching for variable and transient sources. It discovers a broad variety of astrophysical objects and phenomena, including various types of cosmic explosions (e.g., Supernovae), variable stars, phenomena associated with accretion to massive black holes (active galactic nuclei) and their relativistic jets, high proper motion stars, etc. The data are collected from 3 telescopes (2 in Arizona and 1 in Australia), with additional ones expected in the near future (in Chile). Current Approach: The survey generates up to ~ 0.1 TB on a clear night with a total of ~100 TB in current data holdings. The data are preprocessed at the telescope, and transferred to Univ. of Arizona and Caltech, for further analysis, distribution, and archiving. The data are processed in real time, and detected transient events are published electronically through a variety of dissemination mechanisms, with no proprietary withholding period (CRTS has a completely open data policy). Further data analysis includes classification of the detected transient events, additional observations using other telescopes, scientific interpretation, and publishing. In this process, it makes a heavy use of the archival data (several PBs) from a wide variety of geographically distributed resources connected through the Virtual Observatory (VO) framework.12Astronomy & PhysicsPP, ML, ClassificationParallelism over Images and Events: Celestial events identified in Telescope ImagesStreamingFutures: CRTS is a scientific and methodological testbed and precursor of larger surveys to come, notably the Large Synoptic Survey Telescope (LSST), expected to operate in 2020s and selected as the highest-priority ground-based instrument in the 2010 Astronomy and Astrophysics Decadal Survey. LSST will gather about 30 TB per night. 13

36: Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey I

Astronomy & Physics35: Light source beamlinesApplication: Samples are exposed to X-rays from light sources in a variety of configurations depending on the experiment. Detectors (essentially high-speed digital cameras) collect the data. The data are then analyzed to reconstruct a view of the sample or process being studied. Current Approach: A variety of commercial and open source software is used for data analysis examples including Octopus for Tomographic Reconstruction, Avizo (http://vsg3d.com) and FIJI (a distribution of ImageJ) for Visualization and Analysis. Data transfer is accomplished using physical transport of portable media (severely limits performance) or using high-performance GridFTP, managed by Globus Online or workflow systems such as SPADE.Futures: Camera resolution is continually increasing. Data transfer to large-scale computing facilities is becoming necessary because of the computational power required to conduct the analysis on time scales useful to the experiment. Large number of beamlines (e.g. 39 at LBNL ALS) means that total data load is likely to increase significantly and require a generalized infrastructure for analyzing gigabytes per second of data from many beamline detectors at multiple facilities.

14Research Ecosystem PP, LML, Streaming43: Radar Data Analysis for CReSIS Remote Sensing of Ice Sheets IVTypical CReSIS echogram with Detected Boundaries. The upper (green) boundary is between air and ice layer while the lower (red) boundary is between ice and terrain15Earth, Environmental and Polar Science

PP, GISParallelism over Radar ImagesStreaming44: UAVSAR Data Processing, Data Product Delivery, and Data Services IICombined unwrapped coseismic interferograms for flight lines 26501, 26505, and 08508 for the October 2009 April 2010 time period. End points where slip can be seen on the Imperial, Superstition Hills, and Elmore Ranch faults are noted. GPS stations are marked by dots and are labeled.16Earth, Environmental and Polar Science

PP, GISParallelism over Radar ImagesStreamingInternet of Things and Streaming AppsIt 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 sample10: Cargo Shipping Tracking as in UPS, Fedex PP GIS LML13: Large Scale Geospatial Analysis and Visualization PP GIS LML28: Truthy: Information diffusion research from Twitter Data PP MR for Search, GML for community determination39: Particle Physics: Analysis of LHC Large Hadron Collider Data: Discovery of Higgs particle PP Local Processing Global statistics50: DOE-BER AmeriFlux and FLUXNET Networks PP GIS LML51: Consumption forecasting in Smart Grids PP GIS LML17

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http://www.kpcb.com/internet-trends

Database

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PortalSS: Sensor or DataInterchangeServiceWorkflow through multiple filter/discovery cloudsAnotherCloudRaw Data Data Information Knowledge Wisdom DecisionsSSSSAnotherServiceSSAnotherGridSSSSSSSSSSSSSSSSSSFusion for Discovery/DecisionsStorageCloudComputeCloudSSSSSSSSFilterCloudFilterCloudFilterCloudDiscoveryCloudDiscoveryCloudFilterCloudFilterCloudFilterCloud

SSFilterCloudFilterCloudFilterCloudFilterCloudDistributedGridHadoop ClusterSSIOTCloudDevice Pub-SubStorm Datastore Data AnalysisApache Storm provides scalable distributed system for processing data streams coming from devices in real time. For example Storm layer can decide to store the data in cloud storage for furtheranalysisor to send control data back to the devicesEvaluating Pub-Sub Systems ActiveMQ, RabbitMQ, Kafka, Kestrel

Turtlebot and KinectPerformanceFrom Device to Cloud6 FutureGrid India Medium OpenStack machines1 Broker machine, RabbitMQ or ActiveMQ1 machine hosting ZooKeeper and Storm Nimbus (Master for Storm)2 Sensor sites generating data2 Storm nodes sending back the same data and we measure the unidirectional latency

Using drones and Kinects

10: Cargo Shipping Architecture24Commercial

Industry StandardsContinuous TrackingPP Streaming50: DOE-BER AmeriFlux and FLUXNET Networks Application: AmeriFlux and FLUXNET are US and world collections respectively of sensors that observe trace gas fluxes (CO2, water vapor) across a broad spectrum of times (hours, days, seasons, years, and decades) and space. Moreover, such datasets provide the crucial linkages among organisms, ecosystems, and process-scale studiesat climate-relevant scales of landscapes, regions, and continentsfor incorporation into biogeochemical and climate models.Current Approach: Software includes EddyPro, Custom analysis software, R, python, neural networks, Matlab. There are ~150 towers in AmeriFlux and over 500 towers distributed globally collecting flux measurements.Futures: Field experiment data taking would be improved by access to existing data and automated entry of new data via mobile devices. Need to support interdisciplinary study integrating diverse data sources.25Earth, Environmental and Polar Science

Fusion, PP, GISParallelism over SensorsStreaming51: Consumption forecasting in Smart GridsApplication: Predict energy consumption for customers, transformers, sub-stations and the electrical grid service area using smart meters providing measurements every 15-mins at the granularity of individual consumers within the service area of smart power utilities. Combine Head-end of smart meters (distributed), Utility databases (Customer Information, Network topology; centralized), US Census data (distributed), NOAA weather data (distributed), Micro-grid building information system (centralized), Micro-grid sensor network (distributed). This generalizes to real-time data-driven analytics for time series from cyber physical systemsCurrent Approach: GIS based visualization. Data is around 4 TB a year for a city with 1.4M sensors in Los Angeles. Uses R/Matlab, Weka, Hadoop software. Significant privacy issues requiring anonymization by aggregation. Combine real time and historic data with machine learning for predicting consumption.Futures: Wide spread deployment of Smart Grids with new analytics integrating diverse data and supporting curtailment requests. Mobile applications for client interactions.26EnergyFusion, PP, MR, ML, GIS, ClassificationParallelism over SensorsStreaming28: Truthy: Information diffusion research using Twitter DataApplication: Understanding how communication spreads on socio-technical networks. Detecting potentially harmful information spread at the early stage (e.g., deceiving messages, orchestrated campaigns, untrustworthy information, etc.)Current Approach: 1) Acquisition and storage of a large volume (30 TB a year compressed) of continuous streaming data from Twitter (~100 million messages per day, ~500GB data/day increasing over time); (2) near real-time analysis of such data, for anomaly detection, stream clustering, signal classification and online-learning; (3) data retrieval, big data visualization, data-interactive Web interfaces, public API for data querying. Use Python/SciPy/NumPy/MPI for data analysis. Information diffusion, clustering, and dynamic network visualization capabilities already existFutures: Truthy plans to expand incorporating Google+ and Facebook. Need to move towards Hadoop/IndexedHBase & HDFS distributed storage. Previously used Redis as an in-memory database to be a buffer for real-time analysis. Need streaming clustering, anomaly detection and online learning.27Deep LearningSocial NetworkingIndex, S/Q, MR, MRIter, Graph, ClassificationParallelism over TweetsStreamingBig Data Patterns the OgresDistributed Computing Practice for Large-Scale Science & Engineering S. Jha, M. Cole, D. Katz, O. Rana, M. Parashar, and J. Weissman, Work of Characteristics of 6 Distributed Applications NOTE DATAFLOWApplication ExampleExecution UnitCommunicationCoordination Execution EnvironmentMontageMultiple sequential and parallel executableFilesDataflow (DAG)Dynamic process creation, executionNEKTARMultiple concurrent parallel executablesStream basedDataflowCo-scheduling, data streaming, async. I/O Replica-ExchangeMultiple seq. and parallel executablesPub/subDataflow and eventsDecoupled coordination and messagingClimate Prediction (generation)Multiple seq. & parallel executablesFiles and messagesMaster-Worker, events@Home (BOINC)Climate Prediction(analysis) Multiple seq. & parallel executablesFiles and messagesDataflow Dynamics process creation, workflow executionSCOOP Multiple ExecutableFiles and messagesDataflowPreemptive scheduling, reservationsCoupled Fusion Multiple executableStream-basedDataflowCo-scheduling, data streaming, async I/O10 Enterprise DB Generic Use CasesMultiple users performing interactive queries and updates on a database with basic availability and eventual consistency (BASE)Perform real time analytics on data source streams and notify users when specified events occurMove data from external data sources into a highly horizontally scalable data store, transform it using highly horizontally scalable processing (e.g. Map-Reduce), and return it to the horizontally scalable data store (ELT)Perform batch analytics on the data in a highly horizontally scalable data store using highly horizontally scalable processing (e.g MapReduce) with a user-friendly interface (e.g. SQL like)Perform interactive analytics on data in analytics-optimized databaseVisualize data extracted from horizontally scalable Big Data storeMove data from a highly horizontally scalable data store into a traditional Enterprise Data WarehouseExtract, process, and move data from data stores to archivesCombine data from Cloud databases and on premise data stores for analytics, data mining, and/or machine learningOrchestrate multiple sequential and parallel data transformations and/or analytic processing using a workflow manager

These consist of multiple data systems including classic DB, streaming, archives, Hive, analytics, workflow and different user interfaces (events to visualization)

10 Security & Privacy Use CasesConsumer Digital Media UsageNielsen HomescanWeb Traffic AnalyticsHealth Information ExchangePersonal Genetic PrivacyPharma Clinic Trial Data Sharing Cyber-securityAviation IndustryMilitary - Unmanned Vehicle sensor dataEducation - Common Core Student Performance Reporting

7 Computational Giants of NRC Massive Data Analysis Report G1:Basic Statistics e.g. MRStatG2:Generalized N-Body ProblemsG3:Graph-Theoretic ComputationsG4:Linear Algebraic ComputationsG5:Optimizations e.g. Linear ProgrammingG6:Integration e.g. LDA and other GMLG7:Alignment Problems e.g. BLASTWould like to capture essence of these use casessmall kernels, mini-appsOr Classify applications into patterns

Do it from HPC background not database viewpointe.g. focus on cases with detailed analytics

Section 5 of my class https://bigdatacoursespring2014.appspot.com/preview classifies 51 use cases with ogre facets

HPC Benchmark ClassicsLinpack or HPL: Parallel LU factorization for solution of linear equationsNPB version 1: Mainly classic HPC solver kernelsMG: MultigridCG: Conjugate GradientFT: Fast Fourier TransformIS: Integer sortEP: Embarrassingly ParallelBT: Block TridiagonalSP: Scalar Pentadiagonal LU: Lower-Upper symmetric Gauss Seidel

13 Berkeley DwarfsDense Linear Algebra Sparse Linear AlgebraSpectral MethodsN-Body MethodsStructured GridsUnstructured GridsMapReduceCombinational LogicGraph TraversalDynamic ProgrammingBacktrack and Branch-and-BoundGraphical ModelsFinite State MachinesFirst 6 of these correspond to Colellas original. 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!Facets of the OgresProblem Architecture Facet of Ogres (Meta or MacroPattern)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)Classic MapReduce: Search, Index and Query and Classification algorithms like collaborative filtering (G1 for MRStat in Table 2, G7)Global Analytics or Machine Learning requiring iterative programming models (G5,G6). Often fromMaximum Likelihood or 2 minimizationsExpectation Maximization (often Steepest descent) Problem set up as a graph (G3) as opposed to vector, gridSPMD: Single Program Multiple DataBSP or Bulk Synchronous Processing: well-defined compute-communication phasesFusion: Knowledge discovery often involves fusion of multiple methods. Workflow: All applications often involve orchestration (workflow) of multiple componentsUse Agents: as in epidemiology (swarm approaches)Note problem and machine architectures are related

Big dwarfs are OgresImplement Ogres in ABDS+

37One Facet of Ogres has Computational FeaturesFlops per byte; Communication Interconnect requirements; Is application (graph) constant or dynamic?Most applications consist of a set of interconnected entities; is this regular as a set of pixels or is it a complicated irregular graph?Is communication BSP, Asynchronous, Pub-Sub, Collective, Point to Point? Are algorithms Iterative or not?Are algorithms governed by dataflowData Abstraction: key-value, pixel, graph, vectorAre data points in metric or non-metric spaces? Is algorithm O(N2) or O(N) (up to logs) for N points per iteration (G2)Core libraries needed: matrix-matrix/vector algebra, conjugate gradient, reduction, broadcast

Data Source and Style Facet of Ogres I (i) SQL or NoSQL: NoSQL includes Document, Column, Key-value, Graph, Triple store(ii) Other Enterprise data systems: 10 examples from NIST integrate SQL/NoSQL(iii) Set of Files: as managed in iRODS and extremely common in scientific research(iv) File, Object, Block and Data-parallel (HDFS) raw storage: Separated from computing?(v) Internet of Things: 24 to 50 Billion devices on Internet by 2020(vi) Streaming: Incremental update of datasets with new algorithms to achieve real-time response (G7)(vii) HPC simulations: generate major (visualization) output that often needs to be mined (viii) Involve GIS: Geographical Information Systems provide attractive access to geospatial dataBig dwarfs are OgresImplement Ogres in ABDS+

39Data Source and Style Facet of Ogres IIBefore 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)There are storage/compute system styles: Shared, Dedicated, Permanent, TransientOther characteristics are needed for permanent auxiliary/comparison datasets and these could be interdisciplinary, implying nontrivial data movement/replicationBig dwarfs are OgresImplement Ogres in ABDS+

40Analytics Facet (kernels) of the OgresCore Analytics Ogres (microPattern) IMap-OnlyPleasingly parallel - Local Machine Learning MapReduce: Search/Query/IndexSummarizing statistics as in LHC Data analysis (histograms) (G1)Recommender Systems (Collaborative Filtering) Linear Classifiers (Bayes, Random Forests)Alignment and Streaming (G7)Genomic Alignment, Incremental ClassifiersGlobal AnalyticsNonlinear Solvers (structure depends on objective function) (G5,G6)Stochastic Gradient Descent SGD(L-)BFGS approximation to Newtons MethodLevenberg-Marquardt solverCore Analytics Ogres (microPattern) IIMap-Collective (See Mahout, MLlib) (G2,G4,G6)Often use matrix-matrix,-vector operations, solvers (conjugate gradient)Outlier Detection, Clustering (many methods), Mixture Models, LDA (Latent Dirichlet Allocation), PLSI (Probabilistic Latent Semantic Indexing)SVM and Logistic RegressionPageRank, (find leading eigenvector of sparse matrix)SVD (Singular Value Decomposition)MDS (Multidimensional Scaling)Learning Neural Networks (Deep Learning)Hidden Markov Models

Core Analytics Ogres (microPattern) IIIGlobal Analytics Map-Communication (targets for Giraph) (G3) Graph Structure (Communities, subgraphs/motifs, diameter, maximal cliques, connected components)Network Dynamics - Graph simulation Algorithms (epidemiology)Global Analytics Asynchronous Shared Memory (may be distributed algorithms)Graph Structure (Betweenness centrality, shortest path) (G3)Linear/Quadratic Programming, Combinatorial Optimization, Branch and Bound (G5)

Lessons / InsightsProposed classification of Big Data applications with features and kernels for analyticsAdd other Ogres for workflow, data systems etc.Looked at Image-based and Streaming Big Data ProblemsData intensive algorithms do not have the well developed high performance libraries familiar from HPCChallenges with O(N2) problemsGlobal Machine Learning or (Exascale Global Optimization) particularly challenging