(1)Classifying Simulation and Data Intensive Applications and the HPC-Big Data Convergence. WBDB 2015 Seventh Workshop on Big Data Benchmarking Geoffrey Fox, Shantenu Jha, Judy Qiu December 14, 2015,. Department of Intelligent Systems Engineering School of Informatics and Computing, Digital Science Center Indiana University Bloomington. 12/14/2015. 1.

(2) NIST Big Data Initiative Led by Chaitin Baru, Bob Marcus, Wo Chang And Big Data Application Analysis. 12/14/2015. 2.

(3) 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 and 12/14/2015. 3.

(4) 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. 12/14/2015. 4.

(5) Online Use Case Form 8/5/2015. 5.

(6) 8/5/2015. 6.

(7) 51 Detailed Use Cases: Contributed July-September 2013. Covers goals, data features such as 3 V’s, software, hardware • • • • • • • • • •. •. 26 Features for each use case. Biased to science (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. 12/14/2015. 7.

(8) Features and Examples. 12/14/2015. 8.

(9) 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. 12/14/2015. 9.

(10) 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. 12/14/2015. 10.

(11) 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. 12/14/2015. 11.

(12) 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? 12/14/2015. 12.

(13) • •. • •. •. • •. •. •. 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. 8/5/2015. 13.

(14) 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. 8/5/2015. 14.

(15) 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 12/14/2015. 15.

(16) Big Data Patterns – the Ogres Classification. 12/14/2015. 16.

(17) • •. • • • • •. Classifying Big Data Applications. “Benchmarks” “kernels” “algorithm” “mini-apps” can serve multiple purposes Motivate hardware and software features – e.g. collaborative filtering algorithm 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 designed 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 as Ogres with features termed “facets” 50 Facets divided into 4 sets or views where each view has “similar” facets – Allow one to study coverage of benchmark sets 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” 12/14/2015. 17.

(18) 7 Computational Giants of NRC Massive Data Analysis Report Big Data Models? 1) 2) 3) 4) 5) 6) 7). G1: G2: G3: G4: G5: G6: G7:. Basic Statistics e.g. MRStat Generalized N-Body Problems Graph-Theoretic Computations Linear Algebraic Computations Optimizations e.g. Linear Programming Integration e.g. LDA and other GML Alignment Problems e.g. BLAST. 12/14/2015. 18.

(19) HPC 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 Simulation Models IS: Integer sort EP: Embarrassingly Parallel BT: Block Tridiagonal SP: Scalar Pentadiagonal LU: Lower-Upper symmetric Gauss Seidel. 12/14/2015. 19.

(20) 13 Berkeley Dwarfs. 1) 2) 3) 4) 5) 6) 7) 8) 9) 10) 11). Dense Linear Algebra Sparse Linear Algebra Spectral Methods N-Body Methods Structured Grids Unstructured Grids MapReduce Combinational Logic Graph Traversal Dynamic Programming Backtrack and Branch-and-Bound 12) Graphical Models 13) Finite State Machines. Largely Models for Data or Simulation 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!. 12/14/2015. 20.

(21) Processing View. 7. 6 5. 4. 3. 2. 1. 4. 3 2 1. 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. 4 Ogre Views and 50 Facets. Pleasingly Parallel Classic MapReduce Map-Collective Map Point-to-Point Map Streaming Shared Memory Single Program Multiple Data Bulk Synchronous Parallel Fusion Dataflow Agents Workflow. 1 2 3 4 5 6. 7 8 9 10 11. 12. 1 2. 3 4 5. 6 7 8 9 10 11 12 13 14. O N2 = NN / O(N) = N Metric = M / Non-Metric = N Data Abstraction Iterative / Simple Regular = R / Irregular = I Dynamic = D / Static = S Communication Structure Veracity Variety Velocity Volume Execution Environment; Core libraries Flops per Byte; Memory I/O Performance Metrics. Problem Architecture View. 8. Micro-benchmarks Local Analytics Global Analytics Base Statistics. Recommendations Search / Query / Index Classification. Learning Optimization Methodology Streaming Alignment Linear Algebra Kernels. Graph Algorithms Visualization. 14 13 12 11 10 9. 10 9 8 7 6 5. Data Source and Style View. 12/14/2015. Execution View. 21.

(22) Dwarfs and Ogres give Convergence Diamonds. • Macropatterns or Problem Architecture View: Unchanged • Execution View: Significant changes to separate Data and Model and add characteristics of Simulation models • Data Source and Style View: Unchanged – present but less important for Simulations compared to big data • Processing View: becomes Big Data Processing View • Add Big Simulation Processing View: includes specifics of key simulation kernels – includes NAS Parallel Benchmarks and Berkeley Dwarfs. 12/14/2015. 22.

(23) Facets of the Ogres Problem Architecture Meta or Macro Aspects of Ogres Valid for Big Data or Big Simulations as describes Problem which is Model-Data combination. 12/14/2015. 23.

(24) Problem Architecture View of Ogres (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 bioimagery, 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 xii. Workflow: All applications often involve orchestration (workflow) of multiple components. 12/14/2015. 24.

(25) 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”. 12/14/2015. 25.

(26) 6 Forms of MapReduce cover “all” circumstances. Describes - Problem (Model reflecting data) - Machine - Software Architecture. 12/14/2015. 26.

(27) 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. 8/5/2015. 27.

(28) Ogre Facets Execution Features View Many similar Features for Big Data and Simulations Needs split into1) Model 2) Data 3) Problem (the combination) add a) difference/differential operator in model and b) multiscale/hierarchical model. 8/5/2015. 28.

(29) One View of Ogres has Facets that are micropatterns or Execution Features i. ii. iii.. Performance Metrics; property found by benchmarking Ogre Flops per byte; memory or I/O Execution Environment; Core libraries needed: matrix-matrix/vector algebra, conjugate gradient, reduction, broadcast; Cloud, HPC etc. iv. Volume: property of an Ogre instance: a) Data Volume and b) Model Size v. Velocity: qualitative property of Ogre with value associated with instance. Mainly Data vi. Variety: important property especially of composite Ogres; Data and Model separately vii. Veracity: important property of “mini-appls” but not kernels; Data and Model separately 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 drives Model? xiv. Is Model algorithm O(N2) or O(N) (up to logs) for N points per iteration (G2). 8/5/2015. 29.

(30) •. 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. •. Big Data often has large collective communication. – not a common simulation paradigm except where “Reduce” summarizes pleasingly parallel execution as in Some Monte Carlos – Classic simulation has a lot of smallish point-to-point messages. • 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.

(31) “Force Diagrams” for macromolecules and Facebook.

(32) •. 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, SmithWaterman, 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..

(33) Facets of the Ogres Big Data Processing View Largely Model Properties Could produce a separate set of facets for Simulation Processing View. 8/5/2015. 33.

(34) Facets in Processing (runtime) View of Ogres I i.. Micro-benchmarks ogres that exercise simple features of hardware such as communication, disk I/O, CPU, memory performance ii. Local Analytics executed on a single core or perhaps node iii. Global Analytics requiring iterative programming models (G5,G6) across multiple nodes of a parallel system iv. Optimization Methodology: overlapping categories. v. vi.. i. ii. iii. iv. v. vi. vii.. Nonlinear Optimization (G6) Machine Learning Maximum Likelihood or 2 minimizations Expectation Maximization (often Steepest descent) Combinatorial Optimization Linear/Quadratic Programming (G5) Dynamic Programming. Visualization is key application capability with algorithms like MDS useful but it itself part of “mini-app” or composite Ogre Alignment (G7) as in BLAST compares samples with repository. 8/5/2015. 34.

(35) Facets in Processing (run time) View of Ogres II. vii. 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.. –. MapReduce good in Alignment, Basic statistics, S/Q/I, Recommender, Calssification. –. viii. Basic Statistics (G1): MRStat in NIST problem features ix. Search/Query/Index: Classic database which is well studied (Baru, Rabl tutorial) x. Recommender Engine: core to many e-commerce, media businesses; collaborative filtering key technology xi. Classification: assigning items to categories based on many methods xii. Deep Learning of growing importance due to success in speech recognition etc. xiii. Problem set up as a graph (G3) as opposed to vector, grid, bag of words etc. xiv. Using Linear Algebra Kernels: much machine learning uses linear algebra kernels. 8/5/2015. 35.

(36) Facets of the Ogres Data Source and Style Aspects add streaming from Processing view here Present but often less important for Simulations (that use and produce data). 12/14/2015. 36.

(37) i.. Data Source and Style View of Ogres 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) 12/14/2015. 37.

(38) Data Source and Style View of Ogres II. 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 Note 10 Bob Marcus (led NIST effort) Use cases 12/14/2015. 38.

(39) 2. Perform real time analytics on data source streams and notify users when specified events occur Specify filter. Filter Identifying Events Streaming Data Streaming Data Streaming Data. Fetch streamed Data. Posted Data. Post Selected Events. Identified Events Archive Repository. Storm, Kafka, Hbase, Zookeeper 12/14/2015. 39.

(40) 5. Perform interactive analytics on data in analytics-optimized database. Mahout, R Hadoop, Spark, Giraph, Pig … Data Storage: HDFS, Hbase. Data, Streaming, Batch ….. 12/14/2015. 40.

(41) 5A. Perform interactive analytics on observational scientific data Science Analysis Code, Mahout, R Grid or Many Task Software, Hadoop, Spark, Giraph, Pig … Data Storage: HDFS, Hbase, File Collection Direct Transfer. Streaming Twitter data for Social Networking Record Scientific Data in “field”. Transport batch of data to primary analysis data system Local Accumulate and initial computing 12/14/2015. NIST examples include LHC, Remote Sensing, Astronomy and Bioinformatics. 41.

(42) Benchmarks and Ogres. 12/14/2015. 42.

(43) • • •. Benchmarks/Mini-apps spanning Facets. Look at NSF SPIDAL Project, NIST 51 use cases, Baru-Rabl review Catalog facets of benchmarks and choose entries to cover “all facets” Micro Benchmarks: SPEC, EnhancedDFSIO (HDFS), Terasort, Wordcount, Grep, MPI, Basic Pub-Sub …. • SQL and NoSQL Data systems, Search, Recommenders: TPC (-C to x–HS for Hadoop), BigBench, Yahoo Cloud Serving, Berkeley Big Data, HiBench, BigDataBench, Cloudsuite, Linkbench. – includes MapReduce cases Search, Bayes, Random Forests, Collaborative Filtering. • Spatial Query: select from image or earth data • Alignment: Biology as in BLAST • Streaming: Online classifiers, Cluster tweets, Robotics, Industrial Internet of Things, Astronomy; BGBenchmark. • Pleasingly parallel (Local Analytics): as in initial steps of LHC, Pathology, Bioimaging (differ in type of data analysis) • Global Analytics: Outlier, Clustering, LDA, SVM, Deep Learning, MDS, PageRank, Levenberg-Marquardt, Graph 500 entries • Workflow and Composite (analytics on xSQL) linking above. 12/14/2015. 43.

(44) Summary Classification of Big Data Applications and Big Data Exascale convergence. 12/14/2015. 44.

(45) • •. •. •. 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. This talk has described the Convergence Diamonds Convergence that 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. This is summarized on Slides III and IV The software approach builds on the HPC-ABDS High Performance Computing enhanced Apache Big Data Software Stack concept described in Slide II (, ) 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 (for ABDS independent of HPC) 12/14/2015. 45.

(46) Big Data and (Exascale) Simulation Convergence II. Green implies HPC Integration. 46.

(47) Things to do for Big Data and (Exascale) Simulation Convergence III. • 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 will have five views or collections of facets: Problem Architecture; Execution; Data Source and Style; Big Data Processing; Big Simulation Processing – Facets cover data, model or their combination – the problem or application – Note Simulation Processing View has similarities to old parallel computing benchmarks 12/14/2015. 47.

(48) •. •. Things to do for Big Data and (Exascale) Simulation Convergence IV. 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 12/14/2015. 48.

(49) Things to do for Big Data and (Exascale) Simulation Convergence V. • Converge Language: Make Java run as fast as C++ (Java Grande) for computing and communication – see following slide – 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) 12/14/2015. 49.

(50) Java MPI performs better than Threads I. 128 24 core Haswell nodes Default MPI much worse than threads Optimized MPI using shared memory node-based messaging is much better than threads. 12/14/2015. 50.

(51) Java MPI performs better than Threads II 128 24 core Haswell nodes. 200K Dataset Speedup. 12/14/2015. 51.


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