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What is the "Big Data" version of the Linpack 
Benchmark? 
What is “Big Data” version of Berkeley Dwarfs 
and NAS Parallel...
Summary 
• Advances in high-performance/parallel computing in the 1980's 
and 90's was spurred by the development of quali...
The Answer
Linpack for data? 
• There is a simple solution – use Linpack 
• The core of many data analytics algorithms is often linea...
Proposed Spectrum of Benchmarks/Features 
• Classic Database: TPC benchmarks 
• NoSQL Data systems: store, index, query (e...
Why? Cover Software Stack 
Stress different components 
Combines HPC and Apache 
140 packages but still incomplete
Kaleidoscope of (Apache) Big Data Stack (ABDS) and HPC Technologies 
Cross-Cutting 
Functionalities 
Message Protocols: 
T...
HPC-ABDS Layers 
1) Message Protocols 
2) Distributed Coordination: 
3) Security & Privacy: 
4) Monitoring: 
5) IaaS Manag...
Maybe a Big Data Initiative would include 
• We don’t need 140 software packages so can choose e.g. 
• Workflow: Python, P...
Why? Build on Parallel 
Computing Experience 
Benchmarks Instantiate Key Features
HPC Benchmark Classics 
• Linpack or HPL: Parallel LU factorization for solution of 
linear equations 
• NPB version 1: Ma...
13 Berkeley Dwarfs 
• Dense Linear Algebra 
• Sparse Linear Algebra 
• Spectral Methods 
• N-Body Methods 
• Structured Gr...
7 Computational Giants of 
NRC Massive Data Analysis Report 
1) G1: Basic Statistics (see MRStat later) 
2) G2: Generalize...
Why? Cover Big Data 
Application Survey 
Performed by NIST Big Data Working Group 
Leads to Ogres covering Big Data Applic...
51 Detailed Use Cases: Contributed July-September 2013 
Covers goals, data features such as 3 V’s, software, hardware 
• h...
Features of 51 Use Cases I 
• PP (26) Pleasingly Parallel or Map Only 
• MR (18) Classic MapReduce MR (add MRStat below fo...
Features of 51 Use Cases II 
• CF (4) Collaborative Filtering for recommender engines 
• LML (36) Local Machine Learning (...
Data Source and Style Facet I 
• (i) SQL or NoSQL: NoSQL includes Document, Column, Key-value, 
Graph, Triple store 
• (ii...
2. Perform real time analytics on data source streams and 
notify users when specified events occur 
Streaming Data 
Strea...
5. Perform interactive analytics on data in analytics-optimized 
data system 
Hadoop, Spark, Giraph, Pig … 
Data Storage: ...
Data Source and Style Facet II 
• Before data gets to compute system, there is often an 
initial data gathering phase whic...
5A. Perform interactive analytics on 
observational scientific data 
Grid or Many Task Software, Hadoop, Spark, Giraph, Pi...
Why? Typical Big Data Analytics 
See Mahout, MLLib, R, usage in 
application survey
Core Analytics I 
• Map-Only 
• Pleasingly parallel - Local Machine Learning 
• MapReduce: Search/Query/Index 
• Summarizi...
Core Analytics II 
• Global Analytics: Map-Collective (See Mahout, 
MLlib) (G2,G4,G6) 
• Often use matrix-matrix,-vector o...
Core Analytics III 
• Global Analytics – Map-Communication (targets 
for Giraph) (G3) 
• Graph Structure (Communities, sub...
Proposed Spectrum of Benchmarks/Features 
• Classic Database: TPC benchmarks 
• NoSQL Data systems: store, index, query (e...
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What is the "Big Data" version of the Linpack Benchmark? ; What is “Big Data” version of Berkeley Dwarfs and NAS Parallel Benchmarks?

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Advances in high-performance/parallel computing in the 1980's and 90's was spurred by the development of quality high-performance libraries, e.g., SCALAPACK, as well as by well-established benchmarks, such as Linpack.

Similar efforts to develop libraries for high-performance data analytics are underway. In this talk we motivate that such benchmarks should be motivated by frequent patterns encountered in high-performance analytics, which we call Ogres.

Based upon earlier work, we propose that doing so will enable adequate coverage of the "Apache" bigdata stack as well as most common application requirements, whilst building upon parallel computing experience.

Given the spectrum of analytic requirements and applications, there are multiple "facets" that need to be covered, and thus we propose an initial set of benchmarks - by no means currently complete - that covers these characteristics.

We hope this will encourage debate

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What is the "Big Data" version of the Linpack Benchmark? ; What is “Big Data” version of Berkeley Dwarfs and NAS Parallel Benchmarks?

  1. 1. What is the "Big Data" version of the Linpack Benchmark? What is “Big Data” version of Berkeley Dwarfs and NAS Parallel Benchmarks? Based on Presentation at Clusters, Clouds, and Data for Scientific Computing CCDSC 2014 September 6 2014 Geoffrey Fox, Judy Qiu School of Informatics and Computing Digital Science Center Indiana University Bloomington Shantenu Jha Radical Group Rutgers University
  2. 2. Summary • Advances in high-performance/parallel computing in the 1980's and 90's was spurred by the development of quality high-performance libraries, e.g., SCALAPACK, as well as by well-established benchmarks, such as Linpack. • Similar efforts to develop libraries for high-performance data analytics are underway. In this talk we motivate that such benchmarks should be motivated by frequent patterns encountered in high-performance analytics, which we call Ogres. • Based upon earlier work, we propose that doing so will enable adequate coverage of the "Apache" bigdata stack as well as most common application requirements, whilst building upon parallel computing experience. • Given the spectrum of analytic requirements and applications, there are multiple "facets" that need to be covered, and thus we propose an initial set of benchmarks - by no means currently complete - that covers these characteristics. – We hope this will encourage debate
  3. 3. The Answer
  4. 4. Linpack for data? • There is a simple solution – use Linpack • The core of many data analytics algorithms is often linear algebra and involves full not sparse matrices although – Not always Matrix solvers but rather large matrix multiplication – Matrix solution can be done (much faster) with conjugate gradient in cases I’ve looked at (200 iterations for matrix size of a million) • Big Data can be dominated by analytics but also by other aspects of problem such as datastore access and data transport. • We expand “topic of presentation” to “broad based benchmark set” in spirit of Berkeley Dwarfs i.e. “capture key features” and “grand challenges” in (academic) Big Data
  5. 5. Proposed Spectrum of Benchmarks/Features • Classic Database: TPC benchmarks • NoSQL Data systems: store, index, query (e.g. on Tweets) • Hard core commercial: Web Search, Collaborative Filtering (different structure and defer to Google!) • Streaming: Gather in Pub-Sub(Kafka) + Process (Apache Storm) solution (e.g. gather tweets, Internet of Things) • Pleasingly parallel (Local Analytics): as in initial steps of LHC, Astronomy, Pathology, Bioimaging (differ in type of data analysis) • “Global” Analytics: Deep Learning, SVM, Multidimensional Scaling, Graph Community (~Clustering) to finding to Shortest Path (?Shared memory) • Workflow linking above
  6. 6. Why? Cover Software Stack Stress different components Combines HPC and Apache 140 packages but still incomplete
  7. 7. Kaleidoscope of (Apache) Big Data Stack (ABDS) and HPC Technologies Cross-Cutting Functionalities Message Protocols: Thrift, Protobuf Distributed Coordination: Zookeeper, JGroups Security & Privacy: InCommon, OpenStack Keystone, LDAP, Sentry Monitoring: Ambari, Ganglia, Nagios, Inca Workflow-Orchestration: Oozie, ODE, Airavata, OODT (Tools), Pegasus, Kepler, Swift, Taverna, Trident, ActiveBPEL, BioKepler, Galaxy, IPython Application and Analytics: Mahout , MLlib , MLbase, CompLearn, R, Bioconductor, ImageJ, Scalapack, PetSc High level Programming: Hive, HCatalog, Pig, Shark, MRQL, Impala, Sawzall, Drill Basic Programming model and runtime, SPMD, Streaming, MapReduce: Hadoop, Spark, Twister, Stratosphere, Tez, Hama, Storm, S4, Samza, Giraph, Pregel, Pegasus, Reef Inter process communication Collectives, point-to-point, publish-subscribe: Harp, MPI, Netty, ZeroMQ, ActiveMQ, RabbitMQ, QPid, Kafka, Kestrel In-memory databases/caches: GORA (general object from NoSQL), Memcached, Redis (key value), Hazelcast, Ehcache Object-relational mapping: Hibernate, OpenJPA and JDBC Standard Extraction Tools: UIMA, Tika SQL: Oracle, MySQL, Phoenix, SciDB, Apache Derby NoSQL: HBase, Accumulo, Cassandra, Solandra, MongoDB, CouchDB, Lucene, Solr, Berkeley DB, Azure Table, Dynamo, Riak, Voldemort. Neo4J, Yarcdata, Jena, Sesame, AllegroGraph, RYA, Parquet File management: iRODS Data Transport: BitTorrent, HTTP, FTP, SSH, Globus Online (GridFTP) Cluster Resource Management: Mesos, Yarn, Helix, Llama, Condor, SGE, OpenPBS, Moab, Slurm, Torque File systems: HDFS, Swift, Cinder, Ceph, FUSE, Gluster, Lustre, GPFS, GFFS Interoperability: Whirr, JClouds, OCCI, CDMI DevOps: Docker, Puppet, Chef, Ansible, Boto, Libcloud, Cobbler, CloudMesh IaaS Management from HPC to hypervisors: OpenStack, OpenNebula, Eucalyptus, CloudStack, vCloud, Amazon, Azure, Google
  8. 8. 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) SQL / NoSQL / File management: 12) In-memory databases&caches / Object-relational mapping / Extraction Tools 13) Inter process communication Collectives, point-to-point, publish-subscribe 14) Basic Programming model and runtime, SPMD, Streaming, MapReduce, MPI: 15) High level Programming: 16) Application and Analytics: 17) Workflow-Orchestration: Here are 17 functionalities. Technologies are presented in this order 4 Cross cutting at top 13 in order of layered diagram starting at bottom
  9. 9. Maybe a Big Data Initiative would include • We don’t need 140 software packages so can choose e.g. • Workflow: Python, Pegasus or Kepler • Data Mahout, R, ImageJ, Scalapack • High level Programming: Hive, Pig • Parallel Programming model: Hadoop, Spark, Giraph (Twister4Azure, Harp), Storm • Communication: MPI; Kafka or RabbitMQ (Streaming) • In-memory: Memcached • Data Management: Hbase, MongoDB, MySQL or Derby • Distributed Coordination: Zookeeper • Cluster Management: Yarn, Slurm • File Systems: HDFS, Lustre • DevOps: Cloudmesh, Chef, Puppet, Docker, Cobbler • IaaS: Amazon, Azure, OpenStack, Libcloud • Monitoring: Inca, Ganglia, Nagios
  10. 10. Why? Build on Parallel Computing Experience Benchmarks Instantiate Key Features
  11. 11. 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 – IS: Integer sort – EP: Embarrassingly Parallel – BT: Block Tridiagonal – SP: Scalar Pentadiagonal – LU: Lower-Upper symmetric Gauss Seidel
  12. 12. 13 Berkeley Dwarfs • 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 • Graphical Models • Finite State Machines First 6 of these correspond to Colella’s 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. NO clean solution likely for Big Data. Need multiple facets!
  13. 13. 7 Computational Giants of NRC Massive Data Analysis Report 1) G1: Basic Statistics (see MRStat later) 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
  14. 14. Why? Cover Big Data Application Survey Performed by NIST Big Data Working Group Leads to Ogres covering Big Data Application features. Here we focus on benchmarks that cover the Ogres
  15. 15. 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) 26 Features for each use case Biased to science • 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 15
  16. 16. Features of 51 Use Cases I • PP (26) 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
  17. 17. Features of 51 Use Cases II • CF (4) Collaborative Filtering for recommender engines • LML (36) Local Machine Learning (Independent for each parallel entity) • 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
  18. 18. Data Source and Style Facet I • (i) SQL or NoSQL: NoSQL includes Document, Column, Key-value, Graph, Triple store • (ii) Other Enterprise data systems: e.g. Warehouses • (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 data
  19. 19. 2. Perform real time analytics on data source streams and notify users when specified events occur Streaming Data Streaming Data Streaming Data Specify filter Posted Data Identified Events Archive Storm, Kafka, Hbase, Zookeeper Filter Identifying Events Repository Post Selected Events Fetch streamed Data
  20. 20. 5. Perform interactive analytics on data in analytics-optimized data system Hadoop, Spark, Giraph, Pig … Data Storage: HDFS, Hbase Data, Streaming, Batch ….. Mahout, R
  21. 21. Data Source and Style Facet II • 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) • There are storage/compute system styles: Shared, Dedicated, Permanent, Transient • Other characteristics are needed for permanent auxiliary/comparison datasets and these could be interdisciplinary, implying nontrivial data movement/replication • 10 Data Access/Use Styles from Bob Marcus at NIST (you have seen his patterns 2 and 5 and my extension for science 5A follows)
  22. 22. 5A. Perform interactive analytics on observational scientific data Grid or Many Task Software, Hadoop, Spark, Giraph, Pig … Data Storage: HDFS, Hbase, File Collection (Lustre) 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
  23. 23. Why? Typical Big Data Analytics See Mahout, MLLib, R, usage in application survey
  24. 24. Core Analytics I • Map-Only • Pleasingly parallel - Local Machine Learning • MapReduce: Search/Query/Index • Summarizing statistics as in LHC Data analysis (histograms) (G1) • Recommender Systems (Collaborative Filtering) • Linear Classifiers (Bayes, Random Forests) • Alignment and Streaming (G7) • Genomic Alignment, Incremental Classifiers • Global Analytics: Nonlinear Solvers (structure depends on objective function) (G5,G6) – Stochastic Gradient Descent SGD – (L-)BFGS approximation to Newton’s Method – Levenberg-Marquardt solver
  25. 25. Core Analytics II • Global Analytics: Map-Collective (See Mahout, MLlib) (G2,G4,G6) • Often use matrix-matrix,-vector operations, solvers (conjugate gradient) • Clustering (many methods), Mixture Models, LDA (Latent Dirichlet Allocation), PLSI (Probabilistic Latent Semantic Indexing) • SVM and Logistic Regression • Outlier Detection (several approaches) • PageRank, (find leading eigenvector of sparse matrix) • SVD (Singular Value Decomposition) • MDS (Multidimensional Scaling) • Learning Neural Networks (Deep Learning) • Hidden Markov Models
  26. 26. Core Analytics III • Global 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)
  27. 27. Proposed Spectrum of Benchmarks/Features • Classic Database: TPC benchmarks • NoSQL Data systems: store, index, query (e.g. on Tweets) • Hard core commercial: Web Search, Collaborative Filtering (different structure and defer to Google!) • Streaming: Gather in Pub-Sub(Kafka) + Process (Apache Storm) solution (e.g. gather tweets, Internet of Things) • Pleasingly parallel (Local Analytics): as in initial steps of LHC, Astronomy, Pathology, Bioimaging (differ in type of data analysis) • “Global” Analytics: Deep Learning, SVM, Multidimensional Scaling, Graph Community finding (~Clustering) to Shortest Path (? Shared memory) • Workflow linking above

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