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PEARC17:A real-time machine learning and visualization framework for scientific workflows
- 1. A real-time machine learning and visualization framework for scientific workflows Feng Li, Fengguang Song Indiana University-Purdue university, Indianapolis Email: lifen@iupui.edu, fgsong@iupui.edu
- 2. Outlines • Background • An non-parametric anomaly detection method • A scientific workflow & software framework • Experiments • Conclusion and future work
- 3. Background • Recent advancements in parallel computing and HPC • Intensive computation helps solve extremely large problem • This also brings huge amount of data. • Much faster interconnect.(infiniband->RDMA) • How do we deal with such large data: • Post-hoc?(let simulation results flushed to disk) • In-situ?(closely coupled) • Something in between?
- 4. Tuple Space & Dataspaces • Tuple Space • Data is access by content and type, rather than by their raw memory address. • Ability to describe data without referring any computer architecture! • Ideal model to couple different simulation and analysis applications. • Dataspaces • Developed by Rutgers University(Docan, 2012) • Derived from the concept of tuple space • coupling different applications together use RDMA • Example Interface: • dspaces_put(var_name, version, elem_size, boundaries, pdata)
- 5. Vortex detection inTurbulence flow • Vortex? • Swirling motion of fluid around a center region. • Precise definition? • Why it matters? • How to identify it?
- 6. Vortex detection inTurbulence flow • A region-based non-parametric anomaly detection method.(Póczos, 2012) • Simulation data is divided into “regions” • Each region(A collection of data points) can be regarded as a random sampling from a distribution with density p. • Features: (vx , vy, dc) • v,Velocity in both dimensions • dc, Distance to center. BA C D
- 7. Vortex detection inTurbulence flow, Cont. • Density in this data point can be estimated using kNN methods. • Divergence(distance between regions) • L2 divergence: 𝐿(𝑝| 𝑞 = ∫((𝑝 𝑥 − 𝑞(𝑥))+ 𝑑𝑥)-/+ • Once we know how to measure the ’difference’ of two regions, we can use distance-based clustering method.(eg. kmedoids) 𝑝0 𝑋2 = 𝑘/( 𝑛 − 1 ∗ 𝑐 ∗ 𝑣0 𝑖 )
- 8. Demonstration of theWorkflow • Divide → Divergence → Generate new medoids→ Re- assign Cluster ID • Different application coupled using Dataspaces. • Components in the Workflow: • Simulators • Data processing • Data Analysis(Anomaly detection) • Visualization tool(Paraview Catalyst)
- 9. Distributed Sampling • Original k-medoids method does scale well with large dataset • O(𝑛+ ) , n is number of regions • Data processing will be the bottle neck • CLARA(Clustering LARge Application, Kaufman 2009) • Such small granularity operations are expensive in Dataspaces(“boundaries”) • Reorganize the data: • Single Dataspaces operation will operate on a larger chunk of data.
- 10. Experiments • Testbed: Karst indiana University. • Other tools used: • John HopkinsTurbulence Databases. • Paraview Catalyst(Co-processing Library, Fabian 2011) • Originally designed for the ’in-situ’ approach • generic visualization framework, support ”live visualization” • Openfoam(Open Source CFDToolbox) • DataSpaces adaptor to default writer.
- 11. Experiment with JHTDB data • Data description • Forced isotropic dataset from John HopkinsTurbulence Databases(JHTDB) • 1024*1024*1024 grid, 5028 frames • Well formatted inVTK/HDF5 • All regions are clustered into: • Steady flow • Unsteady flow • Random flow • Client side • Paraview GUI • Two views are linked together.
- 12. ExecutionTime Breakdown • Analyzes the communication overhead and efficiency of the framework • How time is spend in each component in this workflow • Average data transfer time and computation time for all the four applications(simulator, data processing, data analysis, catalyst visualization)
- 13. Strong Scalability • How well the workflow can scale with more computing resources • Fixed Input size of 4096*4096(larger) in each timestep, which contains 1GB velocity/pressure data • More processes for all applications? • Latency_produce/Latency_consume
- 14. Conclusion • a flexible and extensible software framework • A parallel non-parametric clustering method • Using DataSpaces greatly reduces I/O time and scales well.
- 16. References 1. Ciprian Docan, Manish Parashar, and Scott Klasky. 2012. Dataspaces: an interaction and coordination framework for coupled simulation workflows.Cluster Computing 15, 2 (2012), 163–181. 2. Barnabás Póczos, Liang Xiong, and Jeff Schneider. 2012. Nonparametric divergence estimation with applications to machine learning on distributions. arXiv preprint arXiv:1202.3758 (2012) 3. Nathan Fabian, Kenneth Moreland, DavidThompson, Andrew C Bauer, Pat Marion, Berk Gevecik, Michel Rasquin, and Kenneth E Jansen. 2011.The paraview coprocessing library: A scalable, general purpose in situ visualization library. In Large Data Analysis andVisualization (LDAV), 2011 IEEE Symposium on. IEEE, 89–96. 4. Raymond Leonard Kaufman and Peter J Rousseeuw. 2009. Finding groups in data: an introduction to cluster analysis.Vol. 344. JohnWiley & Sons.