Cyberinfrastructure for Solving Complex Problems
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Invited Talk at Data Science Workshop, Higher Education Commission, Pakistan. 17th April 2017. Meeting URL: http://hec.gov.pk/english/services/faculty/dscience/Pages/Data-Science-Workshop.aspx
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Cyberinfrastructure for Solving Complex Problems
- 1. Cyberinfrastructure for Einstein’s Equations and Beyond Gabrielle Allen Professor, Astronomy Research Professor, Computer Science Associate Dean, College of Education Senior Research Scientist, NCSA University of Illinois Urbana-Champaign
- 2. National Center for Supercomputing Applications, University of Illinois
- 3. National Center for Supercomputing Applications “NCSA will be a home for addressing complex research problems in science and society, powered by the development and application of advanced and comprehensive digital environments.”
- 4. Solving Complex Problems Science research xx=x[i]; yy=y[i]; zz=z[i]; rho_2=xx*xx*yy*yy; If (rho<epsilon}{ xx=epsilon; rho_2=xx*xx+yy*yy; rho=sqrt(rho_2); } Software (& Hardware) Community Impact
- 5. Complex Problems World: multiscale, multiphysics, data- driven E.g. Neutron Stars, Plants, Viruses, … General Challenges Determine correct scale to describe a physical event and the correct governing equations Determine how different phenomena interact - often at different scales Determine data inputs (experimental, observational, …) Design simple but effective interfaces that can be implemented in software (Find, fund & motivate team)
- 7. Main Research Areas LIGO Scientific Consortium, NANOGrav Consortium, Einstein Toolkit Consortium Connected to Dark Energy Survey, Large Synoptic Survey Telescope projects (NCSA main data hub) Analytic and numerical relativity, black hole and neutron star astrophysics, computational astrophysics, gravitational wave source modeling, high performance & high throughput computing, applications of deep learning, multimessenger astrophysics scenarios Scientific software, cybersecurity & identity management, network engineering, open data repositories and open science
- 8. Gravitational Waves Changes in the curvature of spacetime that propagate as waves at the speed of light Transport energy as gravitational radiation Predicted by Einstein (1916) Theory of General Relativity For 100 years until 2016 indirectly observed Observations by new gravitational wave detectors provide a new window on the universe … gravitational wave astronomy! 8
- 9. Gravitational Wave Physics Instruments Models & Simulation Theory Scientific Discovery! Gµν = 8π Tµν Colliding black holes & neutron stars, supernovae collapse, gamma-ray bursts, big bang, …
- 10. GW150914 Observed by LIGO Sept 14, 2015 1 billion light years away Initial black holes 36 and 29 solar masses Final black hole 62 solar masses Difference radiated as gravitational waves E=mc2 New discoveries! Existence of binary black hole systems Direct detection of gravitational waves First observation of binary black hole merger B.P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration), Phys. Rev. Lett. 116, 061102
- 12. Cactus: www.CactusCode.org Open source component framework for HPC Modular system with high level abstractions Components (“thorns”) defined by parameters, variables, methods Cactus “flesh” binds together Cactus Computational Toolkit: general thorns Different application areas Numerical relativity, CFD, coastal science, petroleum, quantum gravity, cosmology, …
- 13. Building a Computational Numerical Relativity Community Cactus came from the relativity community European project with 10 sites developed community open code base Each group had different expertise Cactus allowed developing shared interfaces/standards Easy to add a component, share components Supports both collaboration and competition EU Network for Gravitational Wave Sources: 2001
- 14. Key Features Cactus framework provides scheduling, application APIs for parallel operations Driver thorn provides scheduling, load balancing, parallelization Application thorns deal only with local part of parallel mesh Different thorns can be used to provide the same functionality, easily swapped.
- 15. Adaptive Mesh Refinement: Carpet Set of Cactus thorns Developed by Erik Schnetter Berger-Oliger style adaptive mesh refinement with sub- cycling in time High order differencing (4,6,8) Domain decomposition Hybrid MPI-OpenMP 2002-03: Design of Cactus means many groups, even competing ones, suddenly had AMR with little code change AEI (Rezzolla, Kaehler) E. Schnetter
- 17. Einstein Toolkit Consortium developing and supporting open software for relativistic astrophysics. provide the core computational tools to enable new science, broaden community, facilitate interdisciplinary research and take advantage of emerging petascale computers and advanced cyberinfrastructure.” Consortium: 126 members, 75 sites, 14 countries Sustainable community model: Maintainers across 6 sites: oversee technical developments, quality control, V&V, distributions /releases Whole consortium engaged in directions, support, development Open development meetings Six month releases http://www.einsteintoolkit.org
- 18. Components Currently 150 Cactus thorns: Initial data, evolution, analysis, AMR, … Tools, viz, etc Provide extensible standard interface for general relativity variables (e.g. variables, parameters, data model for output) Examples and tutorials Complete open production codes for black holes, neutron stars New users: Test account on supercomputer Community support: active mail list http://www.einsteintoolkit.org
- 21. New - DataVault Building community open data repository for numerical relativity data based on yt platform and whole tale Waveforms Simulation data Analysis scripts DNNs Desired features: Community driven Searchable Provenance info Reproducibility Citable Analysis
- 22. Computational Research Grid and distributed computing Automatic code generation Parallel I/O and checkpointing GPU computing Remote visualization Interactive steering Shared data repositories, reproducibility, citablity, etc
- 23. See electromagnetic waves Feel astro-particlesHear gravitational waves LIGO, VIRGO, eLISA JWST, DES, LSST IceCube (neutrinos) Multimessenger Astrophysics
- 26. Used Wolfram Language (Mathematica) based on MXNet. Tesla, GTX1080, and P100 GPUs (Innovative Systems Lab at NCSA) Simple designs: 3 convolutional layers and 2 fully connected layers. 26 Designing DNNs arXiv:1701.00008, Deep Neural Networks To Enable Real-time Multimessenger Astrophysics, Daniel George, Eliu A. Huerta
- 27. Real-time analysis (milliseconds). Thousands of inputs can be processed at once on a GPU. Dedicated inference chips can offer additional speed-up. 27 Speed Up arXiv:1701.00008, Deep Neural Networks To Enable Real-time Multimessenger Astrophysics, Daniel George, Eliu A. Huerta
- 28. 28 Detection & Parameter Estimation arXiv:1701.00008, Deep Neural Networks To Enable Real-time Multimessenger Astrophysics, Daniel George, Eliu A. Huerta
- 29. New Types of GWs Eccentric, Spinning Not included in training. Same accuracy of detection. DNNs learned to generalize. Missed by current methods. arXiv:1701.00008, Deep Neural Networks To Enable Real-time Multimessenger Astrophysics, Daniel George, Eliu A. Huerta
- 30. Future Directoins On-site GPU based analysis Direct stream to GPUs, continuously retrain with real-time noise characteristics Big data and new hardware Petascale data, DGX-1 at LIGO Hanford Extending to new signals 8-dimensions, eccentric, spin- precessing Distributed computing Einstein@home, MXNet, smartphones Future GW missions NANOGrav, eLISA Transient detection with telescopes DES, LSST, JWST, WFIRST Scope for improvements Larger template banks, non- Gaussian noise Deeper networks, complex designs, RNNs Multitask learning, Source modeling 30
- 31. Conclusion Open source toolkits such as the Cactus Framework and Einstein Toolkit developed over last 20 years have been essential to provide computational cyberinfrastructure and also to build communities Developing simulation software for supercomputers requires continuous attention to software design, optimization and scaling, data I/O etc. Community is now developing catalogues of simulated gravitational waveforms --- researchers need data science to curate, manipulate, reproduce, analysis, cite, etc. Deep convolutional networks showing great promise as alternative to conventional matched filter approach to provide new possibilities for real time MMA Same methodologies can be applied to other disciplines, crops-in-silico, learning sciences, etc.
- 32. NCSA Colloquia Series Started in 2014 to bring leaders in computational and data science to Illinois --- now 37 posted! Colloquia are all posted on-line via You-Tube Great speakers in many different disciplines! E.g. "Archiving Capacity and Data Infrastructure: Holes, Goals, Roles and Responsibility" — Margaret Hedstrom, University of Michigan “Big Data Visual Analysis” — Chris Johnson, University of Utah “Toward Reliable and Reproducible Inference in Big Data” -- Victoria Stodden, University of Illinois “From Data to Knowledge with Workflows and Provenance” -- Bertram Ludäscher, University of Illinois Search for NCSA Colloquia Series https://www.youtube.com/pla ylist? list=PLO8UWE9gZTlAgHZPaxQb pUNY0T26zeL_f
- 34. Open Discussion: Collaboration with International Partners
- 35. Open Discussion: Collaboration with International Partners Many discussions about how to collaborate Ideas: Find projects of mutual interest and identify groups to coordinate hackathons/bootcamps, follow up with visit National Data Service (Nationaldataservice.org) Whole Tale (wholetale.org), Brown Dog ( http://browndog.ncsa.illinois.edu) Data sets from Pakistan of interest to US researchers and v.v. Work with HEC and other agencies to support visits and educational fellowships E.g. Conacyt-Illinois model Special development or funding of online courses Would like to collect ideas and put a concept together
Notas del editor
- Science research needed from many disciplines Software and Hardware – software needs to be collaboratively developed and used, and need to be used on modern computing infrastructures (largest computers, clouds, multicore, mobile devices) Community – Need to build an inclusive community, which takes into account cultures, and prepares new workforce Outreach – Work to societal impact and public outreach, open science and accessibity is important part of this
- Positive side – this is similar in all disciplines, and underlying numerical methods are the same Increased fidelity for modeling complex problems depends on multiphysics and multidisciplinary coupling as well as resolution and accuracy of component solvers Challenges typical monolithic software packages, need attention to coupling, potentially refactoring to enable more modularity
- Summer Schools Undergraduate Research Open codes
- The future of astronomy is multimessenger astrophysics. This means that we observe events through multiple messenger such as gravitational waves, light, neutrinos cosmic rays etc. We want to hear events first through GWs (like LIGO) then turn our telescopes (such as the upcoming NASA JWST) around to see it, and then feel it via astro-particles. Getting all this different information simultaneously would lead to groundbreaking insights about the nature of the universe. Mention Ed was involved in LIGO and IceCube.
- GWs are very weak. We got lucky with the first detection. It was very loud. However, the 2nd detection and the majority of expected signals are very weak as shown below. They don’t even show up in a spectrogram. So how do we detect and extract parameters of these signals which are much weaker than the background noise? The current approach used by LIGO for weak signals is template-matching also called matched-filtering. A template bank of about 300,000 signals is used. This is so computationally expensive that current analysis is limited to a small class of signals. Furthermore it takes several days to accurately reconstruct the parameters of an event. So is there a better way to this so that we can quickly find GW events and look at it through our telescopes to enable this scenario of multimessenger astrophysics?
- To give an analogy to current LIGO analysis methods. Supposed you wanted to discover words in a stream of characters. What LIGO does is similar to comparing every subsequence with every word in a dictionary (template bank). However, notice that we are able to pick out these words immediately. Our brains do not compare against a database of all words. This suggests that the same approach can work for GW analysis by using deep learning. The template bank can be used once for training a deep neural network, after which it will be able to recognize signals instantly.
- We used the new DNN functionality in Mathematica which was an added layer over MXNet. We explored only simple designs of DNNs and still achieved excellent results. There is a lot of scope for finding better designs For example recurrent neural networks, particularly LSTM networks, are promising as they have been shown to be superior for voice recognition tasks (which is closely related to GW analysis)
- Much faster than matched-filtering. Takes only milliseconds. Can be run on a laptop or smartphone. Template banks were GBs but the DNN was only 4MB. This means it learned patterns in the data. This is actually a lower-bound since we did not account for several additional steps needed for matched-filtering such as FFTs, aligning the signal templates, etc. In practice, DNNs would be much more faster.
- Both classification and regression. Outperformed all other methods by a huge margin.
- What was most surprising was these DNNs were able to detect completely different kinds of signals even though we did not use it for training. We only expected interpolation, but this was able to extrapolate. Current matched-filtering pipelines would miss these. Emphasize that these kinds of signals were not at all included in the training process. The same accuracy for detection but slightly larger errors for parameter estimation. Our plan is to generate catalogs of eccentric simulations for training to further improve this accuracy.
- We are working on generating catalog of eccentric simulations on Blue Waters We are using the DGX-1 at LIGO Hanford Lab, to build a production-grade pipeline which will be continuously retrained with the latest LIGO data There is also no limit to the size of template banks that can be used for training. So we can extend this to all types of GW events, which is not possible with current matched-filtering methods. Same technique can be applied to detect transients in image data. So we can have a unified deep learning framework for analyzing data from all observation instruments to enable multimessenger astrophysics. Multitask learning allows a single DNN to perform detection, classification to sub-categories, and parameter estimation.