1. BNL - Computational Science Initiative (CSI)  Kerstin Kleese van Dam

2. Established in 2015  Integration of computation capabilities across BNL under one Umbrella  Vision: Recognized leader in data driven- discovery - translating leading computer science and mathematics research into measurable improved scientific discovery processes.

3. Computer Science & Mathematics (CSM) Barbara Chapman, Technical Director Data Science, HPC, Applied Mathematics Research Computational Science Laboratory (CSL) Nicholas D’ Imperio, Director Collaborative Laboratory for Advanced Algorithm Development and Optimization - bringing together HPC, Math and Domain Science Expertise Center for Data Driven Discovery (C3D) Kerstin Kleese – Director (interim) Multidisciplinary Center for the Development, Deployment and Operation of Data Driven Discovery Services Approved: BNL Scientific Data & Computing Center (BSDCC) Eric Lancon, Director Incl. RACF**, Institutional Computer, C3D Systems,… Kerstin Kleese van Dam Date Reports to Robert Tribble, Deputy Director Science &Technology ** Matrixed from the Physics Department *** Provides enabling capabilities to CSI COMPUTATIONAL SCIENCE INITIATIVE Kerstin Kleese van Dam, Director* Michael Ernst, Deputy Director Robert Harrison, Chief Scientist Lauri Peragine, Administrative Assistant

4. Core Scientific Focus Areas  Streaming Analysis of Experimental, Observational and Modeling Results  Numerical Modeling at the extreme Scale in Support of Experimental Planning, Steering and Analysis  Multi-Source Streaming Data Analysis (Complex Modeling)  Active reuse of Knowledge Repositories as part of Streaming Analysis Infrastructures  Nano-technology inspired Challenges for Future Computing Technologies

5. BNL Scientific Data and Computing Center  World class scientific computing center operation provided by the RACF team for all scientific computing at BNL and for its partners.  High Throughput Computing  High Performance Computing  Data Intensive Computing  Fast Data Storage  Petascale Data Archive  Fast Networking Michael Ernst Eric Lancon

6. Computational Science and Mathematics  Reliable, high performance data analysis solutions require and integrated approach, from system architectures to the application level  Leading Edge Research in HPC and Data Science Technologies incl.:  Programming Models, Runtime Systems and Compilers, Novel Architectures  Resource Allocation  Applied Mathematics - Particle Methods Machine Learning, Linear Solvers  Visual Analytics  Fast Network Protocols Barbara Chapman

7. Computational Science Laboratory  Collaborative Center in support of advanced Numerical Modeling  User Support  Code Optimization  Scalable Mathematical Libraries  Development of scalable Algorithms  Benchmarking  NVIDIA GPU Research Center 2016 Nick D’Imperio

8. Computational Science in Support oft Experimental Design and Analysis (3)  Multi-Source Analysis - DiffPy-CMI enabled the integrated analysis of experimental results from different instrument types and computational simulations to create a full structural analysis of complex, real life materials.  Correlative Device Performance Analysis - Atomistic modeling is used to correlate the materials structure to the in operando behavior of a device, by creating theoretical spectra and comparing them measured spectra.

9. Computational Science Support for Experimental Design and Analysis (3)  Parallel Python for NSLS II Data Analysis - Algorithm for the study of time autocorrelation of the speckle pattern resulting from coherent x-ray Scattering (CHX). Creating a parallel, streaming code base optimized for state of the art architectures.  TomoToolkit - Portable, Open- Source Tomography Toolkit. Tools includes TomoPy, tomo-display etc. Using a docker image that encapsulates all the library dependencies makes it possible to run the same program across different platforms, including cloud computers.

10. Center for Data Driven Discovery  Collaboration Center for the Development of Big Data Methods, Libraries, Tools and Services for Experimental, Observational and Computational Data Environments  Image Analysis  Streaming Data Analysis  Multi-Source Data Analysis  Provenance  Long Term Data Curation

11. Deep Learning for NSLS II - start 10/15 Facilities Supported - NSLS II, APS, ALS BNL Beamlines: CHX, HXN, FXI, CMS Low-level: identifying characteristic features in a diffraction image; Intermediate-level: detecting the occurrence of a physical process from a sequence of images; High-level: learning and predicting scientifically-meaningful trends. Current Results - 5 Layer CNN for scattering image recognition, 84% accuracy Deliverable: Deep-Learning Notebooks for users to recognize scattering image, correlate Phase with experimental configuration, and automatically navigate through the parameter space.

12. Dynamic Visualization and Visual Analytics for Scientific Data of NSLS- II and CFN - start 10/15  Facilities Supported: NSLS II - HXN, CFN  Progress to date:  Cross-modal image recognition (HXN), developing the interactive tool generating suggested locations of target objects (current accuracy top 1%)  Color Mapping (HXN) - expressing artifact similarities with color maps  Analysis parameter comparison (CFN/CMS), investigating display methods for correlations and patterns in analysis parameters.  Multi-level display of scattering image set with interaction (CFN/CMS) SEM scan Fluorescence

13. One Time Correlation Analysis for NSLS II  Facilities Supported: NSLS II CSX-1  Software tools to perform one time correlation analysis on long time series images acquired at variable time lags.  Progress to Date: 95% completed, Tool will be available in June 2016.  Future work: Deliver real time streaming one time and two time analysis pipeline - in collaboration with CSL team.

14. Machine Learning Assisted Materials Discovery - Start 11/15  Facilities Supported: TEM at CFN, XAS at NSLS II  Deliverable: (semi) automated quantitative image analysis tool to process high noise, non-uniform substrate and brightness, and subtle changes  Current Status:  Preliminary analysis of detection and tracking  Developed streaming dimensionality reduction and clustering analysis algorithm  3 publications

15. Summary  New organizational Structure in Place  Key requirements and priorities identified  Restructured Project Portfolio has started  Close collaboration with NSLS-II DAMA group and growing collaboration with CAMERA  Happy to Collaborate