Computing Sciences Summer Students: 2019 Talks & Events
Who: Julian Borrill
When: June 06, 11 am - 12 pm
Abstract: The Cosmic Microwave Background (CMB) is the last echo of the Big Bang, and carries within it the imprint of the entire history of the Universe. Decoding this preposterously faint signal requires us to gather every-increasing volumes of data and reduce them on the most powerful high performance computing (HPC) resources available to us at any epoch. In this talk I will describe the challenge of CMB analysis in an evolving HPC landscape.
Bio: Julian Borrill is the Group Leader of the Computational Cosmology Center at LBNL. His work is focused on developing and deploying the high performance computing tools needed to simulate and analyse the huge data sets being gathered by current Cosmic Microwave Background (CMB) polarization experiments, and extending these to coming generations of both experiment and supercomputer. For the last 15 years he has also managed the CMB community and Planck-specific HPC resources at the DOE's National Energy Research Scientific Computing Center.
Who: Rebecca Hartman-Baker
When: June 11, 11 am - 12 pm
Abstract: What is High-Performance Computing (and storage!), or HPC? Who uses it and why? We'll talk about these questions as well as what makes a Supercomputer so super and what's so big about scientific Big Data. Finally we'll discuss the challenges facing system designers and application scientists as we move into the many-core era of HPC. TENTATIVE: After the presentation, we'll take a tour of the NERSC machine room. Closed-toe shoes are REQUIRED for the machine room tour.
Bio: Rebecca Hartman-Baker leads the User Engagement Group at NERSC, where she is responsible for engagement with the NERSC user community to increase user productivity via advocacy, support, training, and the provisioning of usable computing environments. She began her career at Oak Ridge National Laboratory, where she worked as a postdoc and then as a scientific computing liaison in the Oak Ridge Leadership Computing Facility. Before joining NERSC in 2015, she worked at the Pawsey Supercomputing Centre in Australia, where she coached two teams to the Student Cluster Competition at the annual Supercomputing conference, led the HPC training program for a time, and was in charge of the decision-making process for determining the architecture of the petascale supercomputer installed there in 2014. Rebecca earned a PhD in Computer Science from the University of Illinois at Urbana-Champaign.
Who: Jonathan Carter
When: June 13, 11:00 am - 12:00 pm
Abstract: During the poster session on August 1st, members of our summer visitor program will get the opportunity to showcase the work and research they have been doing this summer. Perhaps some of you have presented posters before, perhaps not. This talk will cover the basics of poster presentation: designing an attractive format; how to present your information clearly; what to include and what not to include. Presenting a poster is different from writing a report or giving a presentation. This talk will cover the differences, and suggest ways to avoid common pitfalls and make poster sessions work more effectively for you.
Bio: Before assuming the Deputy role for CS, Dr. Carter was leader of the NERSC User Services Group (USG). He joined NERSC as a consultant in USG at the end of 1996, helping users learn to effectively use the computing systems. He became leader of USG at the end of 2005. Carter maintains an active interest in algorithms and architectures for high-end computing, and participates in benchmarking and procurement activities to deploy new systems for NERSC. In collaboration with the Future Technologies Group in CRD, and the NERSC Advanced Technology Group, he has published several architecture evaluation studies, and looked at what it takes to move common simulation algorithms to exotic architectures. His applications work on the Japanese Earth Simulator earned him a nomination as Gordon Bell Prize finalist in 2005. Prior to LBNL, Dr. Carter worked at the IBM Almaden Research Center as a developer of computational chemistry methods and software, and as a researcher of chemical problems of interest to IBM. He holds a Ph.D. and B.S in chemistry from the University of Sheffield, UK, and performed postdoctoral work at the University of British Columbia, Canada.
Who: Johannes Blaschke
When: June 20, 11 am - 12 pm
Abstract: Many problems in science and engineering involve the interaction of fluids with solid particles. Here we will look at two applications of particle-laden flows: chemical-looping reactors, and self-propelled active particles. Chemical-looping reactors enable zero-emission combustion of fossil-fuels; while active particles are promising building blocks for nano-materials. Yet both of these applications are too complex to be described by existing theoretical models, requiring large-scale computer simulations to plan future prototypes and help guide design decisions. Here we will review some of the challenges involved with simulating these systems, and review the methods used to tackle such problems. Finally, we will take an in-depth look at how hydrodynamic interactions drastically influence the collective behavior active particle suspensions.
Bio: Johannes Blaschke is a postdoctoral researcher in the Computational Research Division at Berkeley Lab. His work focuses on large-scale simulations of non-linear systems in the field of hydrodynamics, soft matter, biophysics, and statistical mechanics. Johannes received his PhD in Theoretical Physics from the University of Goettingen, while conducting fundamental statistical mechanics research at the Max Planck Institute for Dynamics and Self-Organization. After his PhD, Johannes did a postdoc researching biological self-propelled particles at the Technical University of Berlin. In 2017 Johannes joined the Center for Computational Sciences and Engineering.
Who: Bert de Jong
When: June 24, 11 am - 12 pm
Abstract: You probably have seen the excitement and heard about quantum computing. But, you may be curious what it is, and how it may impact research and scientific discovery in the future. You may even wonder what the job prospects might be in this field. Lawrence Berkeley National Laboratory has a significant effort in quantum computing, from hardware development to computer science and applied mathematics to algorithm development for various scientific domains. I will give a little bit of a background and then go into where we are currently with quantum computing.
Bio: Bert de Jong is a Senior Scientist at Lawrence Berkeley National Laboratory, where he leads the Computational Chemistry, Materials, and Climate Group. Focus areas in his group include quantum computing, high-performance and exascale computing as well as machine learning for chemical and materials sciences. de Jong is the director of the LBNL Quantum Algorithms Team, funded by DOE ASCR, focused on developing algorithms and computer science and applied mathematics solutions for chemical sciences and other fields on near-term quantum computing devices. He is also part of LBNL’s superconducting qubits quantum test bed, and the LBNL lead for the Basic Energy Sciences Quantum Information Sciences project. In addition, he is a co-PI on an LBNL led HEP funded quantum information science projects. beyond quantum, de Jong is a co-PI within the DOE ASCR Exascale Computing Project (ECP) and DOE Basic Energy Sciences SPEC Computational Chemistry Center, and is leading LBNL funded efforts on machine learning for biochemical sciences. de Jong has published over 120 papers and book chapters with over 5200 citations and an H-Index of 32. He published one edited book, and has given over 80 invited presentations and lectures at international conferences and universities. de Jong is the Founding Editor-in-Chief for the IOP journal Electronic Structure, and a Specialist Editor for Computer Physics Communications.
Who: Helen He
When: June 24, 2:00 pm - 4:00 pm
Abstract: This class will provide an informative overview to acquaint students with the basics of NERSC computational systems and its programming environment. Topics include: systems overview, connecting to NERSC, software environment, file systems and data management / transfer, and available data analytics software and services. More details on how to compile applications and run jobs on NERSC Cori/Edison will be presented including hands-on exercises. The class will also showcase various online resources that are available on NERSC web pages. (Students should bring their laptops.)
Bio: Helen is a High Performance Computing consultant of the User Engagement Group at NERSC. She has been the main point of contact among users, system people, and vendors, for the Cray XT4 (Franklin), XE6 (Hopper) systems, and XC40 (Cori) systems at NERSC in the past 10 years. Helen has worked on investigating how large scale scientific applications can be run effectively and efficiently on massively parallel supercomputers: design parallel algorithms, develop and implement computing technologies for science applications. She provides support for climate users and some of her experiences include software programming environment, parallel programming paradigms such as MPI and OpenMP, scientific applications porting and benchmarking, distributed components coupling libraries, and climate models.
Who: Rebecca Hartman-Baker
When: June 26, 9:00 am - 1200 pm & 2:00 pm - 5:00 pm
Abstract: In this course, students will learn to write parallel programs that can be run on a supercomputer. We begin by discussing the concepts of parallelization before introducing MPI and OpenMP, the two leading parallel programming libraries. Finally, the students will put together all the concepts from the class by programming, compiling, and running a parallel code on one of the NERSC supercomputers. (Students should bring their laptops.)
Bio: Rebecca Hartman-Baker is the acting leader of the User Engagement Group at NERSC. She got her start in HPC as a graduate student at the University of Illinois, where she worked as a graduate research assistant at NCSA. After earning her PhD in Computer Science, she worked at Oak Ridge National Laboratory in Tennessee and the Pawsey Supercomputing Centre in Australia before joining NERSC in 2015. Rebecca's expertise lies in the development of scalable parallel algorithms for the petascale and beyond.
Who: Suren Byna
When: June 27, 11 am - 12 pm
Abstract: Science is driven by massive amounts of data. This talk will review data management techniques used on large-scale supercomputing systems. The topics include: Efficient strategies for storing and loading data to and from parallel file systems, querying data using array abstractions, and object storage for supercomputing systems.
Bio: Suren Byna is a Staff Scientist in the Scientific Data Management (SDM) Group in CRD @ LBNL. His research interests are in scalable scientific data management. More specifically, he works on optimizing parallel I/O and on developing systems for managing scientific data. He is the PI of the ECP funded ExaHDF5 project, and ASCR funded object-centric data management systems (Proactive Data Containers - PDC) and experimental and observational data management (EOD-HDF5) projects.
Who: Mariam Kiran
When: July 02, 11 am - 12 pm
Abstract: I will talk about how we have been exploring novel machine learning techniques to allow network infrastructures to learn. We have been developing time-series prediction libraries, reinforcement learning techniques and advanced classification techniques which can help identify good versus bad performance in real-time. We aim to use these to improve how our complicated networking infrastructures are managed and can be used to improve science throughput to 100%, enabling science without boundaries of geography.
Bio: Mariam’s research focuses on learning and decentralized optimization of system architectures and algorithms for high performance computing, underlying networks and Cloud infrastructures. She has been exploring various platforms such as HPC grids, GPUs, Cloud and SDN-related technologies. Her work involves optimization of QoS, performance using parallelization algorithms and software engineering principles to solve complex data intensive problems such as large-scale complex simulations. Over the years, she has been working with biologists, economists, social scientists, building tools and performing optimization of architectures for multiple problems in their domain.
Who: Dan Martin
When: July 11, 11 am - 12 pm
Abstract: The response of the Antarctic Ice Sheet (AIS) remains the largest uncertainty in projections of sea level rise. The AIS (particularly in West Antarctica) is believed to be vulnerable to collapse driven by warm-water incursion under ice shelves, which causes a loss of buttressing, subsequent grounding-line retreat, and large (up to 4m) contributions to sea level rise. Understanding the response of the Earth's ice sheets to forcing from a changing climate has required the development of a new generation of next-generation ice sheet models which are much more accurate, scalable, and sophisticated than their predecessors. For example very fine (finer than 1km) spatial resolution is needed to resolve ice dynamics around shear margins and grounding lines (the point at which grounded ice begins to float). The LBL-developed BISICLES ice sheet model uses adaptive mesh refinement (AMR) to enable sufficiently-resolved modeling of full-continent Antarctic ice sheet response to climate forcing. This talk will discuss recent progress and challenges modeling the sometimes-dramatic response of the ice sheet to climate forcing using AMR.
Bio: Dan Martin is a computational scientist and group leader for the Applied Numerical Algorithms Group at Lawrence Berkeley National Laboratory. After earning his PhD in mechanical engineering from U.C. Berkeley, Dan joined ANAG and LBL as a post-doc in 1998. He has published in a broad range of application areas including projection methods for incompressible flow, adaptive methods for MHD, phase-field dynamics in materials, and Ice sheet modeling. His research involves development of algorithms and software for solving systems of PDEs using adaptive mesh refinement (AMR) finite volume schemes, high (4th)-order finite volume schemes for conservation laws on mapped meshes, and Chombo development and support. Current applications of interest are developing the BISICLES AMR ice sheet model as a part of the SCIDAC-funded ProSPect application partnership, and some development work related to the COGENT gyrokinetic modeling code, which is being developed in partnership with Lawrence Livermore National Laboratory as a part of the Edge Simulation Laboratory (ESL) collaboration.
Toward the Systematic Generation of Hypothetical Atomic Structures: Geometric Motifs and Neural Networks
Who: Tess Smidt
When: July 18, 11 am - 12 pm
Abstract: Materials discovery, a multidisciplinary process, now increasingly relies on computational methods. We can now rapidly screen materials for desirable properties by searching materials databases and performing high-throughput first-principles calculations. However, high-throughput computational materials discovery pipelines are bottlenecked by our ability to hypothesize new structures, as these approaches to materials discovery often presuppose that a material already exists and is awaiting identification. In contrast to this assumption, synthesis efforts regularly yield materials that differ substantially from the structures in databases of previously known materials. In this talk, we discuss strategies for generating hypothetical atomic structures using the concepts of geometric motifs (the recurring patterns of atoms in materials) and neural networks that can manipulate discrete geometry (tensor field networks).
Bio: Tess Smidt is the 2018 Alvarez Postdoctoral Fellow in Computing Sciences. Her current research interests include intelligent computational materials discovery and deep learning for atomic systems. She is currently designing algorithms that can propose new hypothetical atomic structures. Tess earned her PhD in physics from UC Berkeley in 2018 working with Professor Jeffrey B. Neaton. As a graduate student, she used quantum mechanical calculations to understand and systematically design the geometry and corresponding electronic properties of atomic systems. During her PhD, Tess spent a year as an intern on Google’s Accelerated Science Team where she developed a new type of convolutional neural network, called Tensor Field Networks, that can naturally handle 3D geometry and properties of physical systems. As an undergraduate at MIT, Tess engineered giant neutrino detectors in Professor Janet Conrad's group and created a permanent science-art installation on MIT's campus called the Cosmic Ray Chandeliers, which illuminate upon detecting cosmic-ray muons.
When: July 23, 11 am - 12 pm
Abstract: This talk will review progress in Artificial Intelligence (AI) and Deep Learning (DL) systems in recent decades. We will cover successful applications of DL in the commercial world. Closer to home, we will review NERSC’s efforts in deploying DL tools on HPC resources, and success stories across a range of scientific domains. We will touch upon the frontier of open research/production challenges and conjecture about the role of humans (vis-a-vis AI) in the future of scientific discovery. (Familiarity with JARVIS and Iron Man Mk I-VI armor series will greatly enhance your understanding of this presentation. Background viewing material includes any one of the Iron Man movies, or Avengers Endgame.)
Bio: Prabhat leads the fantastic Data and Analytics Services team at NERSC. His current research interests include scientific data management, parallel I/O, high performance computing and scientific visualization. He is also interested in applied statistics, machine learning, computer graphics and computer vision. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley.
Who: Juliane Mueller
When: July 25, 11 am - 12 pm
Abstract: Simulation models are used by scientists in various disciplines, e.g., climate, cosmology, engineering, materials science, to approximate physical phenomena that could otherwise not be studied. Every simulation model contains parameters that must be optimized with some objective in mind. For example, the goal (the objective function) might be to tune the parameters such that the error between the simulation model’s output and the observation data is minimized or such that the resulting design of an airfoil maximizes its lift. However, the difficulty is that simulation models take a long time to run and we generally do not have an algebraic description of the objective function. This makes the parameter optimization task extremely challenging. In this talk, I will give a high level overview of how these problems can be efficiently and effectively solved using surrogate models and, if time allows, I will briefly talk about some application problems.
Bio: Juliane Mueller has been a research scientist in the Computational Research Division at Berkeley Lab since 2017. Her work focuses on the development of algorithms for a special class of optimization problems. Juliane received her Ph.D. in Applied Mathematics from Tampere University of Technology in 2012 after which she did a postdoc at Cornell University. Juliane joined Berkeley Lab as Alvarez Fellow in 2014.
Who: Tom Scarvie, Simon Leeman and Jonah Weber
When: Jul 24, 3-4 pm
Where: Advanced Light Source (ALS) Facility, building 6
The ALS is a third-generation synchrotron facility that hosts experiments in a wide variety of fields. Research done at the ALS ranges from the determination of molecular structures of human antibodies bound to a respiratory virus protein to the chemistry of the durable concrete used by Romans 2000 years ago.
Who: Hari Krishnan
When: Jul 30, 2-3 pm
Abstract: Large DOE research projects often require complex and stringent constraints on resources whether human or computational. Handling data acquired from experimental, observed, or simulated sources and running analysis on heterogeneous architectures requires comprehensive understanding of the analysis and data movement process. The work presented in this talk is developed by The Center for Advanced Mathematics for Energy Research Applications (CAMERA) in collaboration with the DOE light sources to investigate approaches for running complex algorithms over current and future data rates and volumes on a distributed heterogeneous computing landscape. The presentation will highlight efforts developed to address technical challenges in deploying and executing a diverse set of distributed end to end pipelines optimized for realtime processing and feedback. Additionally, the talk will cover topics on developing workflows that support data lifecycle management, programmable analysis, and reformulating algorithms to produce results at or near realtime for enabling exploratory analysis.
Bio: Hari Krishnan has a Ph.D. in Computer Science and works for the visualization and graphics group as a computer systems engineer at Lawrence Berkeley National Laboratory. As a member of The Center for Advanced Mathematics for Energy Research Applications (CAMERA), he leads the software infrastructure team, accelerates image analysis algorithms, and works on reconstruction algorithms. Additionally, his research focuses on scientific visualization on HPC platforms and many-core architectures. He leads the development effort on several HPC related projects which include research on new visualization methods, optimizing scaling and performance on NERSC machines, working on data model optimized I/O libraries and enabling remote workflow services. He is also an active developer of several major open source projects which include Xi-CAM, CAM-Link, SHARP, VisIt, ICE, Akuna, and has developed plugins that support performance-centric scalable image filtering and analysis routines in Fiji/ImageJ.