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Computational Science and Engineering at Berkeley

Computational Science and Engineering at Berkeley. Jim Demmel EECS & Math Departments www.cs.berkeley.edu/~demmel 20 Jan 2009. 4 Big Events. Establishment of a new graduate program in Computational Science and Engineering (CSE)

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Computational Science and Engineering at Berkeley

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  1. Computational Science and Engineering at Berkeley Jim Demmel EECS & Math Departments www.cs.berkeley.edu/~demmel 20 Jan 2009

  2. 4 Big Events • Establishment of a new graduate program in Computational Science and Engineering (CSE) • “Multicore revolution”, requiring all software (where performance matters!) to change • ParLab • New Buildings to house research activities • CITRIS and CRT • Cloud computing • RadLab

  3. Outline • New “Designated Emphasis” in CSE • Goals • Participants (112 faculty – so far) • Resources and Opportunities • Course Structure • A few research projects

  4. Designated Emphasis (DE) in CSE • New “graduate minor” – started July 1, 2008 • Motivation • Widespread need to train PhD students in CSE • Opportunities for collaboration, across campus and at LBNL • 18 (20) departments, 85 (112) faculty signed up (so far) • Graduate students participate by • Getting accepted into existing department/program • Taking CSE course requirements • Qualifying examination with CSE component • Thesis with CSE component • Receive “PhD in X with a DE in CSE” • Details at cse.berkeley.edu

  5. Participating Departments (1/2) ( # faculty by “primary affiliation”, # courses ) • Astronomy (7,3) • Bioengineering (3,1) • Biostatistics (2,0) • Chemical Engineering (6,0) • Chemistry (8,1) • Civil and Environmental Engineering (7,8) • Earth and Planetary Science (6,3) • EECS (19,14) • IEOR (5,5) • School of Information (1,0)

  6. Participating Departments (2/2) ( # faculty by “primary affiliation”, # courses ) • Integrative Biology (1,0) • Materials Science and Engineering (2,1) • Mathematics (15, 4) • Mechanical Engineering (9, 6) • Neuroscience (7,1) • Nuclear Engineering (2,1) • Physics (1,1) • Political Science (2,0) • Statistics (5, 11) • New: Biostatistics, Public Health

  7. Resources (1/4) • Executive Director Masoud Nikravesh • Money • Annual for staff support • One time, for course development • CITRIS • Research initiation funds • Access to corporate partners • GSI support, broadcast to other campuses

  8. Resources (2/4) • Space • CITRIS Building - early 2009 • staff offices, seminar, machine room (1400 sq ft) • LBNL CRT Building - 2011

  9. Resources (3/4) • Existing computing resources • EECS clusters - www.millennium.berkeley.edu • Getting old • LBNL / NERSC • CS267 class accounts • Start-up allocations on supercomputers • Needs to be of potential interest to DOE • Potential new resources • Yahoo, …

  10. Resources (4/4) • LBNL • Besides space and cycles: Expert CSE advice, collaborators, short courses • Internships for students (grad or undergrad) • MSRI • Workshops, short courses

  11. Course Structure • 3 kinds of students, course requirements • CS , Math, “Applications” • Each kind of student has 3 course requirements in other two fields • Goal: enforce cross-disciplinary training • Non-CS & Non-Math students: • 1 or 2 Math courses from list • 1 or 2 EECS courses from list • Other classes from Math, Stat, IEOR • Math & CS students: substitute 1 or 2 courses from “applied” department for 1 or 2 inside • May distinguish EECS and CS students • We have $ to support new course development

  12. EECS Courses (so far) • CS267 – Applications of Parallel Computers • CS270 – Combinatorial Algs. & Data Structures • CS274 – Computational Geometry • CS280 – Computer Vision • CS281A – Statistical Learning Theory • CS281B – Learning and Decision Making • CS284 – Geometric Design and Modeling • CS285 – Solid Modeling and Fabrication • CS294-10 – Visualization • EECS225AB– Digital Signal and Image Processing • EECS227A – Convex Optimization • EECS228B – Convex Approximation

  13. Other Computational Courses (1/6) • Mathematics • Ma 221 - Numerical Linear Algebra • Ma 228AB - Numerical Solution of Differential Equations • Ma 220 - Probabilistic Methods • New course being developed • Industrial Engineering and Operations Research • IEOR 261:  Experimenting with Simulated Systems • IEOR 262AB:  Mathematical Programming • IEOR 264:  Computational Optimization • IEOR 269  Integer Programming and Combinatorial Optimization

  14. Other Computational Courses (2/6) • Statistics • Stat 215AB: Statistical Models: Theory and Application • Stat 230A: Linear Models • Stat 232: Experimental Design • Stat 240: Nonparametric and Robust Methods • Stat 241A: Statistical Learning Theory (same as CS281A) • Stat 241B: Advanced Topics in Learning and Decision Making (same as CS281B) • Stat 244: Statistical Computing • Stat 245AB: Biostatistical Methods • Stat 246: Statistical Genetics • Stat 248: Time Series Analysis

  15. Other Computational Courses (3/6) • Astronomy • Astro 202: Astrophysical Fluid Dynamics • Astro 204: Numerical Techniques in Astronomy • Astro 255: Computational Methods in Theoretical Astrophysics • Bioengineering • BE 243: Computational Methods in Biology • Chemistry • Chem 220AB: Statistical Mechanics • Chem 221AB: Advanced Quantum Mechanics • Chem 295: Molecular Simulation

  16. Other Computational Courses (4/6) • Civil and Environmental Engineering • CEE 200A: Environmental Fluid Mechanics • CEE 200B: Numerical Modeling of Environmental Flows • CEE 221: Nonlinear Structural Analysis • CEE 222: Finite Element Methods • CEE 224: Computer Aided Engineering • CEE 229: Structural System Reliability • CEE 233: Computational Mechanics • CEE 234: Computational Inelasticity • Earth and Planetary Science • EPS 204: Elastic Wave Propagation • EPS 206: Geophysical Inverse Methods • EPS 236: Geological Fluid Mechanics

  17. Other Computational Courses (5/6) • Electrical Engineering and Computer Sciences • EECS 219A: Computer-Aided Verification of Electronic Circuits and Systems • Engineering • E 266A: Finite Difference Methods for Fluid Dynamics • E 266B: Spectral Methods for Fluid Dynamics • Material Science and Engineering • MSE 215: Introduction to Computational Materials Science • Mechanical Engineering • ME 280A: Introduction to the Finite Element Method • ME 280B: Finite Element Methods in Nonlinear Continua • ME 287: Multiscale Modeling the Design of New Materials • ME 290D: Solid Modeling

  18. Other Computational Courses (6/6) • Molecular and Cell Biology • C 246: Topics in Computational Biology and Genomics • MCB 262: Computational Neuroscience • Nuclear Engineering • NUC 255: Numerical Methods for Reactor Analysis • Physics • C 203 – Computational Nanoscience • Plant and Microbial Biology • PMB 200B: Genomics and Computational Biology

  19. More on possible new courses (1/6) • Possible obstacles to students • Long prerequisite chains • Important material spread over multiple courses • Repetition of basic material in multiple courses • 23 faculty identified following needs, opportunities: • New 3 unit survey course in numerical methods • 23 new 1-unit classes with this as prerequisite • “Segmented courses” where one can take subset for fewer units • www.cs.berkeley.edu/~demmel/IGERT06_Curriculum.pdf

  20. More on possible new courses (2/6) • Numerical Methods Course (3 units) • Boil down Math221, Ma228AB, other material into one • Possible text by Heath (UIUC) • Linear & nonlinear equations • Eigenvalue/vector problems • Optimization • Inverse problems • Numerical integration, interpolation • FFT • ODEs, PDEs

  21. More on possible new courses (3/6) • Possible new 1 unit courses • Application of spectral methods to fluid flows • Boundary element methods • Climate modeling • Collisionless shock waves and PIC plasma simulation • Computational Astrophysics • Contact Mechanics • Finite Elements • Boundary integral methods for elliptic PDE • Finite dimension (control) systems

  22. More on possible new courses (4/6) • More possible new 1 unit courses • Fourier transforms and wavelets • Imaging, color issues, multidimensional PDFs • Interface techniques and level sets • Large eddy simulations • Lattice-Boltzmann Methods • Maximum likelihood, least squares, median fitting • Mesoscale atmospheric boundary layer modeling • Monte Carlo methods • Multiscale modeling and design of new materials

  23. More on possible new courses (5/6) • Still more possible new 1 unit courses • Numerical optimization • Principal component analysis of climate data • Sparse matrix computations • Turbulence modeling for stratified flows • Visualization

  24. More on possible new courses (6/6) • Segmented 3-unit courses • Parallel Computing (CS267) • Numerical solution of differential equations (Ma228AB) • At least $50K available to support new course development (thanks to Deans Richards & Sastry)

  25. Possible Research Topics (1/2) • A small selection from among the 112 faculty • Some require tightly coupled computing, some ok on “cloud” • Astronomy (10 faculty) • Some simulations (large scale, many smaller scale), some large data sets (up to terabytes/day) • Chemistry and Chemical Engineering (12 faculty) • Some large-scale simulations, some less tightly coupled • Ex: New materials for energy via QMC, chemical database screening • Neuroscience and Cognitive Computing (8 faculty) • Some large scale simulations (of brain, auditory system) • Some large data set analysis (crcns.org)

  26. Possible Research Topics (2/2) • Computational systems biology (9 faculty) • “Digital Human”, many layers of simulation • Econ/EECS/IEOR/Math/PoliSci/Stat (9 faculty) • Statistical analysis and visualization of large scale heterogeneous data bases of economic, financial, social data • Ex: statnews.eecs.berkeley.edu/about/project for news analysis • Economics (8 faculty, including 1 Nobelist) • Econometric and social modeling • Ultra-efficient Climate Computer (7 faculty + staff) • Joint with LBNL • 100x lower power than current supercomputers

  27. Extra Slides

  28. Challenges/opportunities for using Clouds for HPC • Need to gang schedule processors • Batch schedulers for clusters well understood, but need to run in cloud environment • Need interface to run MPI jobs • Autotuning • Patterns/Motifs/Dwarfs for Clouds • Picking best algorithm no matter which resources • Impact of likely higher latency, lower bandwidth • Research in novel “communication-avoiding” algorithms • Some jobs access “large” databases • Up to terabytes/day generated

  29. Managing the DE • Standard By-Laws in Sec. E of proposal • Nominating Committee • Executive Committee • Admissions Committee • Curriculum Committee • Graduate Advising Committee

  30. Managing the DE • Nominating Committee • Propose candidates for annual election of Exec Comm • L&S Dean Mark Richards, COE Dean Shankar Sastry, Jim Demmel

  31. Managing the DE • Executive Committee • 5 members: 1 Math, 1 CS, 3 other, elected annually • 2 year and 1 year terms • Anyone could nominate – deadline was 5/23 • Final List of Nominees • Jim Demmel – CS (joint appt in Math) - Chair • James Sethian – Math • Jonathan Arons – Astronomy • Martin Head-Gordon – Chemistry • Tarek Zohdi – Mechanical Engineering • Balloting by email (was distributed June 9), due June 13 • Election completed, 68 in favor, none opposed (6/23)

  32. Managing the DE • Executive Committee (continued) • Duties • Review faculty membership, program affiliations • Nominate Head Graduate Advisor to Graduate Dean • Manage website design • Appoint other committee members • Make trains run on time Thanks for volunteering 

  33. Managing the DE • Admissions Committee • Establish student admissions criteria • Review applicants, recommend to Head Graduate Advisor for admission • Recruitment

  34. Managing the DE • Curriculum Committee • Review and update list of courses • Review qualifying exam and dissertation requirements • Help identify needs for new courses, developers and instructors for these courses, recommend funding support to Exec Comm

  35. Managing the DE • Graduate Advising Committee • Review mechanisms for advising students • Assist Head Graduate Advisor to • Review and approve individual course selections • Review Qual and Dissertation committees to make sure a DE member is included • Review petitions for exceptions • Dept. rules about Qual Committee members • What if adviser CSE member, but not allowed to be on Qual committee? – Use external examiner • Dept. rules about internal, external advisers?

  36. Managing the DE • Computer Committee? • Do we need another committee? • Possible duties: • Survey computing needs (HW & SW) • Allocate space in CITRIS machine room • Identify common SW needs (to buy and to teach)

  37. Example Course – CS267 • “Applications of Parallel Computing” • see www.cs.berkeley.edu/~demmel (later today) • Taught every Spring, this semester to: • UC Berkeley (45 Grad + 5 Undergrad), • UC Davis, UC Merced, UC Santa Cruz • Google “parallel computing course”, doing “I’m feeling lucky”, gets older version • CSE topics expanding to include ideas from ParLab

  38. Math Courses (so far) • Ma220 – Probabilistic Methods • Ma221 – Numerical Linear Algebra • Ma228AB – Numerical Solutions of Differential Equations

  39. Stat Courses (so far) • Stat215AB: Statistical Models • Stat230A: Linear Models • Stat232: Experimental Design • Stat240: Nonparametric and Robust Methods • Stat241AB: Statistical Learning and Decision Theory (cross-listed with CS) • Stat244: Statistical Computing • Stat245AC: Biostatistical Methods • Stat246: Statistical Genetics • Stat248: Time Series Analysis

  40. IEOR Courses (so far) • IEOR261: Experimenting with Simulated Systems • IEOR262AB: Mathematical Programming • IEOR264: Computational Optimization • IEOR269: Integer Programming and Combinatorial Optimization

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