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Recent Advances in Computing Infrastructure at Penn State and a Proposal for Integrating Computational Science in Undergraduate Education

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Recent Advances in Computing Infrastructure at Penn State and a Proposal for Integrating Computational Science in Undergraduate Education Recent Advances in Computing Infrastructure at Penn State and A Proposal for Integrating Computational Science in Undergraduate Education a presentation for the HPC User Forum 2013 Fall Conference of Korean Society of Computational Science & Engineering Sponsored by National Institute of Supercomputing & Networking Seoul, South Korea October 1, 2013 Vijay K. Agarwala Senior Director, Research Computing and CI Information Technology Services The Pennsylvania State University University Park, PA 16802 USA What is missing in today's undergraduate education? • Lack of comprehensive training in computational science principles in many majors except Computer Science – Object Oriented Programming and Discrete Mathematics – Data Structures and Algorithm Analysis – Mathematical Methods and Numerical Analysis – Parallel Programming and Numerical Algorithms • A typical BS/BA graduate is not well-prepared for a computationally intensive graduate program • Computational proficiency is being acquired on-the-job, either in graduate school or at place of employment What could universities add or change? • An integrated 5-year program, either a BS/MS or dual BS/BA – Keep the general education requirements intact. The tradition of a liberal arts education is highly valued. – The discipline-specific part of undergraduate curricula cannot be shortened either – there is more to be taught than ever before in all majors – Add 8-10 classes to teach core principles of computational science and also extend the computational aspects of a discipline – Intent is not to produce merely computationalists, but graduates who are solidly grounded in science and engineering • e.g. a student earns a BS in Mechanical Engineering and concurrently an MS or a second BS in Computational Science – Incentivize 5th year via scholarships and/or tuition support A few computationally-intensive majors For a dual-degree program, a BS in any of the majors below can be combined with a concurrent MS or a second BS in Computational Science • Aerospace Engineering • Astronomy and Astrophysics • Agricultural Engineering • Biochemistry and Molecular Biology • Bioengineering – Bioinformatics • Biological Engineering – Neuroscience – Agricultural • Chemistry – Natural Resources • Mathematics • Civil Engineering – Computational Mathematics • Chemical Engineering – Systems Analysis – Biomolecular and Bioprocessing • Physics – Energy and Fuels • Statistics • Computer Engineering – Computational • Electrical Engineering – Biostatistics • Engineering Science • Political Science • Mechanical Engineering • Economics – Quantitative Methods • Nuclear Engineering • Sociology and many more academic disciplines/majors A potential example of an Integrated Program: B.S. (Mechanical Engineering) and M.Engrg. (Computational Science) or B.S. (Mechanical Engineering) and B.S. (Computational Science) TOTAL CREDIT = 161 Program Requirements First Semester CR Second Semester CR ENGL 015 Rhetoric and Composition - or- ECON002 Microeconomic Analysis and Policy, - or - ENGL 030 Honors Freshman Composition 3 ECON 004 Macroeconomic Analysis and Policy, - or - FYS First Year Seminar 1 ECON014 Principles of Economics (GS - Social 3 Science) EDSGN 100 Introduction to Engineering Design 3 CHEM 112 Chemical Principles 3 GA, GH or GS course 3 GA, GH, or GS Course 3 CHEM 110 Gen Physics: Mechanics 3 PHYS 211 Gen Physics: Mechanics 4 MATH 140 Calculus with Analytical Geometry I - or - MATH 141 Calculus with Analytical Geometry II - or - MATH 140E Calculus with Engineering Applications I 4 MATH 141E Calculus with Engineering Applications II 4 Total 17 Total 17 Third Semester CR Fourth Semester CR CAS 100 A/B Effective Speech 3 E MCH 212 Dynamics 3 E MCH 211 Statics 3 E MCH 213 Strength of Materials - or - 3 MATH 220 Matrices 2 E MCH 213D Strength of Materials with Design MATH 231 Calculus of Several Variables 2 GHA Health and Physical Activity 1.5 PHYS 212 Gen Physics: Electricity and Magnetism 4 M E 300 Engineering Thermodynamics I 3 Introduction to Programming (C/C++, Fortran) 3 MATH 251 Ordinary and Partial Differential Equations 4 Object Oriented Programming 3 Total 17 Total 17.5 Fifth Semester CR Sixth Semester CR E E 212 Introduction to Electronic Measuring Systems 3 ENGL 202C Technical Writing 3 E MCH 315 Mechanical Response of Engineering Materials 2 M E 340 Mechanical Engineering Design Methodology 3 GHA Health and Physical Activity 1.5 M E 360 Mechanical Design 3 M E 320 Fluid Flow 3 M E 370 Vibration of Mechanical Systems 3 MATSE 259 Properties and Processing of Engineering Materials 3 PHYS 214 Wave Motion and Quantum Physics 2 Data Structures and Algorithm Analysis I 3 Data Structures and Algorithm Analysis II 3 Total 15.5 Total 17 First Summer Internship in Industry Seventh Semester CR Eighth Semester CR M E 450 Modeling of Dynamic Systems 3 Engineering Technical Elective (ETE) 3 M E 345 Instrumentation, Measurements, and Statistics 4 GA, GH, or GS Course 3 M E 410 Heat Transfer 3 GA, GH, or GS Course 3 M E Lab Course 1 I E 312 Design and Manufacturing Processes 3 GA, GH, or GS Course 3 ME Lab Course 1 Mathematical Methods and Numerical Analysis I 3 Mathematical Methods and Numerical Analysis II 3 Total 17 Total 16 Second Summer Internship in Industry Ninth Semester CR Tenth Semester Engineering Technical Elective (ETE) 3 General Technical Elective (GTE) 3 M E Technical Elective (METE) 3 M E 443W Design Project 3 M E 442W Design Project 3 Parallel Programming and Numerical Algorithms II 3 Parallel Programming and Numerical Algorithms I 3 Computational Analysis – Special Topics 3 Computational Analysis – Special Topics 3 Total 15 Total 12 Research Computing and Cyberinfrastructure (RCC) Services At a high level, RCC services can be grouped into three categories: 1. Running state-of-the-art computational and data engines 2. Teaching workshops, seminars, and guest lectures in courses in the areas of large-scale computational techniques and systems 3. Providing one-on-one support and consulting for independent curiosity- driven research 4. Providing support for sponsored research Services provided by RCC • Provide systems services by researching current practices in operating system, file system, data storage, job scheduling as well as computational support related to compilers, parallel computations, libraries, and other software support. Also supports visualization of GIS data and large computed datasets to gain better insight from the results of simulations. • Enable large-scale computations and data management by building and operating several state- of-the art computational engines. • Consolidate and thus significantly increase the research computing resources available to each faculty participant. Faculty members can frequently exceed their share of the machine to meet peak computing needs. • Provide support and expertise for using programming languages, libraries, and specialized data and software for several disciplines. • Investigate emerging visual computing technologies and implement leading-edge solutions in a cost-effective manner to help faculty better integrate data visualization tools and immersive facilities in their research and instruction. • Investigate emerging architectures for data and numerically intensive computations and work with early-stage companies in areas such as interconnects, networking, and graphics processors. • Help build inter- and intra-institutional research communities using cyberinfrastructure technologies. • Maintain close contacts with NSF, DoE, NIH, NASA and other federally funded centres, and help faculty members with porting and scaling of codes across different systems. Programs, Libraries, and Application Codes in Support of Computational Research • Bioinformatics: BLAST/BLAST+, CUDASW++, RepeatMasker, Trinity • Code Development: Boost C++ Libraries, CMake, CUDA, Eclipse, FFTW, GCC, GNU Scientific Library (GSL), Haskell, HDF5, Intel, Java, MAGMA, Intel-MKL, OpenMPI, PETSc, PGI, Python, Subversion, TotalView, Valgrind • Computational Fluid Dynamics: ANSYS Fluent, GAMBIT, GridPro, OpenFOAM, Pointwise, STAR-CCM+ • Computer Vision: OpenCV • Connectivity Solutions: Exceed onDemand • Electromagnetics: Meep • Electron Microscopy: Auto3Dem, EMAN2 • Electronic Structure/DFT: ABINIT, BigDFT, Quantum ESPRESSO, SIESTA, VASP, WanT, WIEN2k • Evolutionary Biology: BEAGLE, BEAST, MrBayes, OpenBUGS • FEM: Abaqus, ANSYS, COMSOL Multiphysics, LS-DYNA, MSC Nastran, OpenSees, Patran • Material Science: Materials Studio, PARATEC • Mathematics: GAUSS, gridMathematica, Maple, Mathematica, MATLAB • Molecular Modeling: Amber12, Desmond, DL_POLY, GROMACS, LAMMPS, NAMD, nMOLDYN, OpenMM, Schrodinger • Optimization: AMPL, CPLEX, GAMS, MATLAB, SeDuMi • Quantum Chemistry: CHARMM, Gaussian, NWChem, Q-Chem, TeraChem/PetaChem • Scientific Collaboration: HUBzero • Statistics: GAUSS, MATLAB, R, SAS, Stata • Visualization: Avizo, DCV, formZ, GView, IDL, Jmol, Maya, ParaView, PyMOL, SCIRun, Tecplot, Visit, VMD, VTK All software installations are driven by faculty. The software stack on every system is customized and entirely shaped by faculty needs. Visualization Services and Facilities Promote effective use of visualization and VR techniques in research and teaching via strategic deployment of facilities and related support. Staff members provide consulting, teach seminars, assist faculty and support facilities for visualization and VR. Recent support areas include: • Visualization and VR system design and deployment • 3D Modeling applications: FormZ, Maya • Data Visualization applications:
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