Professional Science Master (PSM) in High-Performance Computing for
Scientific Applications
Department of Mathematics College of Science and Technology Temple University October 19, 2015
Steering Committee:
Dr. Giacomo Florin, ICMS (Institute for Computational Molecular Science) and Department of Mathematics, CST (College of Science and Technology)
Dr. Michael L. Klein, Dean and Laura H. Carnell Professor of Science, CST Dr. Axel Kohlmeyer, ICMS and Department of Mathematics, CST
Dr. Edward Letzter, Department of Mathematics, CST
Dr. Christopher MacDermaid, ICMS, Department of Chemistry, CST Dr. Gillian Queisser, Department of Mathematics, CST
Dr. Benjamin Seibold, Department of Mathematics, CST Dr. Daniel Szyld, Department of Mathematics, CST Scientific Advisory Committee:
Dr. Shawn Brown, Director of Public Health Applications, Pittsburgh Supercomputing Center, Pittsburgh, PA (Confirmed)
Dr. Juan Gonzalez, President and Chief Scientist, Accelogic LLC, Weston, FL (Confirmed) Dr. Bruce Murch, Group Leader, Corporate Modeling and Simulation, Procter & Gamble Inc., Cincinnati, OH (Pending)
Scott Becker, Vice President, Enterprise Products, Schrödinger Inc., New York, NY. (Pending) Dr. Thomas A. Grandine, Boeing Company, Seattle, WA (Pending)
Dr. Bruce Murch, Group Leader, Corporate Modeling and Simulation, Procter & Gamble Inc., Cincinnati, OH (Pending)
Dr. Jeffrey Saltzman, AstraZeneca, Waltham, MA (Pending)
Tjerk Straatsma, Group Leader, Scientific Computing, Oak Ridge Leadership Computing Facility, Oak Ridge, TN. (Pending)
Dr. Carol S. Woodward, Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, CA (pending)
[Additional names pending.] Associated Faculty:
[Additional names pending. Particularly from CIS.] Executive Summary:
This PSM is targeted towards STEM graduates seeking to use high-performance computation as their primary research instrument in the physical sciences, life sciences, and engineering. The core curriculum will introduce students to the architecture of high-performance computing systems, to the mathematical techniques employed in high-performance computing, to the software tools used in parallel calculations, and to the computational methods used in the sciences and engineering. Cross-disciplinary techniques will be emphasized, and learning through applications and individually designed projects will be prioritized over theory. Our scientific advisory board will connect students in our program to internships in industry and government laboratories. Graduates of our program will be well placed to compete for high-quality positions in industry, government labs, and academia.
A distinguishing feature of our program will be its paired emphasis on the algorithms and technology of high-performance computing, in applications to problems in science and
engineering. This approach is largely driven by the methodology of applied and computational mathematics, making the mathematics department -- with its strong group in applied and computational mathematics -- a logical home for this PSM.
We further propose that this PSM will be a good fit with the College of Science and
Technology’s rapid development in computational science. We expect that our planned courses will be useful to students outside of the PSM who seek to incorporate high-performance
computing into their research. We also expect that potential students will find the college’s strength in computational science very attractive.
We envision our PSM becoming one of a select few premier programs featuring cross-disciplinary training in applications of high-performance computing.
Our program will be a recognized PSM in High-Performance Computing for Scientific Applications. [1]
Rationale:
Demand for scientists with HPC expertise. In its guidelines on graduate education for computational science and engineering, the Society for Industrial and Applied Mathematics states: “Computation is now regarded as an equal and indispensable partner, along with theory and experiment, in the advance of scientific knowledge and engineering practice.” [2]
At the same time, the “demand for advanced scientific computing experts far outstrips the supply of well-trained computer scientists to fill those needs,” according to a report of the Advanced Scientific Computing Advisory Committee of the Department of Energy, and there is a need for “experts cross-trained not only in the relevant computational sciences but also applied
mathematics, statistics or traditional scientific fields such as chemistry and physics.” [3] Also, “very few academic programs provide this kind of cross-disciplinary training.” [Ibid]
Moreover, in July, 2015, President Obama issued the executive order, Creating a National Strategic Computing Initiative, a “whole-of-government effort designed to create a cohesive, multi-agency strategic vision and Federal investment strategy, executed in collaboration with industry and academia, to maximize the benefits of HPC for the United States.” [4]
Our targeted students, coming to us with backgrounds in the sciences and/or engineering, would graduate our program well suited for positions in government laboratories, industry, or academic research institutions where high-performance computing plays a large role.
Distinct from the competition. The multi-disciplinary HPC orientation of our program will distinguish it from much of our potential competition. Our aim is to build a PSM that is premier among master’s degree programs in computational science and engineering.
To offer some specific comparisons, the ostensibly similar programs at nearby institutions are: (1) The Graduate Minor in Computational Science, at Pennsylvania State University, which is not a PSM program and is only available to students enrolled in one of the university’s other graduate programs. (2) The PSM in Industrial Mathematics, at Rutgers University (Camden), which has no direct tie-in to HPC methods or technology. (3) The Masters Program in Applied Mathematics and Computational Science, at the University of Pennsylvania, which is not a PSM and also does not include a focus on HPC methods or technology.
Our proposed PSM will not duplicate other programs at Temple.
Alignment with larger CST initiatives and goals. Our PSM will fit in well with the rapid
development of computational science within the College of Science and Technology. Moreover, the new courses to be required for the PSM will be well suited to graduate and (some)
undergraduate students beyond those specifically enrolled in the PSM. In this way, our program will be less of an “add on” to the college and more of a natural component within it. On the other hand, the strength of computational science within the college should help draw students to this PSM.
The Program:
The program will be based on 30 s.h. (credits) of work, comprised of required courses (totaling 14 s.h.), required individual research projects (totaling 4 s.h.), elective courses, and courses in ethics and professional development (3 s.h.) for PSM students.
The required courses fall into two pairs:
(1) Math 5061 Fundamentals of Computer Programming for Scientists and Engineers (4 s.h.)
(Pending Approval) Mathematical Methods for High-Performance Computing (3 s.h.) (2) Math 5063 Introduction to High-Performance Computing Technology for Scientists (4 s.h.)
(Pending Approval) High-Performance Computer Programming for Scientific Modeling (3 s.h.) These courses are all new, to be introduced in the 2016-2017 academic year. But the courses in (1) will be aimed at a potential audience significantly more broad that just the students enrolled in the PSM. Indeed, the courses in (1) should appeal to a range of advanced undergraduate and graduate students in the sciences and engineering interested in developing their computational portfolios. However, the courses listed in (2) will primarily serve the PSM.
To further help lower costs, we plan (at least to start) for our students to take PSM ethics and professional development courses already in place in CST. (For example, these courses are offered in the CST PSM programs in Bioinformatics and Biotechnology).
Students will also be required to complete an individual Capstone Research Project in either scientific software engineering or high-performance computing technology.
Our Scientific Advisory Committee will help place students in internships. We envision students taking 6 to 10 credits total per semester.
Courses and Academic Path: 1st Year
Fall (September-December) REQUIRED:
• MATH 5061
Fundamentals of Computer Programming for Scientists and Engineers (4 s.h.) This course is a condensed overview of the knowledge and skills necessary to develop software for scientific applications. By focusing on fundamental mathematical constructs and problem solving strategies, the course simultaneously provides the basis to undertake simple programming tasks for an ongoing research activity, or to pursue further training in scientific and high-performance computing. The material also outlines the
commonalities between multiple programming languages to better understand the transferability of methods from applied science, mathematics and engineering. • MATH 5063
Introduction to High-Performance Computing Technology for Scientists (4 s.h.) The main goal of this course is to provide introductory knowledge of high-performance computing technology used in applied science, mathematics and engineering. The acquired skills can be used by computational scientists to communicate their needs and problems to HPC experts, make informed choices in procuring HPC technology for their needs, adapt scientific software to available technology, and participate competently in the deployment and management of facilities for scientific computation.
Optional:
• MATH 5043
Introduction to Numerical Analysis (3 s.h.)
Roots of nonlinear equations, errors, their source and propagation, linear systems, approximation and interpolation of functions, numerical integration.
1st Year - Spring schedule (January-April) Required:
• (New course pending approval)
Mathematical Methods for High-Performance Computing (3 s.h.)
transforms, multipole expansion, multi-grid methods, large-scale constrained
minimization, cluster or network or graph analysis, agent-based models, Monte Carlo methods.
Tools: Numpy and Scipy, BLAS, PETSc, C++ template libraries.
Each algorithm is introduced by an example taken from (e.g.) fluid dynamics, quantum chemistry, molecular biophysics, computational neuroscience.
• (New course pending approval)
High-Performance Computer Programming for Scientific Modeling (3 s.h.) Weeks 1-2: introduction to numerical problems relevant to HPC (example: fluid dynamics), debugging and profiling, Amdahl’s law, optimization.
Week 3: multi-threading parallelization.
Weeks 4-6: domain decomposition and message passing parallelization.
Weeks 7-10: parallel strategies for common numerical tasks: Fourier transforms, matrix multiplications, Monte Carlo, ordinary differential equations.
Weeks 10-12: benchmarking and optimizing parallel calculations. 2nd Year
REQUIRED:
Individual Capstone Research Project.
• Individual research in scientific software engineering (4 s.h.)
Supervised development of a high-performance computational science application that will be benchmarked in a supercomputing resource hosted by Temple or by the
Pittsburgh Supercomputing Center (PSC).
• Individual research in high-performance computing technology (4 s.h.)
Assembly and configuration of a dedicated high-performance resource with a custom-designed scheduling and/or load-balancing system that will be tested using a pre-agreed set of scientific applications.
The Capstone Research Project provides students the opportunity to develop, apply and demonstrate their skills in a professional HPC environment. The research project must be approved in advance by the Steering Committee and requires a supervisor from either the Temple faculty or the Scientific Committee. Students can undertake their research projects in whole or in part during student internships.
Courses in Ethics (2 s.h.) and Professional Development. (Chosen from one of the other CST PSM programs.)
TENTATIVE LIST OF ELECTIVE COURSES (OTHER THAN LISTED ABOVE): From Department of Mathematics:
• MATH 8013 Numerical Linear Algebra I (3 s.h.) MATH 8014 Numerical Linear Algebra I (3 s.h.)
The syllabus of this year-long course sequence includes iterative methods, classical methods, nonnegative matrices. Semi-iterative methods. Multigrid methods. Conjugate gradient methods. Preconditioning. Domain decomposition. Direct Methods. Sparse Matrix techniques. Graph theory. Eigenvalue Problems.
• MATH 8023 Numerical Differential Equations I (3 s.h.) MATH 8024 Numerical Differential Equations II (3 s.h.)
Analysis and numerical solution of ordinary and partial differential equations. Elliptic, parabolic and hyperbolic systems. Constant and variable coefficients. Finite difference methods. Finite element methods. Convergence analysis. Practical applications. This course is usually offered alternating with the Math 8013/8014 course sequence.
• MATH 8107 Mathematical Modeling for Science, Engineering, and Industry (3 s.h.) Prerequisite: Math 8007 and 8008 or permission from the instructor.
In this course, students work in groups on projects that arise in industry, engineering, or in other disciplines of science. In addition to being advised by the course instructors, in all projects an external partner is present. The problems are formulated in
non-mathematical language, and the final results need to be formulated in a language accessible to the external partner. This means in particular that the mathematical and computational methods must be selected or created by the students themselves. Students disseminate their progress and achievements in weekly presentations, a mid-term and a final project report, and a final presentation. Group work with and without the instructor's involvement is a crucial component in this course.
• MATH 8700 Topics in Computer Programming (3 s.h.) MATH 8710. Topics in Computer Programming (3 s.h.) • MATH 9200 Topics in Numerical Analysis (3-6 s.h.)
MATH 9210 Topics in Numerical Analysis (3-6 s.h.)
These courses cover some basic, as well as advanced topics in numerical analysis. The topics can be changed from time to time. The usual topics include: scientific computing, numerical methods for differential equations, computational fluid dynamics, Monte Carlo simulation, Optimization, Sparse matrices, Fast Fourier transform and applications, etc. From other departments within CST:
• CIS 5524 Analysis and Modeling of Social and Information Networks (3 s.h.) (Zoran Obradovic)
• CIS 5525 Neural Computation (3 s.h.) (Zoran Obradovic)
• CIS 5526 Machine learning (3 s.h.) (Slobodan Vucetic)
• CIS 9669 Distributed and Parallel Computer Systems - Formerly Parallel processing (3 s.h.)
(Justin Shi)
• PHYS 5001 Introduction to Quantum Computing (3 s.h.) • PHYS 8102 Statistical Mechanics (3 s.h.)
• CHEM 5302 Statistical thermodynamics (3 s.h.) (Ron Levy’s group)
• CHEM 5301 Quantum Chemistry (3 s.h.) (Vincent Voelz)
• BIOL 5411 Structural Bioinformatics (3 s. h.) (Vincenzo Carnevale)
Available Resources: Students will use existing college computing facilities, office space, and classroom space. Courses will be taught by college faculty already expert in the specific subjects of these courses. Administrative support will be absorbed by the Department of Mathematics until the course outgrows this arrangement. Student research projects will be supervised by research-active faculty (including postdoctoral) and counted toward workload in accordance with similar student supervision for students outside of the PSM. The website for the program will be included in the mathematics department website along with other graduate programs.
Estimated Budget:
In each year, there will be two one-semester courses directed primarily at PSM students, for a total of 7 credits (s.h.) per year. Based on an estimated instructional cost of $4000 per credit hour, we obtain a yearly cost of instruction at $28,000.
We anticipate advertising costs of approximately $2,000 per year, primarily comprised of advertisements in professional journals.
So we can run the program at approximately $30,000 per year.
We also propose a one-time cost of $10,000 for site visits by the Scientific Committee in the first year of the program. (Budget for subsequent site visits to be determined, based on growth of the program.)
Projected Revenue:
The following is a very conservative revenue projection for the first two years, based on
expectation that students take 14 credit hours in their first year and 16 credit hours in the second year. We calculate using the in-state tuition rate of approximately $650 per credit hour.
Model based on three students in first year and six students in second year: 1st year tuition revenue, with 3 students taking 14 credits total: $27,300 2nd year tuition revenue, with 6 students taking an average of 15 credits total: $58,500
A steady state of 7 students per year taking an average of 15 credits total results in revenue of $68,250. (Of course we would actively work to grow the program beyond these numbers. As students graduate and obtain high-quality positions, the program will largely “sell itself.”) The bottom line here is that the program should at least pay for itself within two years, turn a modest profit soon afterward, and subsequently continue to grow.
Administrative Structure: The program will have a Steering Committee from among faculty with closely related interests. The home department will be the Department of Mathematics. A Program Director will be selected from within the Steering Committee. If necessary, an
Associate Director will be named to oversee day-day-operations.
References
[1] http://www.sciencemasters.com (Accessed October 2015)
[2] https://www.siam.org/students/resources/report.php (Accessed October 2015)
[3] http://ascr-discovery.science.doe.gov/2015/01/careers-that-compute (Posted January 2015) [4] https://www.whitehouse.gov/the-press-office/2015/07/29/executive-order-creating-national-strategic-computing-initiative (Posted July 2015)