Abstract
The rise of computational science has facilitated rapid progress in many areas of science and technology over the last decade. There is a growing demand in computational scientists and engineers capable of efficient collaboration in interdisciplinary groups. Training such specialists includes courses on numerical analysis and parallel computing. In this paper we present a new Master’s course Parallel Numerical Methods which bridges the gap between theoretical aspects of numerical methods and issues of implementation for modern multicore and manycore systems. The course aims to guide students through the complete process of solving computational problems, from a problem statement to developing parallel software and analyzing results of computational experiments. An important feature is that many of practical classes are based on research done at the HPC Center of the University of Nizhni Novgorod and therefore illustrate issues, which students may encounter in their research and future career.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bastrakov, S., Meyerov, I., Gergel, V., Gonoskov, A., Gorshkov, A., Efimenko, E., et al.: High performance computing in biomedical applications. Procedia Comput. Sci. 18, 10–19 (2013)
Stoer, J., Bulirsch, R.: Introduction to Numerical Analysis, vol. 12. Springer, Heidelberg (2013)
Mathews, J.H., Fink, K.D.: Numerical Methods Using MATLAB, vol. 31. Prentice hall, Upper Saddle River (1999)
Hamming, R.: Numerical Methods for Scientists and Engineers. Courier Corporation (2012)
Andrews, G.R.: Foundations of Parallel and Distributed Programming. Addison-Wesley Longman Publishing Co. Inc., Boston (1999)
Prasad, S.K., Gupta, A., Rosenberg, A.L., Sussman, A., Weems, C.C.: Topics in Parallel and Distributed Computing: Introducing Concurrency in Undergraduate Courses, 1st edn. Morgan Kaufmann, San Francisco (2015)
Jeffers, J., Reinders, J. (Eds.): High Performance Parallelism Pearls: Multicore and Many-core Programming Approaches, 1st edn. (2014)
Jeffers, J., Reinders, J., Sodani, A. (Eds.): Intel® Xeon Phi™ Processor High Performance Programming, Knights Landing Edition (2016)
Hwu, W.-M.W. (Ed.): GPU Computing Gems Jade Edition. Morgan Kaufmann (2011)
Introduction to numerical methods (2010). Accessed Jan 2017. MIT Open Courseware: http://ocw.mit.edu/courses/mathematics/18-335j-introduction-to-numerical-methods-fall-2010
Introduction to numerical analysis (2004). http://ocw.mit.edu/courses/mathematics/18-330-introduction-to-numerical-analysis-spring-2004
CME206 – Introduction to Numerical Methods for Engineering (2016). http://scpd.stanford.edu/search/publicCourseSearchDetails.do?method=load&courseId=11683
Math 128A: Numerical Analysis (2014). Accessed Jan 2017. http://persson.berkeley.edu/128A
Course MAT321 Numerical Methods (2014). Accessed Jan 2017. https://www.math.princeton.edu/undergraduate/course/MAT321
Burden, R., Faires, J.: Numerical Analysis, 9th edn. Brooks-Cole, Boston (2010)
Kincaid, D., Cheney, E.: Numerical Mathematics and Computing, 7th edn. Brooks-Cole, Boston (2012)
Demmel, J.: Matrix Computations/ Numerical Linear Algebra (2016). https://people.eecs.berkeley.edu/~demmel/ma221_Spr16
Saad, Y.: Computational Aspects of Matrix Theory, Sparse Matrix Computations (2015). http://www-users.cs.umn.edu/~saad/teaching.html
Dongarra, J.: Scientific Computing for Engineers: Spring 2012 (2012). http://www.netlib.org/utk/people/JackDongarra/WEB-PAGES/SPRING-2012/cs594-2012.htm
Heath, M.: Parallel numerical algorithms (2013). http://www.mat.unimi.it/users/pavarino/heath_2013
Edelman, A.: Numerical Computing with julia (2016). http://courses.csail.mit.edu/18.337/2016/calendar.html
Gergel, V., Liniov, A., Meyerov, I., Sysoyev, A.: NSF/IEEE-TCPP curriculum implementation at University of Nizhni Novgorod. In: Proceedings of Fourth NSF/TCPP Workshop on Parallel and Distributed Computing Education, pp. 1079–1084 (2014)
Muller, J.M., Brisebarre, N., De Dinechin, F.: Handbook of Floating-Point Arithmetic. Springer (2009)
Pirova, A., Meyerov, I., Kozinov, E., Lebedev, S.: PMORSy: parallel sparse matrix ordering software for fill-in minimization. Optim. Method. Softw. 32, 274–289 (2016)
Meyerov, I., Sysoyev, A., Astafiev, N., Burylov, I.: Performance optimization of Black-Scholes pricing. In: Jeffers, J., Reinders, J. (Eds.) High Performance Parallelism Pearls: Multicore and Many-core Programming Approaches, pp. 319–340 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Meyerov, I., Bastrakov, S., Barkalov, K., Sysoyev, A., Gergel, V. (2017). Parallel Numerical Methods Course for Future Scientists and Engineers. In: Voevodin, V., Sobolev, S. (eds) Supercomputing. RuSCDays 2017. Communications in Computer and Information Science, vol 793. Springer, Cham. https://doi.org/10.1007/978-3-319-71255-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-71255-0_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71254-3
Online ISBN: 978-3-319-71255-0
eBook Packages: Computer ScienceComputer Science (R0)